# Dagster

> Dagster is an open-source data orchestrator for building, running, and observing data and AI pipelines. You define data assets (tables, files, ML models, reports) as Python functions, and Dagster runs them at the right time, tracks lineage between them, and monitors their quality and freshness. Dagster+ is the managed commercial platform built on the open-source project. Dagster is part of Prefect and continues under its own name and open-source license.

Core concepts:

- Assets: the data products a pipeline produces, declared in Python with their upstream dependencies.
- Resources: configurable connections to external systems such as warehouses and APIs.
- Schedules, sensors, and declarative automation: run assets on a cadence, on external events, or when upstream data changes.
- Partitions and backfills: split assets by time or another dimension and recompute any slice.
- Components: YAML-configured building blocks for common integrations such as dbt, Fivetran, and Sling.
- Dagster+: hosted control plane with serverless or hybrid deployment, branch deployments, a data catalog, alerting, and cost insights.

Pages backed by authored content (blog posts, customer stories, integrations, guides, learning articles, glossary terms, and events) are also available as plain Markdown: add `.md` to the page URL or send `Accept: text/markdown`. The links below point to those Markdown copies. [llms-full.txt](https://dagster.io/llms-full.txt) contains the full text of the product and learning content in one file.

# Integrations

## Airflow

> Accelerate the migration of Airflow DAGs to Dagster assets with opinionated tooling.

Source: https://dagster.io/integrations/airflow
Category: Other

### About this integration

Airlift is a migration toolkit that makes it easy to transition from Airflow to Dagster with minimal disruption to your existing workflows. Rather than requiring a complete rewrite, Airlift enables a gradual, task-by-task migration that can be completed in any order with minimal coordination between teams.

With Airlift, you can:

- **Observe** multiple Airflow instances from a centralized Dagster control plane
- **Migrate** individual tasks or entire DAGs incrementally
- **Federate** execution across multiple Airflow deployments
- **Monitor** both Airflow and Dagster assets in a unified interface

The toolkit is designed to work with your existing Airflow infrastructure, including AWS MWAA, Google Cloud Composer, and Astronomer deployments.

### Installation

```bash
pip install 'dagster-airlift[core]' dagster-webserver dagster
```

### Key Features

#### Incremental Migration

Migrate your Airflow DAGs to Dagster assets one task at a time, allowing teams to move at their own pace without breaking existing workflows.

#### Multi-Instance Observability

Connect multiple Airflow instances to a single Dagster deployment for centralized monitoring and orchestration across your entire data platform.

#### Asset-Centric Approach

Transform Airflow tasks into Dagster software-defined assets, providing better lineage tracking and dependency management.

#### Minimal Code Changes

Existing Airflow code requires minimal modifications during the migration process, reducing risk and development overhead.

### Getting Started

#### Step 1: Peer Your Airflow Instance

Connect your Airflow instance to Dagster to begin observing DAG executions:

```python
from dagster_airlift.core import (
    AirflowBasicAuthBackend,
    AirflowInstance,
    build_defs_from_airflow_instance,
)

# Define your Airflow instance
airflow_instance = AirflowInstance(
    auth_backend=AirflowBasicAuthBackend(
        webserver_url="http://localhost:8080",
        username="admin",
        password="admin",
    ),
    name="my_airflow_instance",
)

# Create Dagster definitions
defs = build_defs_from_airflow_instance(
    airflow_instance=airflow_instance
)
```

#### Step 2: Observe Assets

Map your Airflow tasks to Dagster assets to track data lineage:

```python
from dagster import AssetSpec
from dagster_airlift.core import assets_with_task_mappings

# Define asset specs for your data
customers_spec = AssetSpec(key="customers")
orders_spec = AssetSpec(key="orders") 

# Map assets to Airflow tasks
mapped_assets = assets_with_task_mappings(
    dag_id="etl_pipeline",
    task_mappings={
        "extract_customers": [customers_spec],
        "extract_orders": [orders_spec],
    }
)
```

### Step 3: Migrate Tasks

Gradually migrate task execution from Airflow to Dagster:

```python
# In your Airflow DAG file
from dagster_airlift.in_airflow import proxying_to_dagster
from dagster_airlift.in_airflow.proxied_state import load_proxied_state_from_yaml

# Enable proxying for specific tasks
if PROXYING_ENABLED:
    proxying_to_dagster(
        global_vars=globals(),
        proxied_state=load_proxied_state_from_yaml(
            Path(__file__).parent / "proxied_state.yaml"
        ),
    )
```

### Supported Airflow Versions

Airlift supports Airflow 2.0+ and requires access to Airflow's REST API:

- **Airflow 2.0+**: Full support with stable REST API
- **AWS MWAA**: Supported (Airflow 2.4.3 or later required)
- **Google Cloud Composer**: Full support
- **Astronomer**: Full support
- **Self-hosted Airflow**: Full support with proper API access

### About Airlift

Airlift represents a paradigm shift in data platform migration, moving away from "big bang" rewrites toward incremental, low-risk transitions. By leveraging Airflow's REST API and Dagster's software-defined assets, Airlift provides a bridge between traditional workflow orchestration and modern data platform architecture.

The toolkit was designed based on real-world migration experiences, focusing on practical challenges like organizational coordination, system reliability, and business continuity during platform transitions.

---

## Bring Open Metadata Standards/APIs to your Dagster project

> Configure and schedule metadata and profiler workflows from the OpenMetadata UI.

Source: https://dagster.io/integrations/dagster-open-metadata
Category: Metadata & Data Quality

### About this integration

With this integration you can create a Open Metadata service to ingest metadata produced by the Dagster application. View the Ingestion Pipeline running from the Open Metadata Service Page.

### About Open Metadata

Poorly organized metadata is preventing organizations from realizing the full potential of data. Most metadata is incorrect, inconsistent, stale, missing, and fragmented in silos across various disconnected tools obscuring a holistic picture of data.

**Open Metadata** is an all-in-one platform for data discovery, data lineage, data quality, observability, governance, and team collaboration. It is one of the fastest growing open-source projects with a vibrant community and adoption by a diverse set of companies in a variety of industry verticals. Powered by a centralized metadata store based on Open Metadata Standards/APIs, supporting connectors to a wide range of data services, OpenMetadata enables end-to-end metadata management, giving you the freedom to unlock the value of your data assets.

---

## Build Bash/shell commands into your pipeline

> Execute a Bash or shell command directly or from a script file.

Source: https://dagster.io/integrations/dagster-shell
Category: Compute

### About this integration

Dagster comes with a native `PipesSubprocessClient` resource that enables you to launch shell commands directly from Dagster assets and ops. This integration allows you to pass parameters to external shell scripts while Dagster receives real-time events, such as logs, asset checks, and asset materializations, from the initiated external execution. With minimal code changes required on the job side, this integration is both efficient and easy to implement.

### Installation

```bash
pip install dagster
```

### Example

```python
import shutil

import dagster as dg


@dg.asset
def shell_asset(
    context: dg.AssetExecutionContext, pipes_subprocess_client: dg.PipesSubprocessClient
) -> None:
    shell_script_path = "/path/to/your/script.sh"
    return pipes_subprocess_client.run(
        command=["bash", shell_script_path],
        context=context,
    ).get_results()


defs = dg.Definitions(
    assets=[shell_asset],
    resources={"pipes_subprocess_client": dg.PipesSubprocessClient()},
)
```

### About Shell

A shell is a computer program that presents a command line interface which allows you to control your computer using commands entered with a keyboard instead of controlling graphical user interfaces (GUIs) with a mouse/keyboard/touchscreen combination.

---

## Dagster + Anthropic

> Integrate Anthropic’s Claude AI models into Dagster pipelines for advanced conversational AI capabilities.

Source: https://dagster.io/integrations/dagster-anthropic
Category: Other

#### About dagster-anthropic

The `dagster-anthropic` library allows you to easily interact with the Anthropic REST API using the Anthropic Python API to build AI steps into your Dagster pipelines. You can also log Anthropic API usage metadata in Dagster Insights, giving you detailed observability on API call credit consumption.

When paired with Dagster assets, the resource automatically logs Anthropic usage metadata in asset metadata.

### Installation

```bash
pip install dagster dagster-anthropic
```

### Example

```python
from dagster_anthropic import AnthropicResource
import dagster as dg

@dg.asset(compute_kind="anthropic")
def anthropic_asset(context: dg.AssetExecutionContext, anthropic: AnthropicResource):
    with anthropic.get_client(context) as client:
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1024,
            messages=[{"role": "user", "content": "Say this is a test"}],
        )
    return response

defs = dg.Definitions(
    assets=[anthropic_asset],
    resources={
        "anthropic": AnthropicResource(api_key=dg.EnvVar("ANTHROPIC_API_KEY")),
    },
)
```

#### About Anthropic

Anthropic is an AI research company focused on developing safe and ethical AI systems. Their Claude models are designed to be helpful, harmless, and honest, with a strong emphasis on safety and alignment with human values.

---

## Dagster + Clickhouse

> Read and write natively to ClickHouse from software-defined assets.

Source: https://dagster.io/integrations/dagster-clickhouse
Category: Storage

#### About dagster-clickhouse

The `dagster-clickhouse` library provides an integration with ClickHouse using the native protocol (clickhouse-driver). Use a resource for ad hoc SQL, I/O managers for Pandas or Polars DataFrames, and components to wire templated SQL into Dagster projects.

### Installation

```bash
pip install dagster dagster-clickhouse
```

### Example

```python
import pandas as pd
from dagster_clickhouse_pandas import (
    ClickhousePandasIOManager,
)

import dagster as dg


@dg.asset
def iris_dataset() -> (
    pd.DataFrame
):  # asset name is the table name; I/O manager `schema` is the ClickHouse database
    return pd.read_csv(
        "https://docs.dagster.io/assets/iris.csv",
        names=[
            "sepal_length_cm",
            "sepal_width_cm",
            "petal_length_cm",
            "petal_width_cm",
            "species",
        ],
    )


defs = dg.Definitions(
    assets=[iris_dataset],
    resources={
        "io_manager": ClickhousePandasIOManager(
            host="localhost",
            port=9000,
            database="default",
            schema="iris",
        )
    },
)
```

### About Clickhouse

**ClickHouse** is a column-oriented OLAP database designed for analytical queries over large datasets.

---

## Dagster + Datadog

> Publish metrics to Datadog and centralize monitoring metrics.

Source: https://dagster.io/integrations/dagster-datadog
Category: Logging & Monitoring

### About this integration

While Dagster provides comprehensive monitoring and observability of the pipelines it orchestrates, many teams look to centralize all their monitoring across apps, processes and infrastructure using Datadog's 'Cloud Monitoring as a Service'. The `dagster-datadog` integration allows you to publish metrics to Datadog from within Dagster ops.

### Installation

```bash
pip install dagster-datadog
```

### Example

```python
import os

import dagster as dg
from dagster_datadog import DatadogResource


@dg.asset
def report_to_datadog(datadog: DatadogResource):
    datadog_client = datadog.get_client()
    datadog_client.event("Man down!", "This server needs assistance.")
    datadog_client.gauge("users.online", 1001, tags=["protocol:http"])
    datadog_client.increment("page.views")


defs = dg.Definitions(
    assets=[report_to_datadog],
    resources={
        "datadog": DatadogResource(
            api_key=os.environ["DATADOG_API_KEY"],
            app_key=os.environ["DATADOG_APP_KEY"],
        )
    },
)
```

### About Datadog

**Datadog** is an observability service for cloud-scale applications, providing monitoring of servers, databases, tools, and services, through a SaaS-based data analytics platform.

---

## Dagster + DuckDB

> Read and write natively to DuckDB from software-defined assets.

Source: https://dagster.io/integrations/dagster-duckdb
Category: Storage

### About this integration

This library provides an integration with the DuckDB database, and allows for an out-of-the-box [I/O Manager](https://docs.dagster.io/guides/build/io-managers) so that you can make DuckDB your storage of choice.

### Installation

```bash
pip install dagster-duckdb
```

### Example

```python
from dagster_duckdb_pandas import DuckDBPandasIOManager
from dagster import Definitions, asset
import pandas as pd

@asset(
    key_prefix=["my_schema"]  # will be used as the schema in duckdb
)
def my_table() -> pd.DataFrame:  # the name of the asset will be the table name
    return pd.DataFrame()

defs = Definitions(
    assets=[my_table],
    resources={"io_manager": DuckDBPandasIOManager(database="my_db.duckdb")}
)
```

### About DuckDB

**DuckDB** is a column-oriented embeddable OLAP database. A typical OLTP relational database like SQLite is row-oriented. In row-oriented database, data is organised physically as consecutive tuples.

---

## Dagster + GCP BigQuery

> Integrate Dagster with GCP BigQuery.

Source: https://dagster.io/integrations/dagster-gcp-bigquery
Category: Storage

### About this integration

The Google Cloud Platform BigQuery integration allows data engineers to easily query and store data in the BigQuery data warehouse through the use of the `BigQueryResource`.

### Installation

```bash
pip install dagster-gcp
```

### Examples

```python
from dagster import Definitions, asset
from dagster_gcp import BigQueryResource


@asset
def my_table(bigquery: BigQueryResource):
    with bigquery.get_client() as client:
        client.query("SELECT * FROM my_dataset.my_table")


defs = Definitions(
    assets=[my_table], resources={"bigquery": BigQueryResource(project="my-project")}
)
```

### About Google Cloud Platform BigQuery

The Google Cloud Platform BigQuery service, offers a fully-managed enterprise data warehouse that enables fast SQL queries using the processing power of Google's infrastructure.

---

## Dagster + GCP GCS

> Integrate Dagster with Google Cloud Storage.

Source: https://dagster.io/integrations/dagster-gcp-gcs
Category: Storage

### About this integration

This integration allows you to interact with Google Cloud Storage (GCS) using Dagster. It provides resources, IO managers, and utilities to manage and store data in GCS, making it easier to integrate GCS into your data pipelines.

### Installation

```bash
pip install dagster-gcp
```

### Examples

```python
import pandas as pd
from dagster import Definitions, asset
from dagster_gcp.gcs import GCSResource


@asset
def my_gcs_asset(gcs: GCSResource):
    df = pd.DataFrame({"column1": [1, 2, 3], "column2": ["A", "B", "C"]})

    csv_data = df.to_csv(index=False)

    gcs_client = gcs.get_client()

    bucket = gcs_client.bucket("my-cool-bucket")
    blob = bucket.blob("path/to/my_dataframe.csv")
    blob.upload_from_string(csv_data)


defs = Definitions(
    assets=[my_gcs_asset],
    resources={"gcs": GCSResource(project="my-gcp-project")},
)
```

### About Google Cloud Platform GCS

**Google Cloud Storage (GCS)**, is a scalable and secure object storage service. GCS is designed for storing and accessing any amount of data at any time, making it ideal for data science, AI infrastructure, and frameworks for ML like AutoML. With this integration, you can leverage GCS for efficient data storage and retrieval within your Dagster pipelines.

---

## Dagster + Github

> Integrate with GitHub Apps and automate operations in repositories.

Source: https://dagster.io/integrations/dagster-github
Category: Other

### About this integration

This library provides an integration with [*GitHub Apps*](https://docs.github.com/en/developers/apps/getting-started-with-apps/about-apps) by providing a thin wrapper on the github v4 graphql API. This allows for automating operations within your github repositories and with the tighter permissions scopes that github apps allow for vs using a personal token.

### Installation

```bash
pip install dagster-github
```

### Example

```python
import dagster as dg
from dagster_github import GithubResource


@dg.asset
def github_asset(github: GithubResource):
    github.get_client().create_issue(
        repo_name="dagster",
        repo_owner="dagster-io",
        title="Dagster's first github issue",
        body="this open source thing seems like a pretty good idea",
    )


defs = dg.Definitions(
    assets=[github_asset],
    resources={
        "github": GithubResource(
            github_app_id=dg.EnvVar("GITHUB_APP_ID"),
            github_app_private_rsa_key=dg.EnvVar("GITHUB_PRIVATE_KEY"),
            github_installation_id=dg.EnvVar("GITHUB_INSTALLATION_ID"),
        )
    },
)
```

### About Github

**Github** provides a highly available git repo, access control, bug tracking, software feature requests, task management, continuous integration, and wikis for open source and commercial projects.

---

## Dagster + Great Expectations

> Yield an expectation and its output with all relevant metadata.

Source: https://dagster.io/integrations/dagster-great-expectations
Category: Other

### About this integration

With this integration, you can build Great Expectations validations inside Dagster Ops.

### Installation

### Example

### About Great Expectations

**Great Expectations** is a shared, open standard for data quality. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.

---

## Dagster + Hightouch

> Trigger syncs and monitor them until they complete.

Source: https://dagster.io/integrations/dagster-hightouch
Category: ETL / Reverse ETL

### About this integration

With this integration you can trigger Hightouch syncs and monitor them from within Dagster. Fine-tune when Hightouch syncs kick-off, visualize their dependencies, and monitor the steps in your data activation workflow.

This native integration helps your team more effectively orchestrate the last mile of data analytics—bringing that data from the warehouse back into the SaaS tools your business teams live in. With the `dagster-hightouch` integration, Hightough users have more granular and sophisticated control over when data gets activated.

### Installation

```bash
pip install dagster-hightouch
```

### Example

```python
from dagster import job
from dagster_hightouch.ops import hightouch_sync_op
from dagster_hightouch.resources import ht_resource
import os

HT_ORG = "39619"

run_ht_sync_orgs = hightouch_sync_op.configured(
    {"sync_id": HT_ORG}, name="hightouch_sfdc_organizations"
)

@job(
    resource_defs={
        "hightouch": ht_resource.configured(
            {"api_key": os.environ['HIGHTOUCH_API_KEY']},
        ),
    }
)
def ht_sfdc_job():
    ht_orgs = run_ht_sync_orgs()
```

### About Hightouch

**Hightouch** syncs data from any data warehouse into popular SaaS tools that businesses run on. Hightouch uses the power of Reverse ETL to transform core business applications from siloed data islands into powerful integrated solutions.

---

## Dagster + Jupyter

> Eliminate the tedious productionization of Jupyter notebooks.

Source: https://dagster.io/integrations/dagster-jupyter
Category: Compute

### About Jupyter

Fast iteration, the literate combination of arbitrary code with markdown blocks, and inline plotting make notebooks an indispensible tool for data science. The **Dagstermill** package makes it straightforward to run notebooks using the Dagster tools and to integrate them into data jobs with heterogeneous ops: for instance, Spark jobs, SQL statements run against a data warehouse, or arbitrary Python code.

---

## Dagster + Microsoft Teams

> Keep your team up to speed with Teams messages.

Source: https://dagster.io/integrations/dagster-microsoft-teams
Category: Alerting

### About this integration

By configuring this resource, you can post messages to MS Teams from any Dagster op or asset.

### Installation

```bash
pip install dagster-msteams
```

### Example

```python
# Read the docs on Resources to learn more: https://docs.dagster.io/api/dagster/resources
import dagster as dg
from dagster_msteams import Card, MSTeamsResource


@dg.asset
def microsoft_teams_message(msteams: MSTeamsResource):
    card = Card()
    card.add_attachment(text_message="Hello there!")
    msteams.get_client().post_message(payload=card.payload)


defs = dg.Definitions(
    assets=[microsoft_teams_message],
    resources={"msteams": MSTeamsResource(hook_url=dg.EnvVar("TEAMS_WEBHOOK_URL"))},
)
```

### About Microsoft Teams

**Microsoft Teams** is a business communication platform. Teams offers workspace chat and videoconferencing, file storage, and application integration.

---

## Dagster + MLflow

> Streamline productionizing, maintaining, and monitoring machine-learning models.

Source: https://dagster.io/integrations/dagster-mlflow

### About this integration

With `dagster-mlflow`, you can initialize an MLflow run and use it for all steps within a Dagster run. Additionally, you can access all of MLflow’s methods as well as the [MLflow tracking](https://www.mlflow.org/docs/latest/tracking.html) client’s methods.

### Installation

### Example

### About MLflow

[**MLflow**](https://docs.databricks.com/mlflow/index.html) is an open source platform for managing the end-to-end machine learning lifecycle.

---

## Dagster + Modal

> Run serverless Python functions at scale with Modal.

Source: https://dagster.io/integrations/dagster-modal
Category: Compute

### Dagster & Modal

### About this integration

Modal is a serverless cloud platform that makes it easy to run Python code at scale without managing infrastructure. The Dagster-Modal integration enables you to execute compute-intensive workloads, ML training jobs, and data processing tasks on Modal's auto-scaling platform directly from your Dagster pipelines.

With this integration, you can:

- **Run serverless functions** that automatically scale from zero to thousands of containers
- **Access GPU resources** for machine learning training and inference workloads
- **Execute long-running jobs** without worrying about infrastructure management
- **Optimize costs** with pay-per-use pricing and automatic scaling
- **Deploy instantly** with Modal's container orchestration
- **Monitor execution** through Dagster's observability tools

Modal is particularly powerful for ML workflows, data processing at scale, and any compute-intensive tasks that benefit from elastic infrastructure.

### Installation

```bash
pip install dagster-modal modal
```

### Key Features

#### Serverless Execution

Modal automatically provisions and scales compute resources based on demand, eliminating the need to manage servers or containers.

#### GPU Support

Access powerful GPU instances (A10G, A100, H100) for machine learning training and inference without long-term commitments.

#### Instant Deployment

Deploy functions and containers instantly with Modal's fast cold-start times and efficient resource allocation.

#### Cost Optimization

Pay only for actual compute time with automatic scaling to zero when idle, making it cost-effective for variable workloads.

#### Container Orchestration

Run complex applications with full control over the runtime environment, dependencies, and resource requirements.

---

## Dagster + Pandas

> Implement validation on pandas DataFrames.

Source: https://dagster.io/integrations/dagster-pandas
Category: Metadata & Data Quality

### About this integration

Perform data validation, emit summary statistics, and enable reliable dataframe serialization/deserialization. The dagster\_pandas library provides you with the utilities for implementing validation on pandas DataFrames. The Dagster type system generates documentation of your dataframe constraints and makes it accessible in the Dagit UI.

### Installation

```bash
pip install dagster-pandas
```

### About Pandas

**Pandas** is a very popular Python package that provides data structures designed to make working with “relational” or “labeled” data both easy and intuitive.Pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

---

## Dagster + Pandera

> Generate Dagster Types from Pandera dataframe schemas.

Source: https://dagster.io/integrations/dagster-pandera
Category: Metadata & Data Quality

### Installation

```bash
pip install dagster-pandera
```

### Example

```python
import random
import pandas as pd
import pandera as pa
from dagster_pandera import pandera_schema_to_dagster_type
from pandera.typing import Series
from dagster import asset

APPLE_STOCK_PRICES = {
    "name": ["AAPL", "AAPL", "AAPL", "AAPL", "AAPL"],
    "date": ["2018-01-22", "2018-01-23", "2018-01-24", "2018-01-25", "2018-01-26"],
    "open": [177.3, 177.3, 177.25, 174.50, 172.0],
    "close": [177.0, 177.04, 174.22, 171.11, 171.51],
}


class StockPrices(pa.SchemaModel):
    """Open/close prices for one or more stocks by day."""

    name: Series[str] = pa.Field(description="Ticker symbol of stock")
    date: Series[str] = pa.Field(description="Date of prices")
    open: Series[float] = pa.Field(ge=0, description="Price at market open")
    close: Series[float] = pa.Field(ge=0, description="Price at market close")


@asset(dagster_type=pandera_schema_to_dagster_type(StockPrices))
def apple_stock_prices_dirty():
    prices = pd.DataFrame(APPLE_STOCK_PRICES)
    i = random.choice(prices.index)
    prices.loc[i, "open"] = pd.NA
    prices.loc[i, "close"] = pd.NA
    return prices
```

### About Pandera

**Pandera** is a statistical data testing toolkit, and a data validation library for scientists, engineers, and analysts seeking correctness.

### About this integration

The `dagster-pandera` integration library provides an API for generating Dagster Types from [Pandera dataframe schemas](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html).

Like all Dagster types, dagster-pandera-generated types can be used to annotate op inputs and outputs. This provides runtime type-checking with rich error reporting and allows Dagit to display information about a dataframe's structure.

---

## Dagster + Polars

> Use Polars eager or lazy DataFrames as inputs and outputs in Dagster.

Source: https://dagster.io/integrations/dagster-polars
Category: Metadata & Data Quality

### About this integration

The Polars integration brings lightning-fast DataFrame processing to your Dagster pipelines. Polars is a high-performance DataFrame library written in Rust with Python bindings, offering both eager and lazy evaluation modes for optimal performance on large datasets.

With this integration, you can:

- **Use Polars DataFrames** as inputs and outputs in Dagster assets and ops
- **Leverage lazy evaluation** for memory-efficient processing of large datasets
- **Choose between eager and lazy modes** using Python type annotations
- **Store data efficiently** with multiple serialization formats (Parquet, Delta Lake, etc.)
- **Process data faster** than traditional pandas workflows with automatic query optimization

The integration supports multiple storage backends and filesystems, making it ideal for both local development and cloud-scale data processing.

### Installation

```bash
pip install dagster-polars
```

For additional I/O managers and storage formats:

```python
# For Parquet support
pip install dagster-polars[parquet]

# For Delta Lake support  
pip install dagster-polars[deltalake]

# For BigQuery support
pip install dagster-polars[bigquery]

# Install all extras
pip install dagster-polars[all]
```

#### High Performance

Polars is designed for speed, offering:

- **Rust-powered execution** with memory safety and parallelism
- **Automatic query optimization** for lazy operations
- **Columnar processing** optimized for analytical workloads
- **Memory efficiency** with lazy evaluation and streaming

#### Multiple Storage Formats

The integration provides I/O managers for various storage formats:

- **Parquet**: Efficient columnar storage
- **Delta Lake**: ACID transactions and versioning
- **BigQuery**: Direct integration with Google's data warehouse
- **Local files**: Development and testing

### Example

```python
@asset(io_manager_key="polars_io_manager")
def time_series_data() -> pl.LazyFrame:
    """Load and prepare time series data."""
    return pl.scan_csv("sensor_data.csv").with_columns([
        pl.col("timestamp").str.strptime(pl.Datetime, "%Y-%m-%d %H:%M:%S"),
        pl.col("value").cast(pl.Float64)
    ]).sort("timestamp")

@asset(io_manager_key="polars_io_manager")
def rolling_averages(time_series_data: pl.LazyFrame) -> pl.LazyFrame:
    """Calculate rolling averages with different windows."""
    return time_series_data.with_columns([
        pl.col("value").rolling_mean(window_size=5).alias("rolling_5m"),
        pl.col("value").rolling_mean(window_size=15).alias("rolling_15m"),
        pl.col("value").rolling_mean(window_size=60).alias("rolling_1h")
    ])

@asset
def anomaly_detection(rolling_averages: pl.LazyFrame) -> pl.DataFrame:
    """Detect anomalies using statistical methods."""
    return rolling_averages.with_columns([
        (pl.col("value") - pl.col("rolling_1h")).abs().alias("deviation"),
        pl.col("value").std().over(pl.duration(hours=1)).alias("std_1h")
    ]).with_columns([
        (pl.col("deviation") > pl.col("std_1h") * 3).alias("is_anomaly")
    ]).filter(pl.col("is_anomaly")).collect()
```

### About Polars

Polars is a blazingly fast DataFrame library implemented in Rust with Python bindings. It's designed from the ground up for performance, offering both eager and lazy evaluation modes, automatic query optimization, and excellent memory efficiency.

The library excels at analytical workloads and provides a modern API that's both powerful and intuitive. With its integration into Dagster, you can build high-performance data pipelines that scale from prototypes to production without sacrificing speed or reliability.

---

## Dagster + Prometheus

> Integrate with Prometheus through the prometheus_client library.

Source: https://dagster.io/integrations/dagster-prometheus
Category: Logging & Monitoring

### About this integration

This integration allows you to push metrics to the Prometheus gateway from within a Dagster pipeline.

### Installation

```bash
pip install dagster-prometheus
```

### Example

```javascript
#import dagster as dg
from dagster_prometheus import PrometheusResource


@dg.asset
def prometheus_metric(prometheus: PrometheusResource):
    prometheus.push_to_gateway(job="my_job_label")


defs = dg.Definitions(
    assets=[prometheus_metric],
    resources={
        "prometheus": PrometheusResource(gateway="http://pushgateway.example.org:9091")
    },
)
```

### About Prometheus

**Prometheus** is an open-source systems monitoring and alerting toolkit. Originally built at SoundCloud, Prometheus joined the Cloud Native Computing Foundation in 2016 as the second hosted project, after Kubernetes.

Prometheus collects and stores metrics as time series data along with the timestamp at which it was recorded, alongside optional key-value pairs called labels.

---

## Dagster + Ray

> Scale Python workloads with Ray’s distributed computing framework.

Source: https://dagster.io/integrations/dagster-ray
Category: Compute

#### About dagster-ray

The community-supported `dagster-ray` package allows orchestrating distributed Ray compute from Dagster pipelines. This integration includes RayRunLauncher, ray\_executor, RayIOManager, and PipesRayJobClient for distributed computing workflows.

\`dagster-ray\` enables working with distributed Ray compute from Dagster pipelines, combining Dagster's excellent orchestration capabilities and Ray's distributed computing power together.

Learn more in \`dagster-ray\` [docs](https://danielgafni.github.io/dagster-ray/latest)

### Installation

```bash
pip install dagster dagster-ray
# For KubeRay support:
pip install dagster 'dagster-ray[kuberay]'
```

### Example

```python
from dagster import job, op, asset, Definitions
from dagster_ray import ray_executor, RayIOManager

@op(
    tags={
        "dagster-ray/config": {
            "num_cpus": 8,
            "num_gpus": 2,
            "runtime_env": {"pip": {"packages": ["torch"]}},
        }
    }
)
def my_ray_op():
    import torch
    # your expensive computation here
    result = ...
    return result

@asset(io_manager_key="ray_io_manager")
def ray_asset() -> int:
    return 42

@job(executor_def=ray_executor.configured({"ray": {"num_cpus": 1}}))
def my_ray_job():
    return my_ray_op()

defs = Definitions(
    assets=[ray_asset],
    jobs=[my_ray_job],
    resources={
        "ray_io_manager": RayIOManager(),
    }
)
```

#### About Ray

Ray is a unified framework for scaling AI and Python applications. It provides a simple, universal API for building distributed applications, enabling you to parallelize single machine code with minimal code changes. Ray is used for distributed computing, machine learning, and hyperparameter tuning at scale.

---

## Dagster + Salesforce

> Query records, perform bulk operations, and sync Salesforce customer data.

Source: https://dagster.io/integrations/dagster-salesforce
Category: Other

### About this integration

Including Salesforce in your Dagster pipelines enables comprehensive CRM data integration and automation. With [dagster-salesforce](https://docs.dagster.io/integrations/libraries/salesforce), you can:

\- Query and export Salesforce data (Accounts, Leads, Opportunities) for analysis

\- Perform bulk data operations using Salesforce Bulk API 2.0 for efficiency

\- Create, update, and manage Salesforce records programmatically

\- Sync CRM data with your data warehouse or analytics platform

\- Trigger workflows when Salesforce data changes

\- Support multiple authentication methods including username/password and OAuth

This community-maintained integration provides resources for interacting with the Salesforce API, making it easy to incorporate CRM data into your data orchestration workflows and ensure your analytics stay current with your customer relationships.

### Installation

```plaintext
uv add dagster-salesforce
```

Or with pip:

```bash
pip install dagster-salesforce
```

### Example

```python
from pathlib import Path
from dagster_salesforce import SalesforceResource
from dagster_salesforce.credentials import SalesforceUserPasswordCredentials
import dagster as dg

@dg.asset(compute_kind="salesforce")
def salesforce_accounts(
    context: dg.AssetExecutionContext, 
    salesforce: SalesforceResource
):
    """Query and export Account records from Salesforce."""
    account_obj = salesforce.get_object_client("Account")
    
    output_dir = Path("/tmp/salesforce_accounts")
    results = account_obj.query_to_csv(
        query="SELECT Id, Name, Type, Industry FROM Account LIMIT 1000",
        output_directory=output_dir,
        batch_size=10000,
    )
    
    context.log.info(
        f"Downloaded {sum(r.number_of_records for r in results)} accounts"
    )
    
    # Create a new lead
    lead_obj = salesforce.get_object_client("Lead")
    lead_id = lead_obj.create_record({
        "FirstName": "John",
        "LastName": "Doe",
        "Company": "Acme Corp",
        "Email": "john.doe@acmecorp.com",
    })
    
    context.log.info(f"Created new lead with ID: {lead_id}")
    
    return {
        "accounts_exported": sum(r.number_of_records for r in results),
        "new_lead_id": lead_id,
    }

defs = dg.Definitions(
    assets=[salesforce_accounts],
    resources={
        "salesforce": SalesforceResource(
            credentials=SalesforceUserPasswordCredentials(
                username=dg.EnvVar("SALESFORCE_USERNAME"),
                password=dg.EnvVar("SALESFORCE_PASSWORD"),
                security_token=dg.EnvVar("SALESFORCE_SECURITY_TOKEN"),
                domain="login",
            )
        ),
    },
)
```

### About Salesforce

Salesforce is a cloud-based customer relationship management (CRM) platform that helps businesses connect with and understand their customers. It provides a comprehensive suite of tools for sales, customer service, marketing automation, analytics, and application development, making it one of the world's leading CRM platforms.

---

## Dagster + SharePoint

> Automate file operations, folder management, and data extraction through Graph API.

Source: https://dagster.io/integrations/dagster-sharepoint
Category: Storage

### About this integration

Including SharePoint in your Dagster pipelines enables seamless document management and data extraction workflows. With [dagster-sharepoint](https://docs.dagster.io/integrations/libraries/sharepoint), you can:

\- Automatically detect and process new files added to SharePoint document libraries

\- Download and process documents as they're uploaded by your team

\- Manage folder structures and file metadata programmatically

\- Trigger downstream pipelines when critical documents are updated

\- Integrate SharePoint data into your broader data orchestration workflows

This community-maintained integration uses the Microsoft Graph API to provide reliable file operations and folder management within SharePoint, making it easy to incorporate collaborative documents into your data pipelines.

### Installation

```javascript
uv add dagster-sharepoint
```

Or with pip:

```bash
pip install dagster-sharepoint
```

### Example

```python
from datetime import datetime, timedelta
from dagster_sharepoint import FileInfoConfig, SharePointResource
import dagster as dg

@dg.asset(compute_kind="sharepoint")
def process_sharepoint_file(
    context: dg.AssetExecutionContext,
    sharepoint: SharePointResource,
    config: FileInfoConfig,
):
    """Process SharePoint files."""
    context.log.info(f"Processing file from SharePoint {config}")
    
    contents = sharepoint.download_file(config.id)
    context.log.info(f"Downloaded file {config.parent_path}/{config.name}")
    
    if config.name.endswith(".csv"):
        lines = contents.decode("utf-8").splitlines()
        context.log.info(f"CSV file has {len(lines)} lines")
        return {"file_name": config.name, "lines": len(lines)}
    
    return {"file_name": config.name, "size": len(contents)}

@dg.sensor(
    name="sharepoint_new_files",
    minimum_interval_seconds=600,
    target=[process_sharepoint_file],
)
def sharepoint_new_files(
    context: dg.SensorEvaluationContext,
    sharepoint: SharePointResource,
) -> dg.SensorResult:
    """Sensor that checks for new files in SharePoint."""
    last_check = (
        datetime.fromisoformat(context.cursor)
        if context.cursor
        else datetime.now() - timedelta(weeks=999)
    )
    current_check = datetime.now()
    
    newly_created_files = sharepoint.list_newly_created_files(
        since_timestamp=last_check,
        file_name_glob_pattern="*/Reports/*.csv",
        recursive=True,
    )
    
    if not newly_created_files:
        return dg.SkipReason(f"No new files found")
    
    return dg.SensorResult(
        run_requests=[
            dg.RunRequest(
                asset_selection=[process_sharepoint_file.key],
                run_key=file.id,
                run_config=dg.RunConfig(
                    ops={
                        process_sharepoint_file.key.to_python_identifier(): {
                            "config": file.to_config_dict()
                        }
                    }
                ),
            )
            for file in newly_created_files
        ],
        cursor=current_check.isoformat(),
    )
```

### About SharePoint

Microsoft SharePoint is a web-based collaborative platform that integrates with Microsoft Office. It's primarily used as a document management and storage system, enabling teams to store, organize, share, and access information from any device. SharePoint is widely used in enterprises for intranets, document management, collaboration spaces, and business intelligence tools.

---

## Dagster + Slack

> Improve notifications and keep stakeholders in the loop.

Source: https://dagster.io/integrations/dagster-slack
Category: Alerting

### About this integration

This library provides an integration with Slack, to support posting messages in your company’s Slack workspace.

### Installation

```bash
pip install dagster-slack
```

### Example

```python
# Read the docs on Resources to learn more: https://docs.dagster.io/api/dagster/resources

import dagster as dg
from dagster_slack import SlackResource


@dg.asset
def slack_message(slack: SlackResource):
    slack.get_client().chat_postMessage(channel="#noise", text=":wave: hey there!")


defs = dg.Definitions(
    assets=[slack_message],
    resources={"slack": SlackResource(token=dg.EnvVar["SLACK_TOKEN"])},
)
```

### About Slack

The **Slack** messaging app provides chat, video and voice communication tools and is used extensively across companies and communities. The Dagster slack community can be found at [dagster.io/slack](https://dagster.io/slack).

---

## Dagster + SLURM

> Bridge high-performance computing with modern data orchestration.

Source: https://dagster.io/integrations/dagster-slurm
Category: Compute

### About this integration

Including SLURM in your Dagster pipelines brings modern developer experience to high-performance computing. With [dagster-slurm](https://github.com/ascii-supply-networks/dagster-slurm/), you can:

\- Run the same Dagster assets on your laptop, CI pipeline, or Tier-0 supercomputers without code changes

\- Get full observability of Slurm jobs directly in the Dagster UI with real-time logs and metrics

\- Package environments automatically using Pixi for reproducible, portable execution

\- Submit batch jobs through sbatch while maintaining Dagster's lineage tracking and retries

\- Choose between Bash, Ray, or custom launchers for flexible execution

This community-maintained integration uses Dagster Pipes to connect Dagster's orchestration with SLURM's powerful scheduler, delivering the ergonomics of modern data platforms on HPC systems.

### **Installation**

```plaintext
uv add dagster-slurm
```

Or with pip:

```bash
pip install dagster-slurm
```

### **Example**

```python
import dagster as dg
from dagster_slurm import ComputeResource, RayLauncher

@dg.asset
def training_job(context: dg.AssetExecutionContext, compute: ComputeResource):
    completed = compute.run(
        context=context,
        payload_path="workloads/train.py",
        launcher=RayLauncher(num_gpus_per_node=2),
        resource_requirements={
            "framework": "ray",
            "cpus": 32,
            "gpus": 2,
            "memory_gb": 120,
        },
        extra_env={"EXP_NAME": context.run.run_id},
    )
    yield from completed.get_results()
```

Switch from ExecutionMode.LOCAL to ExecutionMode.SLURM and the same asset submits via sbatch, runs on the cluster, and streams logs back to your Dagster UI.

### **About SLURM**

SLURM (Simple Linux Utility for Resource Management) is an open-source workload manager and job scheduler designed for Linux clusters. It's used by many of the world's supercomputing centers and research facilities to manage compute resources, handle job queues, and coordinate parallel processing across thousands of nodes.

---

## Dagster + Snowflake

> Read and write natively to Snowflake from software-defined assets.

Source: https://dagster.io/integrations/dagster-snowflake
Category: Storage

### About this integration

This library provides an integration with the Snowflake AI Data Cloud. Connect to Snowflake as a resource, then use the integration-provided functions to construct an op to establish connections and execute Snowflake queries. Read and write natively to Snowflake from Dagster's software-defined assets (SDAs).

### Installation

```bash
pip install dagster-snowflake
```

### Example

```python
# Read the docs on Resources to learn more: https://docs.dagster.io/api/dagster/resources
# This integration also offers an IO manager. Learn more: https://docs.dagster.io/guides/build/io-managers
from dagster import Definitions, EnvVar, asset
from dagster_snowflake import SnowflakeResource
import os

@asset
def my_table(snowflake: SnowflakeResource):
  with snowflake.get_connection() as conn:
    return conn.cursor().execute_query("SELECT * FROM foo")

defs = Definitions(
    assets=[my_table],
    resources={
      "snowflake": SnowflakeResource(
            account="snowflake account",
            user="snowflake user",
            password=EnvVar("SNOWFLAKE_PASSWORD"),
            database="snowflake database",
            schema="snowflake schema",
            warehouse="snowflake warehouse",
        )
    }
)
```

### About Snowflake

A cloud-based data storage and analytics service, generally termed "data-as-a-service". **Snowflake**'s data warehouse is one of the most widely adopted cloud warehouses for analytics.

---

## Dagster + Weights & Biases

> Orchestrate MLOps pipelines and maintain machine-learning assets.

Source: https://dagster.io/integrations/dagster-wandb
Category: Other

### About this integration

Use Dagster and Weights & Biases (W\&B) to orchestrate your MLOps pipelines and maintain ML assets. The integration with W\&B makes it easy within Dagster to:

- use and create W\&B Artifacts
- use and create Registered Models in W\&B Model Registry
- run training jobs on dedicated compute using W\&B Launch
- use the wandb client in ops and assets

The W\&B Dagster integration provides a W\&B-specific Dagster resource and IO Manager:

- `wandb_resource`: a Dagster resource used to authenticate and communicate to the W\&B API.
- `wandb_artifacts_io_manager`: a Dagster I/O Manager used to consume W\&B Artifacts.

### Installation

To use this integration you will need a Weights and Biases account. Then you will need an W\&B API Key, a W\&B entity (user or team), and a W\&B project. Full installation details can be found on [the Weights and Biases website here](https://docs.wandb.ai/guides/integrations/other/dagster).

Note that Weights & Biases do offer a free cloud account for personal (non-corporate) use. Check out their [pricing page](https://wandb.ai/site/pricing) for details.

### Example

A complete tutorial can be found on [the Weights and Biases website here](https://docs.wandb.ai/guides/integrations/other/dagster).

### About Weights & Biases

[Weights & Biases](https://wandb.ai/site) makes it easy to track your experiments, manage & version your data, and collaborate with your team so you can focus on building the best machine learning models.

---

## Dagster SSH/SFTP library

> Establish encrypted connections to networked resources.

Source: https://dagster.io/integrations/dagster-ssh-sftp
Category: Other

### About this integration

This integration provides a resource for SSH remote execution using [Paramiko](https://github.com/paramiko/paramiko). It allows you to establish secure connections to networked resources and execute commands remotely. The integration also provides an SFTP client for secure file transfers between the local and remote systems.

### Installation

```bash
pip install dagster-ssh
```

### Example

```python
import dagster as dg
from dagster_ssh import SSHResource


@dg.asset
def ssh_asset(ssh: SSHResource):
    ssh.sftp_get("/path/to/remote.csv", "path/to/local.csv")


defs = dg.Definitions(
    assets=[ssh_asset],
    resources={"ssh": SSHResource(remote_host="foo.com", key_file="path/to/id_rsa")},
)
```

### About SSH SFTP

The **SSH protocol** allows for secure remote login with strong authentication to networked resources. It protects network connections with strong encryption. The Dagster library provides direct SSH and SFTP calls from within the execution of your pipelines.

---

## dbt Cloud™

> Run dbt Cloud jobs as part of your data pipeline.

Source: https://dagster.io/integrations/dagster-dbt-cloud
Category: ETL / Reverse ETL

### About this integration

Dagster allows you to run dbt Cloud™ jobs alongside other technologies. You can schedule them to run as a step in a larger pipeline and manage them as a data asset.

### Installation

```bash
pip install dagster-dbt
```

### Example

```python
from dagster_dbt import dbt_cloud_resource, load_assets_from_dbt_cloud_job
import os

# configure a resource to connect to your dbt Cloud instance
dbt_cloud = dbt_cloud_resource.configured(
    {"auth_token": os.environ["DBT_CLOUD_AUTH_TOKEN"], "account_id": 11111}
)

# import assets from dbt
dbt_cloud_assets = load_assets_from_dbt_cloud_job(
    dbt_cloud=dbt_cloud,
    job_id=33333,
)
```

### About dbt Cloud

**dbt Cloud** is a hosted service for running dbt jobs. It helps data analysts and engineers productionize dbt deployments. Beyond dbt open-source, dbt Cloud provides scheduling , CI/CD, serving documentation, and monitoring & alerting.

If you're currently using dbt Cloud™, you can also use Dagster to run `dbt-core` in its place. You can read more about [how to do that here](https://dagster.io/vs/dagster-vs-dbt-cloud).

---

## Get the right tools for running Dagster with  Azure Data Lake Storage Gen 2 (ADLS2)

> Get utilities for ADLS2 and Blob Storage.

Source: https://dagster.io/integrations/dagster-azure-adls2
Category: Storage

### About this integration

Dagster helps you use Azure Storage Accounts as part of your data pipeline. Azure Data Lake Storage Gen 2 (ADLS2) is our primary focus but we also provide utilities for Azure Blob Storage.

### Installation

```bash
pip install dagster-azure
```

### Examples

```javascript
import pandas as pd
from dagster import Definitions, asset, job
from dagster_azure.adls2 import ADLS2Resource, ADLS2SASToken


@asset
def example_adls2_asset(adls2: ADLS2Resource):
    df = pd.DataFrame({"column1": [1, 2, 3], "column2": ["A", "B", "C"]})

    csv_data = df.to_csv(index=False)

    file_client = adls2.adls2_client.get_file_client(
        "my-file-system", "path/to/my_dataframe.csv"
    )
    file_client.upload_data(csv_data, overwrite=True)


defs = Definitions(
    assets=[example_adls2_asset],
    resources={
        "adls2": ADLS2Resource(
            storage_account="my_storage_account",
            credential=ADLS2SASToken(token="my_sas_token"),
        )
    },
)
```

In this updated code, we use `ADLS2Resource` directly instead of `adls2_resource`. The configuration is passed to `ADLS2Resource` during its instantiation.

### About Azure Data Lake Storage Gen 2 (ADLS2)

**Azure Data Lake Storage Gen 2 (ADLS2)** is a set of capabilities dedicated to big data analytics, built on Azure Blob Storage. ADLS2 combines the scalability, cost-effectiveness, security, and rich capabilities of Azure Blob Storage with a high-performance file system that is built for analytics and is compatible with the Hadoop Distributed File System (HDFS). This makes it an ideal choice for data lakes and big data analytics.

---

## OpenAI

> Integrate OpenAI calls into Dagster pipelines without breaking the bank.

Source: https://dagster.io/integrations/dagster-openai
Category: Other

### About dagster-openai

The `dagster-openai` library allows you to easily interact with the OpenAI REST API via the OpenAI Python API to build AI steps into your Dagster pipelines. You can also log OpenAI API usage metadata in Dagster Insights, giving you detailed observability on API call credit consumption.

When paired with Dagster's Software-defined Assets, the resource automatically logs OpenAI usage metadata in asset metadata.

### Installation

```bash
pip install dagster dagster-openai
```

### Example

```python
from dagster_openai import OpenAIResource

from dagster import (
    AssetExecutionContext,
    Definitions,
    EnvVar,
    asset,
    define_asset_job,
)


@asset(compute_kind="OpenAI")
def openai_asset(context: AssetExecutionContext, openai: OpenAIResource):
    with openai.get_client(context) as client:
        client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": "Say this is a test."}],
        )


openai_asset_job = define_asset_job(name="openai_asset_job", selection="openai_asset")

defs = Definitions(
    assets=[openai_asset],
    jobs=[openai_asset_job],
    resources={
        "openai": OpenAIResource(api_key=EnvVar("OPENAI_API_KEY")),
    },
)
```

### About OpenAI

OpenAI is a U.S. based artificial intelligence (AI) research organization with the goal of developing "safe and beneficial" artificial general intelligence, which it defines as "highly autonomous systems that outperform humans at most economically valuable work".

---

## Orchestrate your dbt™ transformation steps

> Put your dbt transformations to work directly from Dagster.

Source: https://dagster.io/integrations/dagster-dbt
Category: ETL / Reverse ETL

### About this integration

Dagster orchestrates dbt alongside other technologies, so you can schedule dbt with Spark, Python, etc. in a single data pipeline.

Dagster's [Software-defined Asset](https://docs.dagster.io/guides/build/assets/defining-assets) approach allows Dagster to understand dbt at the level of individual dbt models. This means that you can:

- Use Dagster's UI or APIs to run subsets of your dbt models, seeds, and snapshots.
- Track failures, logs, and run history for individual dbt models, seeds, and snapshots.
- Define dependencies between individual dbt models and other data assets. For example, put dbt models after the Fivetran-ingested table that they read from, or put a machine learning after the dbt models that it's trained from.

### Installation

```bash
pip install dagster-dbt
```

### Example

```python
from pathlib import Path

from dagster import AssetExecutionContext, Definitions
from dagster_dbt import (
    DbtCliResource,
    DbtProject,
    build_schedule_from_dbt_selection,
    dbt_assets,
)

RELATIVE_PATH_TO_MY_DBT_PROJECT = "./my_dbt_project"

my_project = DbtProject(
    project_dir=Path(__file__)
    .joinpath("..", RELATIVE_PATH_TO_MY_DBT_PROJECT)
    .resolve(),
)
my_project.prepare_if_dev()


@dbt_assets(manifest=my_project.manifest_path)
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
    yield from dbt.cli(["build"], context=context).stream()


my_schedule = build_schedule_from_dbt_selection(
    [my_dbt_assets],
    job_name="materialize_dbt_models",
    cron_schedule="0 0 * * *",
    dbt_select="fqn:*",
)

defs = Definitions(
    assets=[my_dbt_assets],
    schedules=[my_schedule],
    resources={
        "dbt": DbtCliResource(project_dir=my_project),
    },
)
```

### About dbt

**dbt** is a SQL-first transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation.

Are you looking to learn more on running Dagster with dbt? Explore the [Dagster University dbt course](https://courses.dagster.io/courses/dagster-dbt).

---

## Run Meltano jobs from Dagster

> Tap into open-source configurable ETL and the Singer integration library.

Source: https://dagster.io/integrations/dagster-meltano
Category: ETL / Reverse ETL

### About this integration

The `dagster-meltano` library allows you to run Meltano using Dagster. Design and configure ingestion jobs using the popular [Singer.io](https://singer.io) specification.

Note that this integration can also be [managed from the Meltano platform](https://hub.meltano.com/utilities/dagster/) using `meltano add utility dagster` and configured using `meltano config dagster set --interactive`.

### Installation

```bash
pip install dagster-meltano
```

### Example

An example of running an abitrary Meltano run command:

```python
from dagster import repository, job
from dagster_meltano import meltano_resource, meltano_run_op

@job(resource_defs={"meltano": meltano_resource})
def meltano_run_job():
    tap_done = meltano_run_op("tap-1 target-1")()
    meltano_run_op("tap-2 target-2")(tap_done)

@repository()
def repository():
    return [meltano_run_job]
```

### About Meltano

[Meltano](https://meltano.com/) provides data engineers with a set ot tools for easily creating and managing pipelines as code by providing a wide array of composable connectors. Meltano's 'CLI for ELT+' lets you test your changes before they go live.

### About Quantile

Quantile is an expert data consultancy based in the Netherlands. You can find out more at [quantile.nl](https://quantile.nl/).

---

## Tap into Fivetran sources from within your pipelines

> Orchestrate Fivetran connectors and schedule syncs with upstream or downstream dependencies.

Source: https://dagster.io/integrations/dagster-fivetran
Category: ETL / Reverse ETL

### About this integration

With the Dagster-Fivetran integration you can orchestrate ingestion steps as part of a larger pipeline. Programatically interface with the Fivetran REST API to launch syncs and monitor their progress. Tap into software-defined assets and build a set of assets straight off a Fivetran ingestion.

### Installation

```bash
pip install dagster-fivetran
```

### Example

```python
from dagster import EnvVar
from dagster_fivetran import FivetranResource, load_assets_from_fivetran_instance
import os

fivetran_instance = FivetranResource(
    api_key="some_key",
    api_secret=EnvVar("FIVETRAN_SECRET"),
)
fivetran_assets = load_assets_from_fivetran_instance(fivetran_instance)
```

### About Fivetran

**Fivetran** ingests data from SaaS applications, databases, and servers. The data is stored and typically used for analytics.

---

## Trigger Spark jobs

> Configure and run Spark jobs.

Source: https://dagster.io/integrations/dagster-spark
Category: Compute

### About this integration

Spark jobs typically execute on infrastructure that's specialized for Spark. Spark applications are typically not containerized or executed on Kubernetes. Running Spark code often requires submitting code to a Databricks or EMR cluster.

There are two approaches to writing Dagster ops that invoke Spark computations:

- [Running Pyspark code in ops](https://docs.dagster.io/integrations/libraries/spark)
- [Submitting PySpark ops on EMR](https://docs.dagster.io/integrations/libraries/spark)

`dagster-pyspark` provides a Spark class with methods for configuration and constructing the `spark-submit` command for a Spark job.

### About Apache Spark

**Apache Spark** is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. It also provides libraries for graph computation, SQL for structured data processing, ML and data science.

---

## Using Airbyte with Dagster

> Orchestrate Airbyte connections and schedule syncs alongside upstream or downstream dependencies.

Source: https://dagster.io/integrations/dagster-airbyte
Category: ETL / Reverse ETL

### About this integration

Using this integration, you can trigger Airbyte syncs and orchestrate your Airbyte connections from within Dagster, making it easy to chain an Airbyte sync with upstream or downstream steps in your workflow.

[The guide](https://docs.dagster.io/integrations/libraries/airbyte) focuses on how to work with Airbyte connections using Dagster's software-defined asset (SDA) framework.

### Installation

```bash
pip install dagster-airbyte
```

### Example

```python
from dagster import EnvVar
from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance
import os

# Connect to your OSS Airbyte instance
airbyte_instance = AirbyteResource(
    host="localhost",
    port="8000",
    # If using basic auth, include username and password:
    username="airbyte",
    password=EnvVar("AIRBYTE_PASSWORD")
)

# Load all assets from your Airbyte instance
airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance)
```

### About Airbyte

**Airbyte** is an open-source data integration engine that helps you consolidate your SaaS application and database data into your data warehouses, lakes and databases.

---

## Using AWS Athena with Dagster

> Connect to AWS Athena and analyze data in Amazon S3 using standard SQL.

Source: https://dagster.io/integrations/dagster-aws-athena
Category: Storage

### About this integration

This integration allows you to connect to AWS Athena, a serverless interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Using this integration, you can issue queries to Athena, fetch results, and handle query execution states within your Dagster pipelines.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
from dagster import Definitions, asset
from dagster_aws.athena import AthenaClientResource


@asset
def example_athena_asset(athena: AthenaClientResource):
    return athena.get_client().execute_query("SELECT 1", fetch_results=True)


defs = Definitions(
    assets=[example_athena_asset], resources={"athena": AthenaClientResource()}
)
```

### About AWS Athena

AWS Athena is a serverless, interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. Athena is easy to use; simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. Most results are delivered within seconds. With Athena, there are no infrastructure setups, and you pay only for the queries you run. It scales automatically—executing queries in parallel—so results are fast, even with large datasets and complex queries.

---

## Using AWS CloudWatch with Dagster

> Send Dagster logs to AWS CloudWatch for centralized logging and monitoring.

Source: https://dagster.io/integrations/dagster-aws-cloudwatch
Category: Logging & Monitoring

### About this integration

This integration allows you to send Dagster logs to AWS CloudWatch, enabling centralized logging and monitoring of your Dagster jobs. By using AWS CloudWatch, you can take advantage of its powerful log management features, such as real-time log monitoring, log retention policies, and alerting capabilities.

Using this integration, you can configure your Dagster jobs to log directly to AWS CloudWatch, making it easier to track and debug your workflows. This is particularly useful for production environments where centralized logging is essential for maintaining observability and operational efficiency.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
import dagster as dg
from dagster_aws.cloudwatch import cloudwatch_logger


@dg.asset
def my_asset(context: dg.AssetExecutionContext):
    context.log.info("Hello, CloudWatch!")
    context.log.error("This is an error")
    context.log.debug("This is a debug message")


defs = dg.Definitions(
    assets=[my_asset],
    loggers={
        "cloudwatch_logger": cloudwatch_logger,
    },
)
```

### About AWS CloudWatch

AWS CloudWatch is a monitoring and observability service provided by Amazon Web Services (AWS). It allows you to collect, access, and analyze performance and operational data from a variety of AWS resources, applications, and services. With AWS CloudWatch, you can set up alarms, visualize logs and metrics, and gain insights into your infrastructure and applications to ensure they are running smoothly.

AWS CloudWatch provides features such as:

- Real-time monitoring: Track the performance of your applications and infrastructure in real-time.
- Log management: Collect, store, and analyze log data from various sources.
- Alarms and notifications: Set up alarms to automatically notify you of potential issues.
- Dashboards: Create custom dashboards to visualize metrics and logs.
- Integration with other AWS services: Seamlessly integrate with other AWS services for a comprehensive monitoring solution.

---

## Using AWS ECR with Dagster

> Manage container images with AWS Elastic Container Registry from Dagster pipelines.

Source: https://dagster.io/integrations/dagster-aws-ecr
Category: Other

### About this integration

This integration allows you to connect to AWS Elastic Container Registry (ECR). It provides resources to interact with AWS ECR, enabling you to manage your container images.

Using this integration, you can seamlessly integrate AWS ECR into your Dagster pipelines, making it easier to manage and deploy containerized applications.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
from dagster import asset, Definitions
from dagster_aws.ecr import ECRPublicResource


@asset
def get_ecr_login_password(ecr_public: ECRPublicResource):
    return ecr_public.get_client().get_login_password()


defs = Definitions(
    assets=[get_ecr_login_password],
    resources={
        "ecr_public": ECRPublicResource(
            region_name="us-west-1",
            aws_access_key_id="your_access_key_id",
            aws_secret_access_key="your_secret_access_key",
            aws_session_token="your_session_token",
        )
    },
)
```

### About AWS ECR

AWS Elastic Container Registry (ECR) is a fully managed Docker container registry that makes it easy for developers to store, manage, and deploy Docker container images. AWS ECR is integrated with Amazon Elastic Kubernetes Service (EKS), simplifying your development to production workflow. With ECR, you can securely store and manage your container images and easily integrate with your existing CI/CD pipelines. AWS ECR provides high availability and scalability, ensuring that your container images are always available when you need them.

---

## Using AWS EMR with Dagster

> Run petabyte-scale processing with AWS EMR, Spark, Hive, Presto, and more.

Source: https://dagster.io/integrations/dagster-aws-emr
Category: Compute

### About this integration

The `dagster-aws` integration provides a robust framework for orchestrating data pipelines that leverage AWS services, including AWS EMR (Elastic MapReduce). This integration allows you to run and scale big data workloads using open-source tools such as Apache Spark, Hive, Presto, and more.

Using this integration, you can:

- Seamlessly integrate AWS EMR into your Dagster pipelines.
- Utilize EMR for petabyte-scale data processing.
- Easily manage and monitor EMR clusters and jobs from within Dagster.
- Leverage Dagster's orchestration capabilities to handle complex data workflows involving EMR.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
from pathlib import Path
from typing import Any

from dagster import Definitions, ResourceParam, asset
from dagster_aws.emr import emr_pyspark_step_launcher
from dagster_aws.s3 import S3Resource
from dagster_pyspark import PySparkResource
from pyspark.sql import DataFrame, Row
from pyspark.sql.types import IntegerType, StringType, StructField, StructType


emr_pyspark = PySparkResource(spark_config={"spark.executor.memory": "2g"})


@asset
def people(
    pyspark: PySparkResource, pyspark_step_launcher: ResourceParam[Any]
) -> DataFrame:
    schema = StructType(
        [StructField("name", StringType()), StructField("age", IntegerType())]
    )
    rows = [
        Row(name="Thom", age=51),
        Row(name="Jonny", age=48),
        Row(name="Nigel", age=49),
    ]
    return pyspark.spark_session.createDataFrame(rows, schema)


@asset
def people_over_50(
    pyspark_step_launcher: ResourceParam[Any], people: DataFrame
) -> DataFrame:
    return people.filter(people["age"] > 50)


defs = Definitions(
    assets=[people, people_over_50],
    resources={
        "pyspark_step_launcher": emr_pyspark_step_launcher.configured(
            {
                "cluster_id": {"env": "EMR_CLUSTER_ID"},
                "local_pipeline_package_path": str(Path(__file__).parent),
                "deploy_local_pipeline_package": True,
                "region_name": "us-west-1",
                "staging_bucket": "my_staging_bucket",
                "wait_for_logs": True,
            }
        ),
        "pyspark": emr_pyspark,
        "s3": S3Resource(),
    },
)
```

### About AWS EMR

**AWS EMR** (Elastic MapReduce) is a cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. It simplifies running big data frameworks, allowing you to process and analyze large datasets quickly and cost-effectively. AWS EMR provides the scalability, flexibility, and reliability needed to handle complex data processing tasks, making it an ideal choice for data engineers and scientists.

---

## Using AWS Glue with Dagster

> Initiate AWS Glue jobs, pass parameters, and stream logs and structured messages back to Dagster.

Source: https://dagster.io/integrations/dagster-aws-glue
Category: Compute

### About this Integration

The `dagster-aws` integration library provides the `PipesGlueClient` resource, enabling you to launch AWS Glue jobs directly from Dagster assets and ops. This integration allows you to pass parameters to Glue code while Dagster receives real-time events, such as logs, asset checks, and asset materializations, from the initiated jobs. With minimal code changes required on the job side, this integration is both efficient and easy to implement.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
import boto3
from dagster import AssetExecutionContext, Definitions, asset
from dagster_aws.pipes import (
    PipesGlueClient,
    PipesS3ContextInjector,
    PipesS3MessageReader,
)


@asset
def glue_pipes_asset(
    context: AssetExecutionContext, pipes_glue_client: PipesGlueClient
):
    return pipes_glue_client.run(
        context=context,
        job_name="Example Job",
        arguments={"some_parameter_value": "1"},
    ).get_materialize_result()


defs = Definitions(
    assets=[glue_pipes_asset],
    resources={
        "pipes_glue_client": PipesGlueClient(
            client=boto3.client("glue"),
            context_injector=PipesS3ContextInjector(
                client=boto3.client("s3"),
                bucket="my-bucket",
            ),
            message_reader=PipesS3MessageReader(
                client=boto3.client("s3"), bucket="my-bucket"
            ),
        )
    },
)
```

### About AWS Glue

**AWS Glue** is a fully managed cloud service designed to simplify and automate the process of discovering, preparing, and integrating data for analytics, machine learning, and application development. It supports a wide range of data sources and formats, offering seamless integration with other AWS services. AWS Glue provides the tools to create, run, and manage ETL (Extract, Transform, Load) jobs, making it easier to handle complex data workflows. Its serverless architecture allows for scalability and flexibility, making it a preferred choice for data engineers and analysts who need to process and prepare data efficiently.

---

## Using AWS Lambda with Dagster

> Use serverless functions to execute external code in your pipelines.

Source: https://dagster.io/integrations/dagster-aws-lambda
Category: Compute

### About this integration

Using this integration, you can leverage AWS Lambda to execute external code as part of your Dagster pipelines. This is particularly useful for running serverless functions that can scale automatically and handle various workloads without the need for managing infrastructure. The `PipesLambdaClient` class allows you to invoke AWS Lambda functions and stream logs and structured metadata back to Dagster's UI and tools.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
import boto3

from dagster import asset, AssetExecutionContext, Definitions
from dagster_aws.pipes import PipesLambdaClient


lambda_client = boto3.client("lambda", region_name="us-west-1")

lambda_pipes_client = PipesLambdaClient(client=lambda_client)


@asset
def lambda_pipes_asset(
    context: AssetExecutionContext, lambda_pipes_client: PipesLambdaClient
):
    return lambda_pipes_client.run(
        context=context,
        function_name="your_lambda_function_name",
        event={"key": "value"},
    ).get_materialize_result()


defs = Definitions(
    assets=[lambda_pipes_asset],
    resources={"lambda_pipes_client": lambda_pipes_client},
)
```

### About AWS Lambda

**AWS Lambda** is a serverless compute service provided by Amazon Web Services (AWS). It allows you to run code without provisioning or managing servers. AWS Lambda automatically scales your application by running code in response to each trigger, such as changes to data in an Amazon S3 bucket or an update to a DynamoDB table. You can use AWS Lambda to extend other AWS services with custom logic, or create your own backend services that operate at AWS scale, performance, and security.

---

## Using AWS Redshift with Dagster

> Use Redshift’s data warehousing capabilities in Dagster workflows.

Source: https://dagster.io/integrations/dagster-aws-redshift
Category: Storage

### About this integration

Using this integration, you can connect to an AWS Redshift cluster and issue queries against it directly from your Dagster assets. This allows you to seamlessly integrate Redshift into your data pipelines, leveraging the power of Redshift's data warehousing capabilities within your Dagster workflows.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
from dagster import Definitions, asset, EnvVar
from dagster_aws.redshift import RedshiftClientResource


@asset
def example_redshift_asset(context, redshift: RedshiftClientResource):
    result = redshift.get_client().execute_query("SELECT 1", fetch_results=True)
    context.log.info(f"Query result: {result}")


redshift_configured = RedshiftClientResource(
    host="my-redshift-cluster.us-east-1.redshift.amazonaws.com",
    port=5439,
    user="dagster",
    password=EnvVar("DAGSTER_REDSHIFT_PASSWORD"),
    database="dev",
)

defs = Definitions(
    assets=[example_redshift_asset],
    resources={"redshift": redshift_configured},
)
```

### About AWS Redshift

**AWS Redshift** is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This enables you to use your data to acquire new insights for your business and customers. Redshift offers fast query performance using SQL-based tools and business intelligence applications, making it a powerful tool for data warehousing and analytics.

---

## Using AWS S3 with Dagster

> Read and write objects to durable AWS S3 storage from Dagster pipelines.

Source: https://dagster.io/integrations/dagster-aws-s3
Category: Storage

### About this integration

The AWS S3 integration allows data engineers to easily read, and write objects to the durable AWS S3 storage -- enabling engineers to a resilient storage layer when constructing their pipelines.

#### Installation

```bash
pip install dagster-aws
```

### Examples

Here is an example of how to use the `S3Resource` in a Dagster job to interact with AWS S3:

```python
import pandas as pd
from dagster import Definitions, asset
from dagster_aws.s3 import S3Resource


@asset
def my_s3_asset(s3: S3Resource):
    df = pd.DataFrame({"column1": [1, 2, 3], "column2": ["A", "B", "C"]})

    csv_data = df.to_csv(index=False)

    s3_client = s3.get_client()

    s3_client.put_object(
        Bucket="my-cool-bucket",
        Key="path/to/my_dataframe.csv",
        Body=csv_data,
    )


defs = Definitions(
    assets=[my_s3_asset],
    resources={"s3": S3Resource(region_name="us-west-2")},
)
```

```javascript
### About AWS S3

**Amazon Simple Storage Service (Amazon S3)** is an object storage service that offers industry-leading scalability, data availability, security, and performance. This means customers of all sizes and industries can use it to store and protect any amount of data for a range of use cases, such as data lakes, websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices, and big data analytics. Amazon S3 provides easy-to-use management features so you can organize your data and configure finely-tuned access controls to meet your specific business, organizational, and compliance requirements.
```

---

## Using AWS Secrets Manager with Dagster

> Manage, retrieve, and rotate credentials, API keys, and other secrets.

Source: https://dagster.io/integrations/dagster-aws-secretsmanager
Category: Other

### About this integration

This integration allows you to manage, retrieve, and rotate credentials, API keys, and other secrets using [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/).

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
from dagster import asset, Definitions
from dagster_aws.secretsmanager import (
    SecretsManagerResource,
    SecretsManagerSecretsResource,
)


@asset
def my_asset(secretsmanager: SecretsManagerResource):
    secret_value = secretsmanager.get_client().get_secret_value(
        SecretId="arn:aws:secretsmanager:region:aws_account_id:secret:appauthexample-AbCdEf"
    )
    return secret_value


@asset
def my_other_asset(secrets: SecretsManagerSecretsResource):
    secret_value = secrets.fetch_secrets().get("my-secret-name")
    return secret_value


defs = Definitions(
    assets=[my_asset, my_other_asset],
    resources={
        "secretsmanager": SecretsManagerResource(region_name="us-west-1"),
        "secrets": SecretsManagerSecretsResource(
            region_name="us-west-1",
            secrets_tag="dagster",
        ),
    },
)
```

### About AWS Secrets Manager

**AWS Secrets Manager** helps you protect access to your applications, services, and IT resources without the upfront cost and complexity of managing your own hardware security module (HSM) infrastructure. With Secrets Manager, you can rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. Users and applications retrieve secrets with a call to Secrets Manager APIs, eliminating the need to hardcode sensitive information in plain text.

---

## Using AWS Systems Parameter Store with Dagster

> Manage and retrieve parameters stored in AWS Systems Manager Parameter Store.

Source: https://dagster.io/integrations/dagster-aws-ssm
Category: Other

### About this integration

The Dagster AWS Systems Manager (SSM) Parameter Store integration allows you to manage and retrieve parameters stored in AWS SSM Parameter Store directly within your Dagster pipelines. This integration provides resources to fetch parameters by name, tags, or paths, and optionally set them as environment variables for your operations.

### Installation

```bash
pip install dagster-aws
```

### Examples

```python
from dagster import asset, Definitions
from dagster_aws.ssm import ParameterStoreResource, ParameterStoreTag


@asset
def example_parameter_store_asset(parameter_store: ParameterStoreResource):
    parameter_value = parameter_store.fetch_parameters(
        parameters=["my-parameter-name"]
    ).get("my-parameter-name")
    return parameter_value


@asset
def example_parameter_store_asset_with_env(parameter_store: ParameterStoreResource):
    import os

    with parameter_store.parameters_in_environment():
        return os.getenv("my-other-parameter-name")


defs = Definitions(
    assets=[example_parameter_store_asset, example_parameter_store_asset_with_env],
    resources={
        "parameter_store": ParameterStoreResource(
            region_name="us-west-1",
            parameter_tags=[
                ParameterStoreTag(key="my-tag-key", values=["my-tag-value"])
            ],
            with_decryption=True,
        )
    },
)
```

### About AWS Systems Parameter Store

**AWS Systems Manager Parameter Store** is a secure storage service for configuration data management and secrets management. It allows you to store data such as passwords, database strings, and license codes as parameter values. You can then reference these parameters in your applications or scripts, ensuring that sensitive information is not hard-coded or exposed in your codebase.

AWS Systems Manager Parameter Store integrates with AWS Identity and Access Management (IAM) to control access to parameters, and it supports encryption using AWS Key Management Service (KMS) to protect sensitive data. This service is essential for maintaining secure and manageable configurations across your AWS environment.

---

## Using Dagster with Cube

> Push changes from upstream data sources to Cube’s semantic layer.

Source: https://dagster.io/integrations/dagster-cube
Category: Other

### About this integration

With the `dagster_cube` integration you can setup Cube and Dagster to work together so that Dagster can push changes from upstream data sources to Cube via the Orchestration API.

### Installation

```bash
pip install dagster_cube
```

### Example

```python
import dagster as dg
from dagster_cube import CubeResource


@dg.asset
def cube_query_workflow(cube: CubeResource):
    response = cube.make_request(
        method="POST",
        endpoint="load",
        data={"query": {"measures": ["Orders.count"], "dimensions": ["Orders.status"]}},
    )

    return response


defs = dg.Definitions(
    assets=[cube_query_workflow],
    resources={
        "cube": CubeResource(
            instance_url="https://<<INSTANCE>>.cubecloudapp.dev/cubejs-api/v1/",
            api_key=dg.EnvVar("CUBE_API_KEY"),
        )
    },
)
```

### About Cube

**Cube.js** is the semantic layer for building data applications. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application.

---

## Using Dagster with Docker

> Run external processes in Docker containers directly from Dagster.

Source: https://dagster.io/integrations/dagster-docker
Category: Compute

### About this integration

The `dagster-docker` integration library provides the `PipesDockerClient` resource, enabling you to launch Docker containers and execute external code directly from Dagster assets and ops. This integration allows you to pass parameters to Docker containers while Dagster receives real-time events, such as logs, asset checks, and asset materializations, from the initiated jobs. With minimal code changes required on the job side, this integration is both efficient and easy to implement.

### Installation

```bash
pip install dagster-docker
```

### Example

```python
import dagster as dg
from dagster_docker import PipesDockerClient


@dg.asset
def docker_pipes_asset(
    context: dg.AssetExecutionContext, docker_pipes_client: PipesDockerClient
):
    docker_image = "python:3.9-slim"
    return docker_pipes_client.run(
        image=docker_image,
        command=[
            "python",
            "-m",
            "my_module",
        ],
        context=context,
    ).get_results()


defs = dg.Definitions(
    assets=[docker_pipes_asset],
    resources={
        "docker_pipes_client": PipesDockerClient(),
    },
)
```

### Deploying to Docker?

- Deploying to Dagster+: Use with a Dagster+ Hybrid deployment, the Docker agent executes Dagster jobs on a Docker cluster. Checkout the [Dagster+ Docker Agent](https://docs.dagster.io/deployment/dagster-plus/hybrid/docker) guide for more information.
- Deploying to Open Source: Visit the [Deploying Dagster to Docker](https://docs.dagster.io/deployment/oss/deployment-options/docker) guide for more information.

### About Docker

**Docker** is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. The service has both free and premium tiers. The software that hosts the containers is called Docker Engine.

---

## Using Dagster with Hashicorp Vault

> Centrally manage credentials and certificates, then use them in pipelines.

Source: https://dagster.io/integrations/dagster-hashicorp
Category: Other

### About this integration

Package for integrating Hashicorp Vault into Dagster so that you can securely manage tokens and passwords.

### Installation

```bash
pip install dagster-hashicorp
```

### Example

```python
# See the Resources docs to learn more: https://docs.dagster.io/guides/build/external-resources

from dagster import asset, repository, with_resources
from dagster_hashicorp.vault import vault_resource
import os

@asset(required_resource_keys={"vault"})
def example_asset(context):
    secret_data = context.resources.vault.read_secret(
        secret_path="secret/data/foo/bar"
    )
    context.log.debug(f"Secret: {secret_data}")


assets = with_resources(
    [example_asset],
    {"vault": vault_resource.configured({
        "url": "vault-host:8200",
        "auth_type": {"token": {"token": os.environ['VAULT_AUTH_TOKEN']}},
    })}
)
```

### About Hashicorp Vault

**HashiCorp** provides open-source tools and commercial products that enable developers, operators and security professionals to provision, secure, run and connect cloud-computing infrastructure. **HashiCorp Vault** secures, stores, and tightly controls access to tokens, passwords, certificates, API keys, and other secrets in modern computing.

---

## Using Dagster with Kubernetes

> Launch Kubernetes pods and execute external code directly from Dagster.

Source: https://dagster.io/integrations/dagster-kubernetes
Category: Compute

### About this integration

The `dagster-k8s` integration library provides the `PipesK8sClient` resource, enabling you to launch Kubernetes pods and execute external code directly from Dagster assets and ops. This integration allows you to pass parameters to Kubernetes pods while Dagster receives real-time events, such as logs, asset checks, and asset materializations, from the initiated jobs. With minimal code changes required on the job side, this integration is both efficient and easy to implement.

### Installation

```bash
pip install dagster-k8s
```

### Example

```python
import dagster as dg
from dagster_k8s import PipesK8sClient


@dg.asset
def k8s_pipes_asset(
    context: dg.AssetExecutionContext, k8s_pipes_client: PipesK8sClient
):
    return k8s_pipes_client.run(
        context=context,
        image="pipes-example:v1",
    ).get_materialize_result()


defs = dg.Definitions(
    assets=[k8s_pipes_asset],
    resources={
        "k8s_pipes_client": PipesK8sClient(),
    },
)
```

### Deploying to Kubernetes?

- Deploying to Dagster+: Use with a Dagster+ Hybrid deployment, the Kubernetes agent executes Dagster jobs on a Kubernetes cluster. Checkout the [Dagster+ Kubernetes Agent](https://docs.dagster.io/deployment/dagster-plus/hybrid/kubernetes) guide for more information.
- Deploying to Open Source: Visit the [Deploying Dagster to Kubernetes](https://docs.dagster.io/deployment/oss/deployment-options/kubernetes) guide for more information.

### About Kubernetes

**Kubernetes** is an open-source container orchestration system for automating software deployment, scaling, and management. Google originally designed Kubernetes, but the Cloud Native Computing Foundation now maintains the project.

---

## Using Dagster with Looker

> Monitor a Looker instance as assets in Dagster alongside other data assets.

Source: https://dagster.io/integrations/dagster-looker
Category: Business Intelligence

### About this integration

Dagster allows you to represent your Looker instance as assets, alongside other your other technologies like dbt and Sling. This allows you to see how your Looker assets are connected to your other data assets, and how changes to other data assets might impact your Looker instance. This also enables you to orchestrate the building of your persistent derived tables (PDTs), ensuring your Looker instance reflects the most current data from upstream sources.

### Installation

```bash
pip install dagster-looker
```

### Example

```python
from dagster_looker import (
    LookerResource,
    RequestStartPdtBuild,
    build_looker_pdt_assets_definitions,
    load_looker_asset_specs,
)

import dagster as dg

looker_resource = LookerResource(
    client_id=dg.EnvVar("LOOKERSDK_CLIENT_ID"),
    client_secret=dg.EnvVar("LOOKERSDK_CLIENT_SECRET"),
    base_url=dg.EnvVar("LOOKERSDK_HOST_URL"),
)

looker_specs = load_looker_asset_specs(looker_resource=looker_resource)

pdts = build_looker_pdt_assets_definitions(
    resource_key="looker",
    request_start_pdt_builds=[
        RequestStartPdtBuild(model_name="my_model", view_name="my_view")
    ],
)

defs = dg.Definitions(
    assets=[*pdts, *looker_specs],
    resources={"looker": looker_resource},
)
```

### About Looker

**Looker** is a modern platform for data analytics and visualization. It provides a unified interface for data exploration, modeling, and visualization, making it easier to understand and analyze data. Looker integrates with various data sources and can be used to create interactive reports, dashboards, and visualizations.

---

## Using Dagster with Power BI

> Monitor a Power BI workspace as assets in Dagster.

Source: https://dagster.io/integrations/dagster-powerbi
Category: Business Intelligence

### About this integration

Dagster allows you to represent your Power BI workspace as assets, alongside other your other technologies like dbt and Sling. This allows you to see how your Power BI assets are connected to your other data assets, and how changes to other data assets might impact your Power BI workspace. This also enables you to orchestrate the refreshing of your semantic models, ensuring your Power BI workspace reflects the most current data from upstream sources.

### Installation

```bash
pip install dagster-powerbi
```

### Example

```python
from dagster_powerbi import (
    PowerBIServicePrincipal,
    PowerBIWorkspace,
    build_semantic_model_refresh_asset_definition,
    load_powerbi_asset_specs,
)

import dagster as dg

power_bi_workspace = PowerBIWorkspace(
    credentials=PowerBIServicePrincipal(
        client_id=dg.EnvVar("POWER_BI_CLIENT_ID"),
        client_secret=dg.EnvVar("POWER_BI_CLIENT_SECRET"),
        tenant_id=dg.EnvVar("POWER_BI_TENANT_ID"),
    ),
    workspace_id=dg.EnvVar("POWER_BI_WORKSPACE_ID"),
)

power_bi_assets = [
    build_semantic_model_refresh_asset_definition(resource_key="power_bi", spec=spec)
    if spec.tags.get("dagster-powerbi/asset_type") == "semantic_model"
    else spec
    for spec in load_powerbi_asset_specs(power_bi_workspace)
]

defs = dg.Definitions(
    assets=[*power_bi_assets], resources={"power_bi": power_bi_workspace}
)
```

### About Power BI

**Power BI** is a modern platform for data analytics and visualization. Power BI integrates with various data sources and can be used to create interactive reports, semantic models and dashboards.

---

## Using Dagster with Sigma

> Monitor a Sigma organization as assets in Dagster.

Source: https://dagster.io/integrations/dagster-sigma
Category: Business Intelligence

### About this integration

Dagster allows you to represent your Sigma organization as assets, alongside other your other technologies like dbt and Sling. This allows you to see how your Sigma assets are connected to your other data assets, and how changes to other data assets might impact your Sigma organization.

### Installation

```bash
pip install dagster-sigma
```

### Example

```python
from dagster_sigma import SigmaBaseUrl, SigmaOrganization, load_sigma_asset_specs

import dagster as dg

sigma_organization = SigmaOrganization(
    base_url=SigmaBaseUrl.AWS_US,
    client_id=dg.EnvVar("SIGMA_CLIENT_ID"),
    client_secret=dg.EnvVar("SIGMA_CLIENT_SECRET"),
)

sigma_specs = load_sigma_asset_specs(sigma_organization)
defs = dg.Definitions(assets=[*sigma_specs], resources={"sigma": sigma_organization})
```

### About Sigma

**Sigma** is a modern platform for data analytics and visualization. Sigma integrates with various data sources and can be used to create interactive datasets and workbooks.

---

## Using Dagster with Tableau

> Monitor a Tableau workspace as assets in Dagster.

Source: https://dagster.io/integrations/dagster-tableau
Category: Business Intelligence

### About this integration

Dagster allows you to represent your Tableau workspace as assets, alongside your other technologies like dbt and Sling. This allows you to see how your Tableau assets are connected to your other data assets, and how changes to other data assets might impact your Tableau workspace. This also enables you to orchestrate the refreshing of your workbooks, ensuring your Tableau workspace reflects the most current data from upstream sources.

### Installation

```bash
pip install dagster-tableau
```

### Example

```python
from dagster_tableau import (
    TableauCloudWorkspace,
    build_tableau_materializable_assets_definition,
    load_tableau_asset_specs,
)

import dagster as dg

tableau_workspace = TableauCloudWorkspace(
    connected_app_client_id=dg.EnvVar("TABLEAU_CONNECTED_APP_CLIENT_ID"),
    connected_app_secret_id=dg.EnvVar("TABLEAU_CONNECTED_APP_SECRET_ID"),
    connected_app_secret_value=dg.EnvVar("TABLEAU_CONNECTED_APP_SECRET_VALUE"),
    username=dg.EnvVar("TABLEAU_USERNAME"),
    site_name=dg.EnvVar("TABLEAU_SITE_NAME"),
    pod_name=dg.EnvVar("TABLEAU_POD_NAME"),
)

# Load Tableau asset specs
tableau_specs = load_tableau_asset_specs(
    workspace=tableau_workspace,
)

external_asset_specs = [
    spec
    for spec in tableau_specs
    if spec.tags.get("dagster-tableau/asset_type") == "data_source"
]

materializable_asset_specs = [
    spec
    for spec in tableau_specs
    if spec.tags.get("dagster-tableau/asset_type") in ["dashboard", "sheet"]
]

# Use the asset definition builder to construct the definition for tableau materializable assets
defs = dg.Definitions(
    assets=[
        build_tableau_materializable_assets_definition(
            resource_key="tableau",
            specs=materializable_asset_specs,
            refreshable_workbook_ids=["b75fc023-a7ca-4115-857b-4342028640d0"],
        ),
        *external_asset_specs,
    ],
    resources={"tableau": tableau_workspace},
)
```

### About Tableau

**Tableau** is a modern platform for data analytics and visualization. Tableau integrates with various data sources and can be used to create interactive workbooks, sheets and dashboards.

---

## Using Databricks with Dagster

> Initiate Databricks jobs, pass parameters, and stream logs back into Dagster.

Source: https://dagster.io/integrations/dagster-databricks
Category: Compute

### About this Integration

The `dagster-databricks` integration library provides the `PipesDatabricksClient` resource, enabling you to launch Databricks jobs directly from Dagster assets and ops. This integration allows you to pass parameters to Databricks code while Dagster receives real-time events, such as logs, asset checks, and asset materializations, from the initiated jobs. With minimal code changes required on the job side, this integration is both efficient and easy to implement.

### Installation

```bash
pip install dagster-databricks
```

### Example

#### Dagster Code

```python
import os
import sys

from dagster_databricks import PipesDatabricksClient

from dagster import AssetExecutionContext, Definitions, EnvVar, asset
from databricks.sdk import WorkspaceClient
from databricks.sdk.service import jobs


@asset
def databricks_asset(
    context: AssetExecutionContext, pipes_databricks: PipesDatabricksClient
):
    task = jobs.SubmitTask.from_dict(
        {
            # The cluster settings below are somewhat arbitrary. Dagster Pipes is
            # not dependent on a specific spark version, node type, or number of
            # workers.
            "new_cluster": {
                "spark_version": "12.2.x-scala2.12",
                "node_type_id": "i3.xlarge",
                "num_workers": 0,
                "cluster_log_conf": {
                    "dbfs": {"destination": "dbfs:/cluster-logs-dir-noexist"},
                },
            },
            "libraries": [
                # Include the latest published version of dagster-pipes on PyPI
                # in the task environment
                {"pypi": {"package": "dagster-pipes"}},
            ],
            "task_key": "some-key",
            "spark_python_task": {
                "python_file": "dbfs:/my_python_script.py",  # location of target code file
                "source": jobs.Source.WORKSPACE,
            },
        }
    )

    print("This will be forwarded back to Dagster stdout")
    print("This will be forwarded back to Dagster stderr", file=sys.stderr)

    extras = {"some_parameter": 100}

    return pipes_databricks.run(
        task=task,
        context=context,
        extras=extras,
    ).get_materialize_result()


pipes_databricks_resource = PipesDatabricksClient(
    client=WorkspaceClient(
        host=os.environ["DATABRICKS_HOST"],
        token=os.environ["DATABRICKS_TOKEN"],
    )
)

defs = Definitions(
    assets=[databricks_asset], resources={"pipes_databricks": pipes_databricks_resource}
)
```

#### Databricks Code

```python
from dagster_pipes import (
    PipesDbfsContextLoader,
    PipesDbfsMessageWriter,
    open_dagster_pipes,
)

# Sets up communication channels and downloads the context data sent from Dagster.
# Note that while other `context_loader` and `message_writer` settings are
# possible, it is recommended to use `PipesDbfsContextLoader` and
# `PipesDbfsMessageWriter` for Databricks.
with open_dagster_pipes(
    context_loader=PipesDbfsContextLoader(),
    message_writer=PipesDbfsMessageWriter(),
) as pipes:
    # Access the `extras` dict passed when launching the job from Dagster.
    some_parameter_value = pipes.get_extra("some_parameter")

    # Stream log message back to Dagster
    pipes.log.info(f"Using some_parameter value: {some_parameter_value}")

    # ... your code that computes and persists the asset

    # Stream asset materialization metadata and data version back to Dagster.
    # This should be called after you've computed and stored the asset value. We
    # omit the asset key here because there is only one asset in scope, but for
    # multi-assets you can pass an `asset_key` parameter.
    pipes.report_asset_materialization(
        metadata={
            "some_metric": {"raw_value": some_parameter_value + 1, "type": "int"}
        },
        data_version="alpha",
    )
```

### About Databricks

**Databricks** is a unified data analytics platform that simplifies and accelerates the process of building big data and AI solutions. It integrates seamlessly with Apache Spark and offers support for various data sources and formats. Databricks provides robust tools to create, run, and manage data pipelines, making it easier to handle complex data engineering tasks. Its collaborative and scalable environment is ideal for data engineers, scientists, and analysts who need to process and analyze large datasets efficiently.

---

## Using dlt with Dagster

> Easily ingest and replicate data between systems with dlt through Dagster.

Source: https://dagster.io/integrations/dagster-dlt
Category: ETL / Reverse ETL

### About this integration

This integration allows you to use [dlt](https://dlthub.com/) to easily ingest and replicate data between systems through Dagster.

### Installation

```bash
pip install dagster-embedded-elt
```

### Example

```python
import dagster as dg
from dagster_embedded_elt.dlt import DagsterDltResource, dlt_assets
from dlt import pipeline
from dlt_sources.github import github_reactions


@dlt_assets(
    dlt_source=github_reactions("dagster-io", "dagster"),
    dlt_pipeline=pipeline(
        pipeline_name="github_issues",
        dataset_name="github",
        destination="snowflake",
    ),
    name="github",
    group_name="github",
)
def github_issues_to_snowflake_assets(
    context: dg.AssetExecutionContext, dlt: DagsterDltResource
):
    yield from dlt.run(context=context)


defs = dg.Definitions(
    assets=[
        github_issues_to_snowflake_assets,
    ],
    resources={
        "dlt": DagsterDltResource(),
    },
)
```

### About dlt

[Data Load Tool (dlt)](https://dlthub.com/) is an open-source library for creating efficient data pipelines. It offers features like secret management, data structure conversion, incremental updates, and pre-built sources and destinations, simplifying the process of loading messy data into well-structured datasets.

---

## Using Sling with Dagster

> Extract and load data from popular sources to destinations with Sling.

Source: https://dagster.io/integrations/dagster-sling
Category: ETL / Reverse ETL

### About this integration

This integration allows you to use [Sling](https://slingdata.io/) to extract and load data from popular data sources to destinations with high performance and ease.

### Installation

```bash
pip install dagster-embedded-elt
```

### Example

```python
import dagster as dg
from dagster_embedded_elt.sling import (
    SlingConnectionResource,
    SlingResource,
    sling_assets,
)

source = SlingConnectionResource(
    name="MY_PG",
    type="postgres",
    host="localhost",
    port=5432,
    database="my_database",
    user="my_user",
    password=dg.EnvVar("PG_PASS"),
)

target = SlingConnectionResource(
    name="MY_SF",
    type="snowflake",
    host="hostname.snowflake",
    user="username",
    database="database",
    password=dg.EnvVar("SF_PASSWORD"),
    role="role",
)


@sling_assets(
    replication_config={
        "SOURCE": "MY_PG",
        "TARGET": "MY_SF",
        "defaults": {
            "mode": "full-refresh",
            "object": "{stream_schema}_{stream_table}",
        },
        "streams": {
            "public.accounts": None,
            "public.users": None,
            "public.finance_departments": {"object": "departments"},
        },
    }
)
def sling_assets(context, sling: SlingResource):
    yield from sling.replicate(context=context)


defs = dg.Definitions(
    assets=[sling_assets],
    resources={
        "sling": SlingResource(
            connections=[
                source,
                target,
            ]
        )
    },
)
```

### About dlt

Sling provides an easy-to-use YAML configuration layer for loading data from files, replicating data between databases, exporting custom SQL queries to cloud storage, and much more.


# Customer stories

## Flo Energy's Data Platform for Critical Energy Data

> Learn why modern data orchestration is about more than scheduling and how Dagster's declarative automation continuously determines what needs to run based on the state of your data assets.

Source: https://dagster.io/customers/flo-energys-data-platform-for-critical-energy-data
Published: 2026-08-04

[Flo Energy’s](https://floenergy.sg/) aim is to help businesses access affordable, renewable electricity solutions. In Singapore, Flo positions itself as a leading independent electricity retailer, offering commercial electricity plans, sustainability support, and tools like the FlowSmart dashboard for usage, billing, and contract management. In Australia, Flo extends that model with commercial electricity plans and renewable certificate offerings, including products that match customers' electricity consumption with Large-scale Generation Certificates (LGCs).

Supporting our business is a data operation that turns fragmented meter, weather, market, and strategy data into reliable insights. To accomplish this, Flo adopted Dagster as its centralized orchestration layer. What began as a platform for the data team has since evolved into an important platform supporting the business, enabling engineers and adjacent teams to build, monitor, and debug workflows across the organization.

### Turning energy data into business decisions

Energy companies rely on data that is both technically reliable and operationally valuable. At Flo, Nils Terpstra, who leads the data and analytics team, estimates that 80% of the company's business is data-centric, requiring the synthesis of complex meter, weather, market, and strategy data into actionable insights.

This challenge is common across the energy industry, where providers operate in fragmented, time-sensitive environments. Pipeline failures are more than engineering headaches; they can disrupt pricing, forecasting, reporting, and overall operational confidence.

Flo required a platform that could unify scheduled batch workflows, event-driven processes, dbt transformations, Snowflake analytics, and downstream BI, while remaining accessible enough for broad cross-functional collaboration.

### A history with Dagster

When Nils joined Flo, he was not approaching Dagster for the first time. One year earlier, he had worked at another electricity retailer facing similar problems. That team had been using an alternative orchestration tool to run data jobs.

This alternative tool could run scheduled jobs and trigger work, but it did not provide the asset-aware visibility, dependency management, or data-specific debugging experience that energy data workflows require. Realizing the shortcomings, the data team successfully migrated the company's data workflows to Dagster as a dedicated data orchestration platform.

That earlier experience shaped the decision when Nils joined Flo. Rather than start with generic job scheduling and grow into orchestration later, Nils chose Dagster at Flo when establishing the data team.

> When I joined Flo and saw a similar need, it was clear that Dagster was the right foundation to start with and it was great to see how much the platform had matured, from the features and integrations to the documentation.

That maturity mattered. Flo was not just looking for a tool that could run jobs. They needed a platform that could support the way an energy data team actually works: ingesting data from multiple sources, transforming it, validating it, and exposing the results to both technical and business users.

### Scaling with Dagster

Dagster integrates Flo’s analytics stack: [Snowflake](https://www.snowflake.com) for storage, [dbt](https://www.getdbt.com/) for transformations, and Holistics for reporting all into a unified orchestration layer. This is ideal for a platform that requires both scheduled batch jobs and event-driven updates.

Event-driven workflows are handled by Dagster’s [sensor-based automation](https://docs.dagster.io/guides/automate/sensors) coupled with [schedules](https://docs.dagster.io/guides/automate/schedules) to handle orchestration. Rather than feeling like disconnected jobs, everything works together to produce the final forecasts necessary for the business. As the platform grew, Dagster’s code locations enabled decentralized ownership, allowing [teams](https://docs.dagster.io/deployment/dagster-plus/authentication-and-access-control/rbac/teams) to manage their own workflows while maintaining visibility and operational control across the organization.

Today, the platform serves as a centralized hub, successfully orchestrating more than a thousand assets to support Flo's commercial operations.

By choosing this platform approach rather than the reactive "maintenance approach”, Flo positioned the platform to scale alongside their business needs.

### Observable energy workflows

With Dagster, Flo’s workflows became easier to trace from source data to downstream outputs. When something failed, the team could see where it failed, what depended on it, and what needed to be rerun. Instead of asking whether a scheduled job had completed somewhere, the team could inspect the state of the data assets themselves.

That shift is particularly important in energy, where data freshness and completeness matter. If meter data is missing, weather data is stale, or a transformation fails silently, downstream pricing and forecasting workflows can become less trustworthy. Dagster helped make those risks visible.

The result was a platform that supported rapid troubleshooting and more confident operations. Failures became easier to investigate. Alerts became easier to connect to the workflows they represented. Data quality checks became part of the pipeline itself, rather than a separate manual process.

With this new observability, the team has achieved an average materialisation success rate of approximately 99.9% across its monitored production workloads. 

### Building quality into the pipeline

Data quality is fundamental for an energy provider. Flo’s data needs to support operational decisions, commercial reporting, sustainability programs, and customer-facing insights. That means the platform needs to identify issues before they affect downstream reporting and analytics.

Dagster’s [asset checks](https://docs.dagster.io/guides/test/asset-checks) gave Flo a way to bring validation closer to the assets being produced. Asset checks are designed to test specific properties of data such as whether a column contains null values, whether a table matches an expected schema, or whether an asset needs refreshing. Dagster also supports modeling existing dbt tests as asset checks.

That matters because Flo already uses dbt for transformation. Instead of duplicating business logic in the orchestrator, Dagster can let dbt remain the place where transformation logic lives, while representing dbt models, dependencies, failures, logs, run history, and checks in the Dagster asset graph. Dagster’s [dbt integration](https://docs.dagster.io/integrations/libraries/dbt) represents dbt models as assets and lets teams track failures, logs, run history, and dependencies at the level of individual dbt models.

For Flo, that separation of concerns is important. Dagster orchestrates the system. dbt owns much of the transformation logic. Snowflake stores the analytical data. Holistics exposes the outputs. Each layer has a clear role, and Dagster makes the whole system observable.

### Why Dagster works for energy data teams

Energy data platforms have a specific shape. They are multi-source, time-sensitive, and decision-oriented. They combine historical usage, external market signals, operational constraints, customer information, and forecasts. They often require both scheduled processing and event-driven automation.

Dagster fit Flo’s needs because it gave the team a practical way to organize that complexity.

**Python and YAML support made the platform approachable and flexible.** Flo could use Python where workflows required full programming flexibility, while [Dagster Components](https://docs.dagster.io/guides/build/components) and YAML-based definitions made common patterns easier to read and maintain. Dagster Components are designed to provide project structure, common integration types, and a YAML DSL.

**Native dbt support kept transformation logic where it belonged.** Flo did not need to move business logic out of other systems and into the orchestration layer. Instead, Dagster could represent dbt models as assets, connect them to upstream and downstream dependencies, and surface failures and checks in the orchestration layer.

**Sensors enabled event-driven workflows.** Not every energy workflow has a consistent schedule. Some workflows should run when external data arrives or when an upstream asset is ready. Dagster sensors gave Flo a way to respond to those events and build more natural automation around real-world data availability.

**Code locations helped teams scale ownership.** As adoption grows across engineering and adjacent teams, Flo could separate workflows into different code locations. That will give teams autonomy without creating one tangled project. Data workflows can remain cleanly separated from other internal workflows, while the organization still benefited from a shared orchestration layer.

**Debugging became a platform capability, not a tribal skill.** For Nils, one of the clearest benefits of Dagster was the ability to understand what happened when something went wrong. Having previously witnessed a migration to Dagster at his former company, Nils specifically valued how Dagster replaced the tribal, manual debugging required in the old tool with a platform capability. Instead of chasing logs across disconnected systems, teams could use Dagster to inspect runs, assets, dependencies, and failures in context.

### **Advice for teams building with Dagster**

When asked what advice he would give to other teams evaluating Dagster, Nils kept it simple:

> Just do it.

For those unsure where to start, consider picking one core, high-impact data pipeline such as a critical forecasting or billing workflow and migrate it to Dagster. This provides immediate visibility and proof of value without requiring a full infrastructure overhaul.

That advice reflects Flo’s experience. Starting with Dagster gave Flo a foundation that could support that growth. It allowed the team to begin with core data workflows, then expand into more event-driven automation, more quality checks, more dbt integration, and more team-owned code locations over time.

For energy companies facing similar challenges, Flo’s story offers a useful pattern: do not wait until orchestration becomes painful to invest in it. Build the platform before the complexity becomes unmanageable.

---

## How Magenta Telekom Built the Unsinkable Data Platform

> Learn how Magenta Telekom replaced fragmented, manual data workflows with a modular, Dagster-powered platform that reduced onboarding from 3 months to 1 day and laid the foundation for AI-driven decision-making.

Source: https://dagster.io/customers/how-magenta-telekom-built-the-unsinkable-data-platform
Published: 2026-02-25

[Magenta Telekom](https://www.magenta.at/), a leading Austrian telecom provider and subsidiary of Deutsche Telekom, serves over 5 million customers with mobile, internet, and TV services. To modernize its data infrastructure and reduce reliance on manual workflows, Magenta adopted Dagster as the core of its new data platform. With Dagster, Magenta is building a modular, scalable data platform that enables secure, observable, and efficient data operations across teams - laying the foundation for Data- and AI-driven decision-making that delights Magenta customers and drives above-average revenue growth to outpace the market.

- **Unification:** Siloed data in Excel → Centralized, observable data domains
- **Standardization**: Bespoke scripting and shadow IT -> Blueprint-based modular platform
- **Governance:** Catalog chaos and debate -> Declarative, enforceable domain standards
- **Onboarding:** 3 months -> 1 day

### Modernizing a legacy data system

Magenta  is a company built on data. From streaming video services to core telco infrastructure and billing systems, Magenta's business depends on data flowing reliably and safely across dozens of teams, tools, and services.

For years, Magenta’s data environment was loosely connected and complex. Many critical workflows were managed through Excel sheets shared over email, and two-thirds of engineering efforts were dedicated to maintaining legacy systems. Shadow IT had naturally emerged in some areas, and without a unified governance model, internal data catalogs developed independently rather than in coordination.

This case study uncovers how Magenta turned those challenges into an opportunity: reimagining its data platform from the ground up. With Dagster as the orchestration backbone, Magenta has built a modular, scalable architecture that empowers developers, accelerates onboarding, and sets the stage for long-term innovation and AI readiness.

### Designing a platform to scale with the business

Magenta set out to overhaul its data infrastructure, not just to modernize it, but to re-architect how data is developed, governed, and used across the company. Legacy systems had drained engineering resources, while disconnected tooling and manual processes slowed innovation and introduced risk. Magenta needed a platform that could bring visibility into all data work, eliminate shadow IT, automate error-prone workflows, and enable both security and speed at scale. They weren’t just looking for a better scheduler; they were building a composable, secure, developer-first platform that could grow with their business.

What Magenta needed in a data orchestration platform:

- **Free up engineering time from legacy maintenance:** A modern platform with automation and standardized blueprints that reduces manual upkeep and enables teams to focus on net-new development.
- **Replace manual Excel workflows used in critical processes:** End-to-end data pipelines that automate ingestion, validation, and reporting, as well as support business logic in reusable, tested components.
- **Bring visibility to all data pipelines, including those built by analysts or in the shadows:** A unified orchestration layer with full asset lineage, observability, and support for both engineering and business user workflows.
- **Resolve governance disagreements and enforce standards without friction:** A declarative architecture that embeds governance (like testing, lineage, and metadata capture) directly into the development lifecycle.
- **Onboard new contributors, including offshore consultants, without months of setup:** Pre-configured, containerized environments with access controls and network setup out of the box, so developers could start building from day one.
- **Support secure, scalable experimentation with AI and machine learning:** Environments that separate production from experimentation, with guardrails like data masking, role-based access control, and reproducible infrastructure for sensitive data work.

### Why Magenta chose Dagster

> What we want to build is something that is like an unsinkable ship… compartmentalized on its own, with adjacent components for things that would traditionally have floated off into shadow IT.\
>  - Georg Heiler, Senior Data Expert, Magenta

The Magenta data enablement team has been satisfied with having Dagster in their stack, saying, “It includes the quality components, and it’s just so much smoother, end-to-end.” The team also appreciates that Dagster provides:

- **Composable, modular architecture:**  Magenta needed a platform that allowed them to define standardized data "domains:” isolated, self-contained, and deployable units that teams could build and operate independently. Dagster's project structure and support for [code locations](https://docs.dagster.io/guides/build/projects) and multi-project setups aligned perfectly with this need.
- **Developer-first experience:** Magenta’s data enablement team wanted tools that felt like modern software engineering: with CI/CD pipelines, testability, and low-friction local development. Dagster’s focus on developer experience, including testing, observability, and asset lineage, was a major draw.
- **Declarative, asset-based orchestration:** They were drawn to [Dagster’s asset-centric model](https://docs.dagster.io/guides/build/assets/defining-assets), which organizes workflows around data outputs instead of just tasks or steps. This made governance easier, improved traceability, and simplified debugging.
- **Support for secure, scalable environments:** With strict compliance and data security requirements, Magenta needed to isolate production workloads from development and enable safe collaboration with external teams. Dagster’s code location abstraction and ability to separate orchestration from execution helped enable this.
- **Observability and governance embedded in the platform:** Dagster’s built-in asset lineage, testing, and metadata features provide Magenta a foundation for improved governance without slowing teams down or adding yet another tool to manage.
- **Integration flexibility:** Magenta needed a platform that supports Spark, REST APIs, Tableau dashboards, and eventually machine learning workflows. Dagster’s flexible plugin system, support for custom IO managers, and integrations like dbt and Tableau made it a natural fit.

Dagster’s architecture gave them confidence that they could extend the platform over time to support new use cases (like LLMs, secure AI workflows, or external data products).

> This is not just a data warehouse… this is a platform built to scale with the business.

Magenta did more than just adopt Dagster: they designed an entire platform around it. The team adopted a blueprint-driven approach, combining best practices with the necessary flexibility for large-scale enterprise data environments. Their goal was to create a composable, secure, and self-serve ecosystem where developers and analysts alike could build, deploy, and manage data workflows with confidence.

**Blueprint-based data domains:** At the heart of Magenta’s implementation is the concept of standardized data domains. Each domain, whether for network planning, billing, or analytics, is instantiated from a shared blueprint that includes:

- A Docker container for reproducible runtime environments
- Dagster Code locations for orchestration and asset lineage
- dbt project for each domain, SQL-based modeling, and data testing using this [project](https://github.com/nicholasyager/dbt-loom) to give a dbt cloud-like experience with dbt core. 
- GitLab CI/CD pipelines for continuous integration and delivery
- Pre-configured Python libraries and utility scripts. Georg Heiler has discussed this topic here and provides a starter template for those who want to try it [hands-on](https://github.com/l-mds/local-data-stack/). 
- Infrastructure-level data domain isolation using cloud-native security boundaries (i.e., in Azure subscriptions, in GCP projects, and in AWS accounts).
- Shared logic for data ingestion, validation, and output formatting
- Standardized integrations with VertexAI, DuckLake, and Tableau

Domains are customized with [cruft](https://cruft.github.io/cruft/) to fit their specific use case, but still benefit from centralized updates and automation. When the core blueprint evolves, improvements can be rolled out automatically to all domains.

**GitLab Workspaces for day-one productivity:** To streamline onboarding and collaboration, Magenta implemented secure, cloud-based GitLab Workspaces. These environments provide a turn-key solution for working with data. They:

- Come pre-loaded with the right packages, network permissions, and access controls and resources - even GPUs, this saves countless hours of troubleshooting by delivering a turn-key solution to work with data in Magenta. 
- Let internal developers and external consultants spin up a dev environment instantly, increasing productivity without sacrificing governance
- Eliminate the three to five-month ramp-up previously required for offshore contributors. This is achieved by
  - Provisioned infrastructure (all the tools are set up, licenses pre-prepared, and flexible resource configuration)
  - Firewalls pre-configured (jump host)
  - By utilizing Dagster’s built-in dependency injection to quickly enable less technical users. Using the right levels of abstraction (IO managers, resources) allows experts to design them, and others vibe code business logic. No longer are the limits of collaboration with business a long lead time to move to production. All critical and complex concerns are already handled and abstracted, allowing people to focus directly on working with the data. A bit similar to Outlook for emails.
- Support safe collaboration without compromising security or compliance

**Integrated ingestion and transformation layers:** Magenta built an abstraction layer for data ingestion, using [Dagster Pipes](https://docs.dagster.io/integrations/libraries/pipes/dagster-pipes) to handle Spark-based workloads and embedded ELT for simpler use cases. This allowed them to standardize ingestion across domains, while still supporting:

- REST APIs and flat files
- Large-scale batch processing with Spark
- Lightweight pipelines for internal automation

![](https://dagster.io/webflow-assets/60d6e613f28c537d9d8c.png)

Transformation logic is handled using [dbt](https://docs.dagster.io/integrations/libraries/dbt), which is fully integrated into the Dagster asset graph. Analysts can contribute models directly via Git workflows, with automated testing and validation built in.

**Data warehouse foundation with Data Vault modeling:** Underlying Magenta's orchestration layer is a Data Vault approach that automates the mechanics of the warehouse itself. While Dagster orchestrates the overall data usage across the enterprise from ingestion through to business intelligence, the Data Vault methodology provides the resilient, auditable foundation for storing and historizing raw business data. This separation of concerns allows Dagster to focus on workflow orchestration and business logic, while Data Vault handles the structural integrity and scalability of the warehouse layer.

**Tableau and BI automation:** To close the loop from ingestion to insight, Magenta created a custom Tableau integration that allows Dagster to refresh only the dashboards affected by upstream data changes. This reduces costs and ensures faster, more relevant updates across the business intelligence layer.

**Governance and security:** Magenta embedded governance directly into their pipelines using Dagster’s declarative asset model and custom test libraries. Features include:

- Stateless and stateful data quality checks
- Custom dbt macros for business-specific rules
- Asset-based lineage tracking and auditing
- Metadata capture to support data catalog integrations with DataHub and Collibra. 

Security was also a top priority. Magenta architected its platform to separate production and development environments, enabling secure experimentation with masked and anonymized data, as well as strict role-based access control. 

### From bottlenecks to breakthroughs

Within six months of implementation, Magenta has experienced measurable improvements across its data ecosystem from engineering velocity to business confidence in data. The platform hasn’t just modernized how workflows run; it’s reshaped how teams collaborate, build, and scale. With Dagster, Magenta now benefits from:

**Faster time to value**

With pre-configured development environments and blueprint-based domain templates, Magenta teams can now go from zero to shipping data pipelines in a matter of hours. This has reduced onboarding time from months to a single day, allowing internal and external teams to contribute immediately, focusing on business impact instead of being bogged down by infrastructure impediments.

**Unified observability and lineage**

Dagster’s asset-centric approach provides full visibility into data flows: from ingestion to reporting. Teams no longer rely on ad hoc scripts or guesswork to understand what’s happening. With built-in testing, lineage, data quality, and metadata, data flows smoothly through the system, enabling stakeholders to rely on accurate and timely information. Building this out is foundational work for enabling a semantic layer that will allow less technical users to chat with the data for true self-service analytics. 

**Modular, scalable architecture**

By organizing each use case as a separate domain, Magenta can scale its platform efficiently without increasing complexity. New domains automatically inherit shared standards and best practices, while platform updates can be deployed centrally across all domains. With many domains already in place, Magenta needed a solution that enables the organization to quickly provision new workspaces and manage access without requiring custom development each time.

[Georg](https://www.linkedin.com/in/geoheil/) and [Aleksander Milicevic](https://www.linkedin.com/in/milicevica23/) recently gave a talk at the Vienna Data engineering meetup on this architecture and approach. You can check out the talk [here](https://georgheiler.com/event/scaling-data-pipelines-@telekom/).

**Enterprise-grade governance**

Separation between standardized developments and production environments ensures compliance, while new support for masked and anonymized data access enables secure experimentation with sensitive data. Magenta can now confidently allow analysts and external partners to collaborate within tightly scoped environments.

**Reduced cost and errors**

Replacing manual workflows, such as Excel-based billing & various SQL Scripts without a single source of truth, with automated, validated pipelines has improved operational efficiency and reduced the risk of human error. BI dashboards are now refreshed intelligently, enhancing data quality and keeping dashboards up to date. 

> *We were driving with a 30-year-old Fiat car, and now we have a Formula One racing car.*

### Whats Next

> *We would say this is our game-changing platform. Instead of onboarding for several months, we have day one productivity.*

Future plans for Magenta and Dagster: with strong backing from its new Data Tribe Lead, Magenta aims to increase engineering investment in the new platform from recent 20–30% to over 80%, as the new GCP-based platform, Pluto, becomes the unified and converged data foundation that will position Magenta as a Data & AI powerhouse. Magenta also plans to roll out its first fully integrated, end-to-end commercial use cases that will showcase the complete data lifecycle - from ingestion to transformation to visualization - all powered by Dagster.

---

## Scaling fintech: How smava achieved zero downtime with Dagster

> German fintech smava eliminated alert fatigue and achieved zero downtime by migrating to Dagster, automating 1000+ dbt models through YAML-driven code generation and reducing developer onboarding to 15 minutes

Source: https://dagster.io/customers/smava-case-study
Published: 2025-11-25

### Key results

- **Zero downtime**: No downtime since July 2025
- **Eliminated failure alerts**: Maintenance overhead “almost down to zero”
- **1,000+ models**: Automated generation of more than 1,000 dbt models
- **Team satisfaction**: Better developer experience, less alert fatigue, and higher productivity
- **Scalability confidence**: Assurance that the platform can scale with smava’s growth targets

[smava](https://www.smava.de/) is a Berlin-based fintech pioneer (founded in 2007) that has built one of Germany’s leading online credit-comparison and brokerage platforms.

The company enables consumers to compare personal-loan offers from more than 20 banking and credit-partner institutions through a single, streamlined digital interface.

By leveraging digitized workflows, tailored loan recommendations, and a broad marketplace of lenders, smava empowers borrowers to secure significantly better and fairer loan terms, making loans **transparent**, **fair**, and **affordable**.

In short, smava is helping to digitize and disrupt Germany’s traditionally paper-heavy and bank-centric credit market by offering transparency, choice, and speed in personal-loan origination.

Alex Naumov is Principal Data Architect at smava, where he leads the Data Engineering & Architecture function and manages data and analytics engineers. He is driven by a great passion for data and transforming organizations to become data-driven and the best in what they do. Fascinated by coding and computers since his early teens, Alex has worked in the data domain for around 16 years: ten as a consultant, mostly as a data or solution architect, and since 2019 with smava in Berlin. His experience spans 8 countries, 10+ sectors and roughly 30 projects/customers, and a wide variety of technologies.

### **The challenge**

As smava’s data ecosystem evolved alongside rapid business growth, its original data infrastructure began reaching the limits of scalability and maintainability. The system relied on a combination of legacy tools, including a custom Java application for Data Vault code generation and orchestration paired with a low-code ETL platform, that had served the company well in earlier stages. However, as data volumes and complexity increased, this setup made it difficult to maintain transparency, consistency, and agility.

Key pain points included limited version control, CI/CD workflows, and visibility into operational issues, all of which slowed down development and troubleshooting. When failures occurred, the team had little autonomy in resolving them quickly due to dependency on external vendor support.

Over time, the maintenance load began to outweigh innovation. The data team faced an ever-growing queue of alerts and manual interventions, while the broader organization’s data demands continued to rise in line with smava’s growth targets and business requirements.

To continue supporting smava’s mission of making loans transparent, fair, and affordable, the company set out to modernize its data platform further. It sought a scalable orchestration solution that could enhance reliability, introduce professional development workflows, and allow engineers to focus on building value instead of firefighting.

### **The solution**

The team faced a practical challenge: they needed to replace their aging Java-based code generator, and wanted to modernize the platform further while keeping the benefits of automated Data Vault code generation. Initially, they [looked at Airflow](https://dagster.io/blog/dagster-airflow), but during their research, Alex discovered Dagster and reached out for a proof of concept (POC). The results were immediately compelling. “The mindset shift, organizing work around actual [data assets rather than tasks](https://dagster.io/blog/software-defined-assets), was definitely like a game changer for us,” Alex explains. Dagster’s asset-centric architecture and superior CI/CD capabilities addressed smava’s data development challenges that, he says, “would have been quite a big pain with Airflow.”

The implementation strategy was pragmatic and phased. smava started by migrating their legacy workloads to Dagster to establish a stable foundation, then built their crown jewel: a custom dbt code generator that creates Data Vault models from simple YAML configurations. This system generates over 1,000 dbt models automatically, eliminating manual coding while maintaining enterprise-grade quality checks and tests. The result is generated code that integrates seamlessly with Dagster’s asset catalog, providing full lineage visibility and orchestration capabilities.

Example YAML configuration for data exports:

```yaml theme=classic
source_system: some_system
source_type: mysql
import_asset: source_table
description: source table import from database
schema: source_db
table_name: source_table
export_format: csv
export_chunk_size: 10000
columns:
  - expression: source_table.record_id
    alias: record_id
    type: int
  - expression: related_table.reference_id
    alias: reference_id
    type: int
  - expression: source_table.status
    alias: status
    type: varchar(255)
  - expression: source_table.category
    alias: category
    type: varchar(255)
  - expression: source_table.created_at
    alias: created_at
    type: timestamp
  - expression: source_table.updated_at
    alias: updated_at
    type: timestamp
incremental_columns:
  - source_table.updated_at
query_overrides: >
  select $columns
  from schema_name.table_name as source_table
  left join schema_name.related_table as related_table
  on source_table.reference_key = related_table.id
```

Example YAML configuration for Data Vault (DV) objects:

```yaml theme=classic
hub_name: some_hub
description: Some hub
staging_materialization: view	# optional
incremental_strategy: append	# optional
merge_update_columns: "[list of columns]"	# optional
merge_exclude_columns: "[list of columns]"	# optional
enforce_contracts: true
tests_filter: some filter	# optional
business_key_definitions:
  - bk_id: bk1
    order: 1
    columns:
      - column_name: hub_1_bk_col_1
        column_type: int
      - column_name: hub_1_bk_col_2
        column_type: int
    imports:
      - import_name: import_1
        source_system: source_system_1
        columns:
          - expression: hub_1_bk_col_1_expr
          - expression: hub_1_bk_col_2_expr
      - import_name: another_import
        source_system: some_source_system
        columns:
          - expression: expr_1
          - expression: expr_2
  - bk_id: bk2
    order: 2
    columns:
      - column_name: hub_1_bk_col_3
        column_type: varchar(50)
    imports:
      - import_name: import_name
        source_system: some_source_system
        columns:
          - expression: some_expression
```

Today, smava’s technical architecture runs on AWS Fargate with a hybrid setup connecting multiple source systems, with different technologies, through Dagster and Python. The platform integrates dbt Cloud and dbt Core, with all generated models and their intensive contracts imported as Dagster asset checks. It combines the speed and reliability of automated code generation with the observability, CI/CD workflows, version control, and collaborative development practices that now enable smava’s data team to focus on building value, accelerating innovation, and scaling confidently alongside the business.

![](https://dagster.io/webflow-assets/c69f1cd02645e68f7e45.png)

### **The results**

Rebuilding around Dagster’s data-centric orchestration fundamentally changed how smava’s data team works. Most striking was the complete elimination of the alert fatigue that had been crushing team morale: with Dagster’s real-time visibility into pipeline health, Alex says, “Alerts directly went to zero.” In fact, he notes, the platform has had zero downtime since July 2025, a stark contrast to their previous frequent outages.

![](https://dagster.io/webflow-assets/0db4f0f77a6adcc1006b.png)

The developer experience improvements were equally transformative. New team members can now get up and running in just 15 minutes with proper local development environments, compared to the several days of training required for their previous tools. “You just spend maybe 15 minutes to set up your Dagster, your other dependencies and plugins, to VS Code, and now they’re actually liking it,” Alex explains.

Professional development practices like CI/CD, pull requests, code reviews, and branch deployments became standard, replacing the black-box debugging of their legacy systems. The end-to-end lineage and column-level lineage capabilities have proven essential for their Data Vault architecture, allowing analysts, analytics engineers, and data scientists to trace data origins and transformations across multiple layers. The unified data catalog provides read-only access to stakeholders who can now see “what’s happening, what’s running, what’s failed” without requiring technical expertise.

In addition to operational improvements, the platform reduced infrastructure overhead by consolidating legacy instances and ECS clusters. Alex highlights an even greater benefit: eliminating manual maintenance tasks saved significant “invisible costs,” freeing the team to focus on delivering business value. Most importantly, smava can now confidently assure executives that their infrastructure can scale with growth targets, providing a stable foundation to support continued innovation.

### **Looking ahead**

The success of the data platform team with Dagster has attracted attention across smava, highlighting the potential to extend its use to other teams. The team is exploring how smava can benefit further from Dagster for groups such as the data science team, as well as teams with existing Airflow implementations. Alex notes, “We are aware that Dagster is actually a perfect fit for machine learning workflows too,” and this awareness is guiding how the team evaluates opportunities to expand Dagster’s use across other complex data pipelines.

smava is now ready to scale in every sense of the word. Their automated code generation system can easily accommodate new data sources and evolving business requirements without manual coding effort, while Dagster’s asset-centric approach provides the flexibility to adapt their Data Vault architecture as needs grow. With zero maintenance overhead and executive confidence in the platform’s scalability, Alex says they now have a system that can confidently handle “x%, y%, whatever growth we are talking about, we are ready.”

### **Key takeaways**

**Zero downtime achievement:** smava went from frequent system outages to zero downtime since July 2025, demonstrating how modern data orchestration can deliver enterprise-grade reliability in regulated financial services.

**Maintenance overhead elimination:** The team reduced maintenance work to “almost zero” by eliminating daily alert fatigue and reactive firefighting, freeing engineers to focus on strategic data initiatives instead of operational fixes.

**Massive scale through automation:** Their YAML-driven code generation system now produces over 1,000 dbt models automatically, ensuring enterprise-grade data quality without manual coding, at a level of sophistication that would be difficult to achieve by hand.

**15-minute developer onboarding:** New team members can set up complete local development environments in just 15 minutes, compared to weeks of training with legacy tools, dramatically boosting productivity and knowledge transfer.

**Executive confidence achieved:** The platform transformation allowed Alex and his team to confidently assure smava’s C-level leaders that their data infrastructure can support ambitious growth targets, turning data capabilities from a potential constraint into a strategic advantage for the business.

---

## Zero Incidents, Maximum Velocity: How HIVED achieved 99.9% pipeline reliability with Dagster

> Learn how UK logistics company HIVED achieved 99.9% pipeline reliability with zero data incidents over 3 years using Dagster, while reducing turnaround time for new data products to just 3-7 days.

Source: https://dagster.io/customers/hived-case-study
Published: 2025-11-18

**KEY RESULTS:**

- **99.9%+ pipeline reliability:** Zero data incidents over 3 years supporting millions of deliveries
- **3-7 Day Turnaround:** New data products and analytics requests delivered within a week for rapid response to business needs
- **Fully automated pipelines:** Used Dagster to eliminate manual processes, transforming legacy processes into fully automated pipelines with dependency management and failure monitoring
- **Self-service capabilities**: Analytics engineers can independently add new data sources through YAML configurations.

### **The challenge**

When [Sebastián Arriagada](https://www.linkedin.com/in/sebastianarriagadap/) joined [HIVED](https://www.hived.space/) three years ago, the UK logistics company had built a pragmatic data solution that worked well for its stage. The team leveraged reliable ingestion tools, such as Fivetran, alongside other legacy processes for BigQuery ingestion. Scheduled BigQuery queries handled their core analytics needs through straightforward cron jobs. This approach had served them well as they established their data foundation and built initial reporting capabilities.

However, as HIVED scaled rapidly and operational decisions became increasingly dependent on dashboard insights, the limitations of their current approach became apparent. The system lacked dependency management between processes, meaning failures could cascade without clear visibility into downstream impact. 

For Sebastián, the challenge wasn't that their existing system was wrong, but that HIVED had outgrown it. As an analytics-heavy organization where operational decisions depend on reliable data, they needed the visibility into data lineage, proactive failure handling, and dependency management that their current architecture couldn't provide. The question wasn't whether to replace what they had built, but how to evolve it to match their current operational needs.

The company needed modern data orchestration to establish dependency management, failure monitoring, and end-to-end visibility across its data pipelines. As a single-person data engineering team supporting a team of analytics engineers and growing operational demands, Sebastián required tools that were reliable, easy to maintain, and could scale without constant intervention, moving from reactive firefighting to proactive platform management that would keep HIVED delivering for its customers, flawlessly and on time.

### **The solution**

Sebastián had used Airflow previously and found that Airflow’s limited local development and testing capabilities made building data pipelines slow and cumbersome. Dagster's developer-centered approach, including local testing capabilities, built-in monitoring, and reliable scheduling, made it the clear choice for a lean team that needed tools requiring minimal maintenance overhead.

The implementation centered on creating a unified view of data operations with native dependency management and failure monitoring. HIVED built their platform using open-source Dagster deployed on Kubernetes, establishing automated pipelines with proper lineage tracking and Slack notifications for failures. With the goal of replacing reactive maintenance with proactive development, Sebastián developed an abstraction layer that allows analytics engineers to define new data ingestions through YAML configurations, enabling self-service capabilities without requiring constant data engineering intervention. 

HIVED ultimately used Dagster as the orchestration layer for a [unified data stack](https://www.hived.space/blog/building-a-robust-data-stack-for-scalable-analytics) that includes Airbyte for ingestion, dbt for transformations, and BigQuery as their warehouse. This integrated platform granted immediate visibility into data lineage and impact analysis through Dagster's UI, eliminating the blind spots that plagued their previous cron-based approach. Now, when failures occur, the team can instantly see which downstream models and dashboards are affected, enabling proactive communication with their data end users — and earning back their trust. This architectural foundation positioned HIVED's analytics capabilities alongside their expanding logistics network across the UK.

“One of the main things I really like about Dagster is how local development and testing were taken into account from the very beginning...How you just take your schedule and your assets and do all that work locally, then spin up Dagster and test the whole thing very easily."

*Because I'm a one-man data platform team, I need tools that are easy to set up, easy to maintain in general, and that don't require me every day to go fix this and that. From both a developer experience and reliability perspective, it was an easy choice really to go with Dagster.* 

### **The results**

HIVED has achieved remarkable reliability over three years of Dagster implementation, with Sebastián noting, "I don't think we have had any incident with Dagster in these three years. Really." This 99.9%+ pipeline reliability supports millions of deliveries without data incidents and has fundamentally transformed how HIVED’s business users interact with analytics and even the most experienced new employees are consistently surprised about the quality and reliability of data at HIVED.

Their new Dagster-centered data platform also unlocked dramatic improvements in team velocity and responsiveness to data end-user requests and business needs. New data products and analytics requests now have a turnaround time of just three to seven days, depending on complexity, compared to the previous firefighting mode, where infrastructure maintenance consumed most engineering cycles. HIVED’s analytics engineers work full-time on new dashboards and business intelligence rather than troubleshooting broken pipelines, so they can make critical business decisions around HIVED’s operational expansion across the UK market.

Operationally, the unified orchestration platform enables sophisticated analytics that power business growth, including location expansion analysis using integrated mapping tools and delivery performance optimization. To democratize data access, Sebastián built a self-service YAML-based ingestion system that allows analytics engineers to independently add new data sources through pull requests, removing bottlenecks (and lightening his own load) while maintaining governance and Data engineering best practices. This infrastructure efficiency, combined with open-source tooling deployed on Kubernetes, keeps costs lean while powering a logistics operation that relies heavily on real-time operational metrics for route optimization and performance measurement.

### **Looking ahead**

HIVED’s immediate roadmap focuses on modernizing their remaining legacy processes and expanding platform capabilities. Sebastián plans to migrate older ops-based pipelines to Dagster's asset-centric approach to leverage enhanced UI features and improved visibility. The team is also implementing Dagster’s Looker integration to complete their end-to-end data lineage visibility, providing a comprehensive view from ingestion through business intelligence consumption.

HIVED is looking to move existing ML and AI workflows onto a unified (orchestration) platform to support its growing Data Science and AI team.  This will include ongoing training and deploying models for route optimization, delivery time predictions, and operational efficiency improvements. Sebastián says HIVED’s Dagster-centered data infrastructure allows the organization to seamlessly integrate ML workflows alongside its existing analytics pipelines. 

Sebastián’s advice to other high-growth companies in the logistics and operations sectors: think orchestration first. "I feel like the orchestrator is such a core piece in the whole data stack, and that is where you should start,” he explains. “It also influences the decisions around the other tools that you're going to use." As HIVED continues expanding their UK coverage, the scalable platform architecture ensures they can support growing operational complexity without proportional increases in data engineering overhead, maintaining their lean infrastructure approach while giving their end users trustworthy data for the sophisticated analytics HIVED needs to drive the company’s growth.

### **Key takeaways**

- **99.9%+ pipeline reliability**: Zero data incidents over 3 years supporting millions of deliveries, compared to frequent failures with the previous cron-based approach
- **3-7 day turnaround**: New data products and analytics requests delivered within a week, enabling rapid response to business needs
- **Fully automated pipelines:** Used Dagster to eliminate manual processes, transforming early-days paid ingestion solutions or legacy processes to fully automated pipelines with dependency management and failure monitoring
- **Team velocity transformation**: Shifted from firefighting mode to building new data products, with all analytics engineers working full-time on business intelligence rather than troubleshooting
- **Self-service capabilities**: Analytics engineers can independently add new data sources through YAML configurations and pull requests, removing data engineering bottlenecks
- **Cost-effective infrastructure**: Lean open-source stack on Kubernetes supports millions of deliveries while maintaining minimal operational overhead for a single-person data engineering team

---

## Small Market, Big Data: Tampa Bay Rays' Analytics Advantage with Modern Orchestration

> How the Tampa Bay Rays process game data 50-70% faster with Dagster, delivering insights in 15 minutes instead of waiting for 9 a.m. batch jobs. Modern Moneyball analytics.

Source: https://dagster.io/customers/tampa-bay-rays-case-study
Published: 2025-11-06

### **The challenge**

Just like the Oakland Athletics in Moneyball transformed baseball by finding hidden value in overlooked statistics, the [Tampa Bay Rays](https://www.mlb.com/rays) have built their competitive advantage on superior data analytics. "I always tell everybody, they're like what do you do? And I ask, have you seen the movie Moneyball? Oh yeah. I do that. That's usually the easiest way to explain it," says [Brian Plexico](https://www.linkedin.com/in/brianplexico/), Vice President of Baseball Systems.

The Rays, known for their strategic approach to resource allocation, have built their reputation on analytics-driven excellence. "Our edge is trying to get more from less," Plexico explains. "And we feel that analytics are one opportunity to do that."

But the scale of modern baseball data has exploded beyond what their legacy system could handle. "Back when I first started, we'd get 75 CSVs a day from Major League Baseball. That was the extent of it," Plexico recalls. "Now with the player tracking, ball tracking, and other sources, the data has exploded over the last 10 years or so." Today, every professional baseball game generates millions of data points: Statcast tracking systems capture player positions as coordinates, bat speed, ball trajectory, and even data from both ends of the bat to determine swing path and velocity.

Their previous system couldn't keep pace with these demands, creating mounting challenges:

- **Schedule dependency hell:** Rigid timing meant one delayed API response could cascade into hours of failures
- **Data source chaos:** 20+ providers with different formats, from MLB's Statcast to proprietary tracking systems
- **Debugging nightmares:** "With our current system, there's a lot more digging... you're bouncing around a lot more"
- **Real-time expectations:** Stakeholders need insights as soon as possible after games end, not after morning batch processes

The team needed a complete rethinking of how they processed, validated, and delivered data to decision-makers.

**KEY RESULTS:**

- **50-70% faster pipeline execution:** Processing reduced from hours to minutes
- **15-minute data availability:** Game data ready within 10 - 15 minutes vs 9 a.m. batch jobs
- **One-week onboarding:** New data sources integrated 2-3x faster than the previous system
- **Zero-touch reliability:** Critical pipelines run unattended nightly without intervention

### **Why the Tampa Bay Rays chose Dagster**

After six months of evaluating "many different platforms" and another six months testing Dagster's open source version, Plexico and his team made a decisive choice. While "many other teams in baseball use orchestration tools that are not Dagster," the Rays deliberately went a different direction. Several key factors convinced them that Dagster was the right fit for their unique challenges.

#### **Asset-centric thinking aligned with baseball data** 

"Most orchestration tools are just A, B, C, D, and there was no concept of an asset in the same way that there is in Dagster," Plexico explains. "It was a huge selling point to me that once a game ends, I can pull the data for that one game because I've got it partitioned by a game ID." This [asset-based approach](https://dagster.io/blog/software-defined-assets) naturally maps to how the Rays think about their data: games, players, innings, pitches, not abstract tasks.

#### **Developer-first philosophy**

Coming from a Ruby on Rails background, Plexico appreciated Dagster's opinionated yet flexible approach. "I'm a developer by trade first, more than a data engineer. I brought a lot of my software experience into this project, and Dagster fit in that realm a lot better for me... it takes a lot of choices away from you. You just do it the Rails way, and Dagster has a lot of similarities."

#### **Integrated debugging and observability**

The team needed to quickly identify and resolve failures without extensive log investigation. "With Dagster, it's all right there in the logs because of the way the assets are laid out. It's much more obvious given the asset graph and run logs where things might have gone wrong. It's just a lot faster to get to the debugging part."

#### **Cost-effective cloud solution**

The Rays evaluated Dagster+ as equivalent to hiring another data engineer. "For us, it came down to what benefits Dagster+ provided us and it was a no-brainer for the cost," especially crucial for a team that prides itself on doing more with less.

#### **Community and support ecosystem**

Beyond the technology, the active community sealed the deal. Plexico became one of the first to complete Dagster University and is a frequent user of the Slack community's AI bot. "The customer support is a huge benefit for us."

### **The implementation**

The Rays took a pragmatic approach to migration, implementing what they called a "one-to-one replacement" strategy. Rather than attempting a risky wholesale migration, they rebuilt critical pipelines in Dagster one at a time while maintaining operations on their legacy system. "The things we've changed, they haven't even noticed, which is awesome. It just works," Plexico notes. This strategy allowed them to prove value incrementally while ensuring zero disruption to the organization’s daily analytics needs.

Their new architecture leverages Dagster's sensor capabilities to create truly event-driven pipelines. When a game ends, sensors detect the completion and immediately trigger data ingestion rather than waiting for scheduled batch jobs. "An East Coast game goes final at 10:30 p.m., the game's marked completed by 10:55. We use a sensor and determine that the game is completed, and at that point, we can start kicking everything off."

The team integrated several key tools within their Dagster ecosystem:

- [**Sling integration**](https://docs.dagster.io/integrations/libraries/sling) for data movement, which Plexico calls his "favorite" feature for wrapping their existing BCP processes
- [**Slack integration**](https://docs.dagster.io/integrations/libraries/slack) for alerts and notifications, providing one-click access to pipeline debugging
- **SQL Server integration** maintaining their existing data warehouse while modernizing orchestration

A typical game pipeline processes multiple data layers: box scores and lineups arrive first, followed by play-by-play data, then granular pitch-level analytics, including Statcast tracking data that provides coordinate-level player positions and ball physics. "We get all the data and split it out because it's used for different things. We've got people that are interested in pitching, we've got people that are interested in hitting, plenty of other things we're interested in."

### **The results**

The Rays transformed how a small-market team can compete through data. "Even 50% would probably be low-balling it," Plexico notes when describing their performance improvements.

Their game-changing approach centers on Dagster's asset-based orchestration, transforming how they think about baseball data:

- **Event-driven intelligence replaces rigid schedules:** "Instead of waiting till 9 a.m. to kick off this job to get every game that got played the night before, I can say, oh, this one game ended, gimme that game." Using Dagster sensors, the Rays now process individual games within 10-15 minutes of completion.
- **From hours to minutes:** West Coast games that end at 1 a.m. Eastern are fully processed and ready for analysis by 1:15 a.m., not 9 a.m. Stakeholders wake up to fresh insights, not stale data.
- **Reliability that just works:** "Something as simple as the current standings in the morning after the previous night’s games: We've written a pipeline. We drop it in. And we basically have to do nothing more than parse the JSON and it goes into the system." The result? No more frantic morning calls about missing wins in the standings.

The business impact has been substantial. With faster, more reliable data pipelines, the analytics team can focus on developing new insights rather than maintaining infrastructure. "We can move on to other things. It just runs. And that's what we were hoping for when we signed up," Plexico explains. This operational efficiency directly supports the team's ability to make data-driven decisions from the front office to the dugout, critical advantages for a team competing against opponents with significantly larger budgets.

### **Looking ahead**

The Rays have ambitious plans for expanding their Dagster implementation over the next year. "I'm excited about asset checks," Plexico explains. "The idea of catching some of these data issues as they come in is gonna be huge for us."

Their immediate priorities include:

- [**Intelligent orchestration**](https://docs.dagster.io/guides/automate/declarative-automation) **over rigid scheduling:**\
  Moving from time-based triggers to pure event-driven workflows where "one asset triggers the next," enabling machine learning models to run the moment fresh data arrives rather than waiting for scheduled windows.
- **Self-service analytics democratization:**\
  Opening the Dagster UI to the broader organization to answer the two questions that plague every data team: "Has this run yet?" and "Where does this data come from?" As Plexico notes, "The ability for everybody to just go in and see the live view\... I think the power of that is unmatched."
- [**Branch deployments**](https://docs.dagster.io/deployment/dagster-plus/deploying-code/branch-deployments#supported-platforms) **for safer iteration:**\
  As more team members contribute code, branch deployments will enable testing without risking production pipelines that process data for crucial morning meetings.
- **Ongoing** [**Asset checks**](https://docs.dagster.io/guides/test/asset-checks) **for data quality:**\
  Implementing automated validation to catch issues from unreliable third-party sources before bad data reaches production systems, particularly important given the variable quality of external data feeds.

### **Key takeaways**

- **Cost-effective scaling achieved.** "What Dagster+ provided us was a no-brainer for the cost", equivalent to hiring another data engineer but delivering far more value
- **Developer velocity unleashed.** New data sources onboarded in one week versus "two or three times as long" with the legacy system
- **Team adoption without friction.** Four data engineers are actively developing, with non-pure developers successfully contributing thanks to Dagster's intuitive Python approach
- **Moneyball 2.0 enabled.** Processing everything from basic box scores to Statcast tracking data, enabling data-driven decisions that help a small-market team compete with larger organizations
- **Sleep-friendly operations.** "We don't have to think about it every night... that's probably been the highlight for me so far."
- **Community-powered success.** Heavy use of Dagster's Slack community and AI assistant, with Plexico being one of the first to complete Dagster University

---

## PostHog Powers Customer-Facing Web Analytics with Dagster

> Learn how PostHog's engineering team used Dagster to eliminate weekend firefighting, improve dashboard performance for enterprise customers, and reduce feature shipping time by 80%.

Source: https://dagster.io/customers/posthog-case-study
Published: 2025-10-22

**KEY RESULTS:**

- **No more weekend firefighting:** Eliminated manual backfill monitoring and pod restarts that kept engineers working weekends on customer-facing features.
- **Faster incident response:** Cut troubleshooting time from days of debugging to hours with built-in observability and Slack-to-run linking.
- **Enterprise-scale performance:** Enabled Web Analytics dashboards to serve billions of monthly events to largest customers without timeouts or failures.
- **Organic team adoption:** Grew from 6 to 20 engineers using Dagster across multiple product teams in just 3 months.
- **Rapid feature delivery:** Reduced typical feature development cycles from 1-2 days down to same-day or next-day shipping.

### **The Challenge**

[PostHog](https://posthog.com/) builds developer tools that help product engineers ship successful products. Their Web Analytics feature offers a streamlined dashboard with essential metrics like visitors, views, sessions, bounce rate, and conversions. It's designed for early-stage founders, engineers, and technical marketers who need something more focused than full product analytics.

But there was a critical problem. PostHog’s customers were ingesting billions of events per month, and their Web Analytics dashboards were failing under the load. Queries were timing out, and dashboards were returning errors instead of insights. PostHog's customers were feeling the pain.

"Very large customers, which operate at billion-scale monthly events ingestion, they just couldn't use their web analytics dashboard," explained [Lucas Ricoy](https://www.linkedin.com/in/lricoy/), an engineer on the Web Analytics team at PostHog. "Slow or failed queries were a major pain point and directly affected users' experience on Web Analytics."

This wasn't some backend quirk the engineering team could ignore. Paying customers couldn't access a product they were paying for.

The team had built their data processing workflows using Django management commands and lightweight orchestration. This worked fine at a smaller scale, but as PostHog's customers grew, the infrastructure couldn't keep up.

Backfills were particularly brutal. Lucas's manager, [Robbie](https://www.linkedin.com/in/robbiecoomber/), would kick off a backfill job over the weekend and then spend his time monitoring a Kubernetes pod to make sure it didn't crash. If the pod died, the entire backfill died with it. There was no retry logic, no state management, just manual restarts and crossed fingers.

"We basically had to keep the state in our heads while running them," Lucas said. "We could have improved them, but for the level of complexity, they were just not enough anymore."

The team had other options for orchestration. They could use Celery for task scheduling or Temporal for workflow management. But neither was built for the kind of data-intensive processing that powers a product feature at scale. They needed something designed specifically for data pipelines, with partitioning, backfills, and asset-based orchestration built in.

The team knew they had to fix it. PostHog's products needed to continue to work at the scale it was promising customers.

### **The Solution**

Dagster had already been recommended internally at PostHog for data processing workflows. James Greenhill, who leads the ClickHouse team, had been advocating for it. When Lucas evaluated options for powering Web Analytics at scale, Dagster stood out immediately.

"We could've gone with Celery or Temporal for Web Analytics, but Dagster had built-in features for data processing that made it extra helpful for our use case: Partition and backfill definitions, the Kubernetes operator, retry capabilities, the UI for launching ad-hoc materializations," Lucas explained.

The team built pre-aggregation pipelines that power the customer-facing Web Analytics dashboards. Instead of querying billions of raw events on every page load, Dagster orchestrates pipelines that process the data into specialized materialized views. When customers load their dashboards, they're querying pre-computed aggregates that return in milliseconds instead of timing out.

The heavy computation happens in ClickHouse, but Dagster orchestrates the entire workflow. The backfill policies were transformative. Lucas could define partition strategies based on customer data volumes (hourly for trillion-row customers, daily or monthly for smaller workloads) and let Dagster handle execution. If something failed, Dagster would retry automatically. If a pod crashed, a new one would spin up and continue from where it left off.

"Being able to define a backfill partition and just leave Dagster running is great for us," Lucas said. "Right now it's just a matter of configuring the dialogue and  leaving it running."

The team set up Slack alerts for pipeline failures. When something breaks in a customer-facing feature, every second counts. Now they could click directly from Slack into the specific Dagster run, see the full stack trace, and understand what went wrong. The observability was miles ahead of their previous setup.

Testing was straightforward, too. Engineers could run pipelines locally with different configurations before deploying customer-facing changes. The Python-based approach meant they could use familiar tools and workflows to build product features, not just internal data pipelines.

### **The Results**

The impact on the customer experience was immediate and dramatic.

Those enterprise customers with billions of monthly events went from seeing timeout errors to loading their dashboards almost instantly. The pre-aggregation pipelines handled production scale without breaking a sweat. Even with the feature still not enabled for every customer as it is being evaluated, the impact was substantial.

"It's almost instant because of the work we have done there," Lucas said. "It's really using top-notch technologies to build this orchestration and keep it working without having to play whack-a-mole every day."

The weekend backfill monitoring disappeared completely. There were no more manual pod restarts for customer-facing features, no more keeping the state in people's heads, and the orchestration powering the product just worked.

Product development velocity improved significantly. The team could ship new Web Analytics features and fix customer issues faster because they weren't constantly firefighting infrastructure problems. Tasks that previously took 1-2 days could now be done in hours.

"There is no frustration anymore on shipping that stuff," Lucas noted. "It's good enough. It's great for us that we're not having pain points on building those things anymore."

When teams need to ship new features quickly, they can turn them around in days or even hours. "Right now, I think we are like a day or two. We can stitch stuff together to test it internally in maybe some hours," Lucas explained. For an organization shipping customer-facing features with that level of data complexity and scale, that's remarkable velocity.

The business impact extended beyond just the Web Analytics team. When PostHog moved from Dagster open source to Dagster+ about two months ago, they discovered features that improved their ability to ship reliable products. The insights dashboard gave visibility into pipeline performance and costs. The deployment management made it easier to work across environments when shipping features.

Most telling was the organic growth in adoption. When Lucas joined PostHog eight months ago, only about six people were using Dagster. Three months later, that number had tripled to 16-20 people across multiple teams. Other product teams started using Dagster to power their features: revenue analytics, experiments, error tracking, and the Max AI assistant.

"It saves us so much time to not have to build orchestrations ourselves, and it's way simpler to write a Python file than build all of the Airflow internal details to make it run," Lucas explained. "So it's like no pain point. It really just works."

The team built additional product capabilities on top of the core orchestration. They created asset checks that automatically validate data accuracy for customer-facing metrics by comparing raw and pre-aggregated results, with a goal of less than 1% variance. They integrated with the open exchange rates API to provide currency conversion data to customers. They set up sensors to track execution metrics.

Perhaps most importantly, the team shifted from reactive firefighting to proactive product development. Instead of spending 30-40% of their time debugging infrastructure issues affecting customers, they could focus on building features and improving the product experience.

"Our product dashboards are now both faster and more reliable, and the data platform requires less manual oversight," Lucas said.

### **Looking Ahead**

PostHog continues to expand its use of Dagster to power more product features across the organization. Teams are migrating existing Django management commands to Dagster-based workflows. The ClickHouse team uses Dagster heavily for operations that support multiple product features.

The experiments team is exploring moving more product workflows to Dagster. Lucas wants to expand the usage of asset checks for better data quality monitoring across customer-facing features.

"I would like to expand our usage of assets and asset checks for figuring out data quality," Lucas said. "For some things, I can see a lot of use cases we could use that for a better data quality product."

PostHog is also exploring AI-powered analytics features. They've started building Posthog AI, including an assistant that leverages their Dagster-orchestrated data layer to automate AI model evaluation comparisons.

As PostHog continues to scale and add more data-intensive product features, having reliable orchestration becomes even more critical. The foundation is solid, and the team is confident it can handle whatever comes next.

"Engineers appreciate the observability, local development ease, and clean abstractions," Lucas said. "It has replaced manual operations with reproducible, debuggable workflows."

### **Key Takeaways**

- **Dagster powers customer-facing product features**, not just internal analytics, enabling PostHog to serve enterprise customers at scale
- **Backfill policies eliminated manual monitoring** and made large-scale data processing manageable for production workloads
- **Product velocity improved dramatically:** Feature development time dropped from 1-2 days to hours
- **Reliability directly impacts customer experience:** Fast, accurate dashboards powered by Dagster improve user satisfaction
- **Developer experience enables product innovation:** Local testing and Python-based development accelerate shipping new features
- **Organic adoption signals product-market fit:** Teams chose Dagster because it solved real product engineering problems

---

## From "YAML Mess" to Unified Control: How Otto Transformed Their Data Platform with Dagster

> Learn how Otto transformed their data platform from fragmented tools to unified control with Dagster. 6 teams, improved stability, faster debugging, and better collaboration.

Source: https://dagster.io/customers/otto-case-study
Published: 2025-07-28

### **Key results:**

- 6 teams migrated from multiple legacy orchestrators to Dagster
- Improved data pipeline stability and monitoring
- Reduced time to identify and resolve failures
- Monolithic pipelines transformed into reusable, maintainable components
- Better “community feeling” and increased collaboration between teams

### Challenges When Operating at Scale

As one of Germany’s largest e-commerce companies, [Otto](https://www.ottogroup.com/en/) operates at massive scale. Their data teams are responsible for processing millions of transactions and managing complex forecasting models that predict everything from customer demand to the timing of warehouse inventory arrivals. As the company grew, however, Otto’s data pipeline infrastructure had evolved to be a patchwork of multiple teams across the organization, each with their own tools and approaches. 

Some of the company's data teams were using Argo Workflows, and found themselves trapped in endless configuration files that grew painfully cumbersome as their pipelines grew more sophisticated. "Argo is not written in a programming language — you use it by configuring YAMLs," explains Heiner Hippke, a senior machine learning specialist who leads Otto's forecasting team. "So we had these *huge* Argo YAMLs that comprised complete pipelines which we referred to as ‘YAML hell.’ Argo was just not flexible enough for our needs.”

Meanwhile, other teams using Apache Airflow (via Google Cloud Composer) faced different but equally challenging data pipeline issues. The infrastructure overhead was significant, and there was no local development or testing. "When there was a problem we had to spin up the production environment to see if it was even running or not, which took a lot of time," says [Christian Kalla](https://www.linkedin.com/in/christian-kalla-5303a568/), a platform engineer at Otto. The lack of local development capabilities meant slower iteration cycles and higher costs while teams wasted valuable time managing operational overhead and struggling to maintain complex data pipelines. These inefficiencies weren't just inconvenient for the technical teams, though — they were also  limiting the company’s ability to respond quickly to market changes.

The deciding factor came when Otto’s existing patchwork data architecture couldn’t handle the complex ML orchestration patterns that drive the company’s business-critical forecasting models, which are retrained every day. “The pain must be really huge to get everyone together to make the decision, *let's go for a new data pipeline tool*,” Heiner says. 

### **The Results**

Otto's migration to Dagster has transformed both technical capabilities and team dynamics across the company’s six different data teams. The shift from highly manual, configuration-heavy tools has delivered immediate productivity gains, thanks in no small part to local development and testing capabilities. "Debugging in case of failure is much easier now," says Heiner, who also praises Dagster’s easy integration with monitoring systems and Slack/Teams for issue alerting and Dagster’s Python-native development as another major improvement to the developer experience.  Ultimately, he says, Otto's teams can now iterate faster and resolve issues more quickly.

Perhaps even more importantly, Dagster’s data-centric architecture allows teams to center their work on actual data assets rather than tasks, breaking down silos between data engineers and data scientists at Otto. "Assets are the mind-changing abstraction,” says control systems engineer Tobias Krauss. “Two worlds together, stakeholders and engineers meet in the middle." Teams are now able to share and reuse assets across the organization while maintaining platform-wide visibility with Dagster’s unified catalog and lineage capabilities, resulting in, Tobias says, “Better community feeling and collaborative energy between teams”. 

The teams have also achieved significant architectural improvements, moving from monolithic pipeline configurations to modular, reusable components. Otto has built sophisticated “asset factories” for common patterns like data transfers from Oracle to Google Cloud Storage to BigQuery, dramatically reducing redundant development work across teams. 

### **Looking ahead**

Otto's success with Dagster has created momentum for broader organizational adoption. Currently, Dagster usage is concentrated within their business intelligence teams, but the platform engineering team sees significant opportunity to expand across the entire organization. 

The team is also focused on standardizing data pipelines organization-wide by making Dagster the default choice for all new data projects. "I'd love to reduce redundancy with Dagster, with more teams using shared asset factories and even reducing the need to transfer data oftentimes," says Heiner Hippke. With their foundational infrastructure now in place, Otto is positioned to scale their data platform efficiently while maintaining the developer-friendly experience that made their initial adoption successful.

### **Key takeaways**

- **Improved developer experience:** Eliminated "YAML Hell" with Python-native development, enabling local testing and faster debugging
- **Cross-functional collaboration:** Data scientists can now contribute directly to data pipelines alongside data engineers
- **Modular architecture:** Broke down monolithic pipelines into reusable, maintainable components using asset factories
- **Faster time to production:** Reduced development cycles through local testing capabilities and a unified control plane for data
- **Organizational scalability:** Successfully onboarded multiple teams with an embedded enablement approach, creating a foundation for company-wide adoption
- **Infrastructure standardization:** Built reusable Terraform modules and shared patterns that teams can adopt quickly
- **Increased pipeline reliability:** Improved monitoring and alerting capabilities with seamless integration to Teams/Slack notifications

---

## Digitizing an Analog Industry: How Ida Leverages Multi-Tenant Data Orchestration with Dagster

> How Ida built a multi-tenant Dagster architecture to process millions of rows daily, turning chaotic fresh produce data into AI predictions that prevent food waste.

Source: https://dagster.io/customers/ida-case-study
Published: 2025-07-21

### KEY RESULTS

- Processes millions of rows across hundreds of stores in under a few minutes daily
- 30-minute feature deployments from idea to production
- Zero downtime tolerance - stores receive predictions before opening every morning
- Preventing thousands of tons of food waste daily and optimizing customer operations for a 10x ROI

### The Challenge

[Ida](https://www.ida.eu/eng/ida-eng) is a proposal and order management software dedicated to fresh produce. Today, more than 150 million tons of fresh foods—fruits and vegetables, meat, fish, and poultry—are discarded every year. This startup is on a mission to eradicate food waste: Ida aims to revolutionize fresh produce supply chains by providing AI-powered demand forecasting and order management for perishable goods that expire in days.

![](https://dagster.io/webflow-assets/8c822a063a5fbc4a637d.png)

![](https://dagster.io/webflow-assets/74c7e0238e649b7aee54.jpg)

It’s a huge challenge because fresh produce ordering is still highly analogue: A manager walks through manually counting apples and scribbling order quantities on a clipboard. This scene plays out across thousands of Ida customer stores daily, generating data that's fragmented, inconsistent, and changes without warning: 

#### **The Data Chaos Reality**

"These aren't edge cases – they're Tuesday," explains [Louis Bertolotti](https://www.linkedin.com/in/louis-bertolotti-186b38154/), Ida's founding engineer. The company faces constant data chaos:

- **Carrefour's data looks nothing like Système U's data** - different column names, business logic, and edge cases
- **API formats change overnight without notification**, breaking existing integrations
- **Someone deciding that "KG" now means something different than it did yesterday**
- **Accidentally receiving last year's sales file**
- **A new product category appearing with completely different units of measurement**
- **Data quality varies dramatically** - from sophisticated APIs to manual CSV uploads

#### **Previous Architecture Pain Points**

Ida's platform team initially built a serverless, event-driven pipeline based on GCP and cloud run. "It was really cost-efficient for us, but very slow for the data pipeline itself," said [Mathieu Grosso](https://www.linkedin.com/in/mathieu-grosso-5a0750143/), an AI engineer who is also Ida's co-founder and CTO. "Every time we had a new client, we had to do another Lambda. We had to do it over and over again; it was really painful."

The team needed an architecture that could handle:

- Multi-tenant data with different formats from each retailer
- Extremely fast iteration (clients changing API formats overnight)
- Tight **6 AM deployment deadlines** when retailers called with urgent issues
- The need to **"fix and deploy before 8 AM"** when retailers have broken ordering processes

### **The Solution: Multi-Tenant Magic**

The Ida team rebuilt what they describe as "something that probably shouldn't work, according to conventional wisdom, but handles millions of rows across hundreds of stores in under a few minutes every morning." Their unconventional decisions are driven by the unique challenges of ingesting chaotic data from an analog market:

- **Multiple dbt projects in a single Dagster instance** (multi-tenant by design)
- **Running dbt in production** (instead of just for analytics)
- **ELT data transformation** of "chaotic data" from many sources using BigQuery
- **Branch deployments instead of a dedicated staging environment** for ELT

#### **The Architecture: Medallion with Asset-Aware Orchestration**

Their data flow follows a **medallion architecture** (Bronze → Silver → Gold), orchestrated through Dagster's asset-aware model:

**🥉 Bronze Layer: Embrace the Chaos** Data comes in through many different channels. Everything lands raw and unprocessed in BigQuery tables. No ETL magic—just ELT reality with BigQuery as their central data warehouse.

**🥈 Silver Layer: DBT as Production Middleware** This is where the magic happens. They use DBT not as an analytics tool, but as production middleware—what their team calls **"modeling clay for data"**—all running directly on BigQuery's compute engine.

- **Raw → Staging (STG):** Clean, standardize, and deduplicate. Column names get normalized, data types get fixed, obvious errors get flagged.
- **Staging → Dimensions & Facts:** Transform into their "Ida format":
  - **DIM tables:** Product catalogs, stores, suppliers (descriptive, time-independent)
  - **FACT tables:** Sales, orders, stock movements (events and measurements)

**🥇 Gold Layer: Production-Ready Data** Clean, tested data flows into Cloud SQL (OLTP database) and powers the mobile app that produce managers use every morning.

![](https://dagster.io/webflow-assets/e4a7df7ef8b5a19f7a6d.png)

#### **Multi-Tenant Project Structure**

```yaml theme=classic
harvest_flow/
├── dbt/
│   ├── cool-retailer/ 
│   │   ├── models/staging/stg_sales.sql
│   │   ├── models/transform/fact_sales.sql
│   │   └── schema.yml
│   └── fancy-retailer/
│       └── ...
└── dagster_etl/
    ├── cool-retailer/assets/load_gold.py
    └── fancy-retailer/assets/...
```

##### Dagster automatically converts:

`dbt/cool-retailer/models/staging/stg_sales.sql ` → Dagster asset `cool-retailer/dbt/stg_sales`

`dbt/cool-retailer/models/transform/fact_sales.sql` → Dagster asset `cool-retailer/dbt/fact_sales`

##### Automatic Dependency Resolution

```sql theme=classic
# dbt/cool-retailer/models/transform/fact_sales.sql 
SELECT ... FROM {{ ref('stg_sales') }} -- This ref() creates the dependency!
```

```python theme=classic
# In cool-retailer/assets/load_gold.py
@asset(deps=[AssetKey(["cool-retailer", "dbt", "fact_sales"])])
def load_sales_cool_retailer():
    # This Python asset depends on the DBT model above!
```

#### **Complete Execution Flow**

When Cool-Retailer sends new sales data, here's what Dagster automatically executes:

1. **Raw data ingestion**
2. **cool-retailer/dbt/stg\_sales** (cleans the raw data)
3. **cool-retailer/dbt/fact\_sales** (depends on stg\_sales)
4. **load\_sales\_cool\_retailer** (Python asset depending on DBT model)

**Why this order?** Dagster reads the {{ ref('stg\_sales') }} in the DBT files and automatically builds the dependency graph. **No manual scheduling—pure dependency-driven execution.**

#### The Results

#### **Performance Magic: BigQuery + DBT + Dagster**

**The challenge:** Cool-Retailer's daily processing requires handling hundreds of stores, millions of sales rows, and complex aggregations.

**The solution:** **BigQuery's columnar storage + partitioning + DBT's incremental models = linear scalability.** Full DBT runs complete in under a few minutes for hundreds of stores processing millions of sales rows daily.

#### **Real-World War Stories**

**The 30-Minute Feature** Last month, CoolRetailer told them about a new product flag indicating seasonal inventory rotation. In a traditional setup, this would be a multi-day engineering effort.

With their DBT setup? "I added a few lines to the dedicated dbt model, and let Dagster handle the execution of all the impacted assets. **Thirty minutes later, our models were categorizing seasonal products across hundreds of locations.** Two days later, they told us the logic missed winter items. **I updated it in 5 minutes.**"

This is the **"modeling clay" philosophy in action**—DBT lets them reshape data transformations as quickly as they can think of them.

#### **Safety Net: Production DBT Tests Against Data Chaos**

To handle the unexpected, **DBT tests are their lifeline.** With their ELT approach, they catch anomalies through comprehensive DBT testing that runs every single morning before any predictions go live:

From simple uniqueness tests:

```yaml theme=classic
# dbt/cool-retailer/models/schema.yml
- name: fact_sales
  columns:
    - name: id
      tests:
        - unique
        - not_null
```

```sql theme=classic
# dbt/cool-retailer/tests/dbt_test.sql
SELECT 
  ...
  CASE 
    WHEN condition_A 
    THEN 'Error message A'
    WHEN condition_B
    THEN 'Error message B'
  END AS issue
FROM {{ ref('stg_sales') }}
```

**Every morning, before any predictions run, Dagster executes hundreds of these tests across all retailer projects.** If cool-retailer/dbt/stg\_sales fails a uniqueness test, the entire downstream pipeline stops. No ML models get bad data, no store managers get broken recommendations.

#### **Multi-Tenant Magic Implementation**

The Dagster implementation that the team is particularly proud of, Grosso said, is their multi-tenant dbt setup where **"every dbt model automatically becomes a Dagster asset with full lineage tracking"** and the automatic dependency resolution from dbt ref() functions. They call it their **"multi-tenant magic"** – separate dbt projects for each food supplier, all orchestrated in a single Dagster instance.

This approach handles the reality that **different retailers have completely different data formats while ensuring identical outputs** for ML models and production applications. "Now we can model the data to always have the same internal schema with a medallion architecture. So we end up with clean tables, which are all really clean and tidy, and it was really thanks to the Dagster integration."

**Different inputs, identical outputs. That's the multi-tenant magic.**

#### **Branch Deployment Success**

“The really interesting part for us about Dagster’s branch deployment is the fact that we can connect it to our agent. Because we're on GCP, we have a Kubernetes agent in place to do all of the processing, which is vastly easier for us.“

### Looking ahead

Ida's success with Dagster has positioned them for aggressive expansion across European fresh produce markets. The team is currently adapting their architecture for a major new retailer that will send sales data in real time — transforming their batch-oriented approach into rapid micro-batches while maintaining Dagster's asset-aware dependency model.

"We're implementing comprehensive Dagster asset checks beyond our dbt tests," explained Bertolotti. "When a micro-batch receives anomalous data, Dagster can automatically pause downstream processing and alert our team."

The company is also building **metadata-driven anomaly detection using Dagster's rich metadata system** to track data volumes, processing times, and business metrics. This system automatically flags when patterns break in the now-digitized world of fresh produce data.

They're redesigning their asset interface around **Dagster's partitioning system for store-level partitions** for parallel processing across hundreds of locations.

### Key takeaways

- **Zero downtime tolerance achieved:** Store managers now receive ML-powered demand predictions every morning instead of relying on manual clipboard counting, enabling data-driven ordering decisions.
- **Comprehensive data anomaly testing:** Use Pandera and table integrations in Dagster to ensure that every received dataset is okay, then use dbt tests as asset checks to catch data anomalies before predictions go live.
- **Immediate failure tracing:** Asset lineage shows exactly which assets failed and which downstream systems were affected. Rollback is quick because Dagster tracks asset versions.
- **Linear Scalability:** The combination of BigQuery's columnar storage, dbt's incremental models, and Dagster's orchestration allows full DBT to complete in minutes for hundreds of stores processing millions of sales rows daily.
- **Development velocity:** DBT becomes "modeling clay for data" - rapid reshaping of transformations allows 30-minute feature deployments from idea to production
- **Saved from the garbage:** Ida helps prevent thousands of tons of food from being wasted every day with AI demand predictions for fresh products that expire quickly

---

## How Clippd Built Organization-Wide Data Visibility and Saved 8+ Hours Per Week

> Sports analytics platform Clippd automated daily data operations, eliminated 8+ hours of weekly manual work, and democratized data access across teams.

Source: https://dagster.io/customers/clippd-case-study
Published: 2025-07-08

[Clippd](https://www.clippd.com/) is a unique golf performance tracking platform that uses artificial intelligence, machine learning, and advanced data science to create a golfer's "unique golfing DNA." By analyzing patterns across all activities, Clippd contextualizes performance based on factors like weather conditions, course difficulty, and historical performance for individual golfers and 200+ college golf programs. Unfortunately, Chief Data Officer [Chris Robertson](https://www.linkedin.com/in/christopher-robertson-02746248/) and his team had inherited an expensive, inflexible, and (worst of all) highly manual data system that was impeding the company’s ability to keep up with growing customer demand for Clippd’s data-intensive services.

### Key Results

- **Data availability delays:** From hours -> zero
- **Manual pipeline labor:** 8+ hours - a full work day - each week -> zero 
- **Rapid scale**: From frozen growth -> comprehensive ELT pipeline supporting analytics for 200+ college golf programs and multiple downstream use cases
- **Democratized data access**: Black-box system -> accessible data with organization-wide visibility

### Strangled by manual bottlenecks

> *Someone had to go in manually, start up the instance, do all the processing in the morning, which meant that you don't have data available for the first few hours of the day.*

Beyond a forced wait for fresh data, this daily routine was also costing Clippd’s technical team approximately 8 hours per week in manual data operations, eventually driving them to a critical inflection point. Users across the company demanded data for insights that could grow the business, but legacy infrastructure blocked Clippd from becoming the data-centric organization it needed to be.

The company had inherited a setup from early consulting work, along with significant operational friction that threatened Clippd’s ability to scale. The problems were multifaceted and interconnected:

- **Manual daily operations**: Every morning, someone had to manually start up instances and process data, creating hours of delay before fresh data was available.
- **Limited accessibility**: Only one or two people could handle data operations, creating bottlenecks that prevented broader organizational engagement with data.
- **Lack of native integrations**: Chris says it could be hard to get ingestion working correctly because the inherited data stack lacked proper integrations for their other tooling: “We had to fudge some things a little bit to get it to work.”
- **Scalability blockers:** Processing larger datasets required creative workarounds on single machines, hitting walls that threatened their ability to support their growing customer base.
- **Expensive licensing model**: Due to the cost structure of the inherited toolset cost structure, access was restricted to a small number of users. “The full team didn't really have access. They were using the data but didn't really know what was going on under the hood in the data pipeline,” explains Chris. “We were living with quite low observability over the pipelines and processing.”
- **Black-box operations**: The lack of visibility meant most team members couldn't understand or troubleshoot data processes, slowing things down even further.

> We needed to move to a more robust system – something a bit more grown-up,” Chris says. But the team realized they needed more than just a technical upgrade. They needed to build a data system platform that could transform Clippd into a truly data-driven organization.

### Why Clippd chose Dagster

Chris quickly settled on dbt as Clippd’s transformation layer (but ruled out dbt Cloud because there was no integration with ClickHouse, Clippd’s analytics database). After that, he says, “It was basically, how do we decide on the orchestration layer and the ingestion parts of this ELT pipeline?”

After evaluating multiple orchestration options, including Airflow and Prefect, Clippd chose Dagster for its data-centric approach that aligned perfectly with their analytics-focused use case. “I wasn’t unhappy with the other options, but it was clear that Dagster just fit our use case much better,” Chris says. The decision came down to several key factors:

- **Data-centric orchestration**: Unlike task-focused tools, Dagster's asset-based approach matched how Clippd thought about its analytics data pipelines.
- **Native dbt integration**: Dagster's ability to work at the model level rather than packaging everything into large dbt jobs gave them the granular control they needed.
- **Modern developer experience**: The platform offered clean development workflows and local testing capabilities that the team valued.
- **Unified control plane**: The ability to provide comprehensive visibility across their entire data ecosystem was essential for breaking down the black box pipelines that the team suffered under their inherited toolset setup.
- **Scalability without complexity**: Dagster could grow with their needs while still maintaining simplicity for their lean team.

Dagster’s native dbt support was particularly compelling. "The native dbt support, where you can go down to the model level, was really big for us,” Chris explains, “Because with Airflow you have to package things up into big dbt jobs to run as Airflow DAGs. That wasn't really something we wanted to do."

The team appreciated that Dagster could serve as both an immediate solution to their analytics pipeline needs and a foundation for more sophisticated use cases in the future.

### Building a modern ELT foundation with Dagster

Clippd implemented a comprehensive modern data stack with Dagster as the orchestration layer at its core. The architecture brought together best-in-class tools while maintaining the simplicity and reliability the team needed:

- **Ingestion layer**: irbyte for connecting to multiple sources ranging from processed golf performance data, product analytics platforms, accounting systems, and other enterprise data sources to consolidate information into our ClickHouse analytics cluster.
- **Orchestration**: Dagster manages the entire pipeline lifecycle with comprehensive observability.
- **Transformation**: dbt models integrated natively with Dagster for granular control and monitoring.
- **Storage**: ClickHouse analytics database optimized for their analytical workloads.
- **Visualization**: Tableau and Metabase for downstream analytics and reporting.

The migration strategy was both swift and thorough. Rather than attempting a big-bang replacement, the team ran both systems in parallel for a few weeks while focusing on getting their essential reporting requirements operational in Dagster as quickly as possible. This approach allowed them to maintain business continuity while building confidence in the new platform.

Once the core functionality was stable, they continued expanding their capabilities far beyond what they had previously achieved. The team took advantage of Dagster's flexibility to implement more sophisticated monitoring, alerting, and data quality checks that simply were not possible in their previous setup.

Ultimately, Clippd’s new data stack leveraged Dagster's modern architecture principles. It features automated scheduling, comprehensive logging, and Slack-integrated alerting that keep the team informed of pipeline status without overwhelming them with noise.

> *Even though I'm not hands-on every day, the fact that I can dip into the Dagster UI to see what's going on, see how things look, and then if I want to look into something, I can just get into the code base as well — that’s really massive. No more data-goes-in-and-comes-back-out black box.*

### Transforming your data *and* your organization at the same time

The transformation has fundamentally changed how Clippd operates as a data-driven organization. Beyond the immediate operational benefits, Dagster has enabled a cultural shift where data accessibility and reliability are no longer barriers to innovation.

**Operational excellence**

- **Eliminated manual operations**: Pipelines now run automatically at 4 AM, ensuring fresh data is always available when stakeholders log in first thing in the morning
- **Freed up significant time**: The team saves approximately 8+ hours per week previously spent on manual operations, equivalent to a full day of work
- **Improved reliability**: Automated monitoring and Slack alerts provide proactive issue detection instead of waiting for dashboards to break
- **Better resource allocation**: Time previously spent on manual operations is now invested in extracting insights and building data products

**Platform democratization**

- **Organization-wide visibility**: The entire team can now engage with their data platform, not just one or two specialists
- **End-to-end transparency**: Team members can examine the UI, understand pipeline status, and dive into the codebase when needed
- **Faster iteration cycles**: New models can be developed in dbt, integrated with Dagster, and deployed to production quickly
- **Increased stakeholder engagement**: The platform's reliability and the team's increased agility have driven adoption across the business

**Cultural transformation**

- **Data-driven decision making**: The organization has evolved from a company that happened to have data to one where data is central to operations and competitive strategy.
- **Positive feedback loops**: As stakeholders see how quickly the team can deliver new analytics, they become more invested in data-driven approaches.
- **Improved trust**: Consistent, reliable data has built confidence across the organization that insights are accurate and available when needed.

The platform has become a catalyst for broader organizational change, Chris says. “Now other areas of the business are actively seeking to lean on the data more for doing more analytics-driven approaches to their work.”

> *We're able to build new models in dbt, have them show up in Dagster, and move very quickly to actually deploying that in production.*

### Advanced analytics and machine learning

With their foundational data platform solidly established, Clippd is positioned to tackle more sophisticated use cases that were previously out of reach. The team plans to expand their medallion architecture with bronze, silver, and gold data layers to support advanced analytics and machine learning initiatives around customer lifetime value and churn modeling.

The reliable foundation Dagster provides gives them confidence to pursue these advanced capabilities. “I'm pretty comfortable with how we're able to report on the things that exist within the business,” Chris says. “Now we can lean into that maturity curve to think about how we explain what’s happening now, so we can start to predict what's going to happen in the future?"

The company is also exploring additional data quality implementations, potentially integrating tools like Great Expectations through Dagster's native support, and expanding their automated pipeline coverage to include more intermittent data science workloads that will support their ever-growing customer base of college golf programs and, of course, individual duffers everywhere.

---

## From scattered scripts to unified platform: How Dagster powers data decisions for 4.5 million Belgian citizens

> Belgian ministry FWB modernized their data platform with Dagster, doubling pipeline speed and enabling self-service analytics for 4.5M citizens.

Source: https://dagster.io/customers/federation-wallonie-bruxelles-case-study
Published: 2025-07-01

As one of Belgium’s three linguistic communities, the [Fédération Wallonie-Bruxelles](https://www.federation-wallonie-bruxelles.be/) oversees education, sports, culture, youth services, etc. for 4.5 million citizens — and manages a workforce of over 7000, spread across 5 different general administrations (“Administrations Générales”) and hundreds of teams/departments within the ministry. With such massive scope, the organization generates staggering amounts of data from the citizens it serves and the organizations it subsidizes. But FWB’s fragmented, closed-source data systems were slow, inaccessible, and too often maddeningly manual.

[Martin Erpicum](https://www.linkedin.com/in/martinerpicum/) is a data engineer at Fédération Wallonie-Bruxelles, where he helped transform FWB’s cumbersome legacy system into a modern, modular data platform. As Martin explains to his C-suite, Dagster’s flexibility enables his team to meet FWB’s significant organizational needs without being constrained by the platform. “No vendor lock-in means that 90% of our code can be re-used anywhere,” he says. “And, in just ten months of use, I’ve already seen multiple instances where Dagster’s data observability features helped us catch and resolve issues that would have otherwise gone unnoticed.”

### Key Results

- **2x pipeline speed:** Time to deliver results from raw data to BI tools reduced by more than 50%
- **Reactive -> proactive:** Data teams now proactively propose new data products to users, instead of just reacting to requests 
- **Visibility unlocked:** Data quality issues that would previously go undetected are now quickly discovered and resolved with Dagster’s unified control plane
- **Zero accessibility -> self-service**: End users now provision their own data dashboards from always up-to-date data mart

### Breaking free from closed-source constraints

When Martin arrived at the Fédération Wallonie-Bruxelles, he found a ministry trapped in a cycle of technical debt and operational inefficiency that many large organizations will recognize. FWB’s existing data architecture had evolved organically over years, creating a patchwork of disconnected systems that hindered rather than helped their mission to serve Belgian citizens effectively. Martin’s team found it difficult to track changes or ensure data quality, and they longed to empower users to build and maintain their own data workflows.

The critical issues that drove change include: 

- **Closed-source tool dependency** that limited flexibility and transparency in government operations
- **Complete absence of version control** for data pipelines, making changes risky and difficult to track
- **Limited or non-existent data observability** into pipeline health, performance, or failures
- **Significant barriers to user onboarding** that prevented teams from becoming self-sufficient with data

The ministry's technical stack reflected this complexity, with data flowing from SQLServer, MySQL, PostgreSQL, Denodo, DB2, FTP files, and various APIs into their Azure Data Lake and BI tools like PowerBI and Metabase. “Most data transformations had to be done manually or through Excel,” Martin says. “This was a nightmare because each year we needed to reprocess everything, and I have colleagues that had to spend two or three months making modifications to one data set.”

### Choosing flexibility and modern engineering practices

After struggling with closed-source tools and fragmented data architecture, Martin and his team systematically evaluated their orchestration options to find a platform that could unify their complex ecosystem while granting more accessibility for analytics users.

The ministry tested multiple solutions including TiMi, KNIME, Apache Airflow, and Prefect before choosing Dagster. Their hands-on experience with Airflow for several weeks, followed by months with Prefect, gave them clear insights into what they needed to avoid. "Airflow was not intuitive for our users—the UI lacked user-friendliness, concepts were hard to grasp and its logging capabilities were limited," Martin explains.

After a thorough evaluation, Dagster emerged as the clear choice for several key reasons:

- **Asset-centric architecture**: Dagster's focus on data assets as first-class citizens aligned perfectly with how their teams conceptualized data workflows, rather than just thinking about tasks and processes.
- **Modern developer experience**: The platform offered clean development workflows, local testing capabilities, and software engineering best practices that their technical teams valued.
- **Advanced observability**: Built-in monitoring and debugging capabilities were essential for managing their complex, heterogeneous data environment across multiple sources and destinations.
- **Organizational adaptability**: Dagster's flexible architecture could accommodate FWB’s five “Administrations Générales” working across very different domains.
- **Future-proof foundation**: The platform could serve both their immediate orchestration needs and provide a foundation for advanced analytics and AI/ML capabilities.

Dagster's differentiated approach resonated immediately with their needs. The asset-centric architecture aligned perfectly with how their teams actually thought about data, while the modern developer experience promised to overcome the friction and inaccessibility that had historically prevented widespread adoption of data tools across the ministry. "Dagster was particularly well-suited to the polycephalous structure of our organization,” Martin says, referring to FWB’s many internal administrations, and noting that Dagster was the only solution they’d found that offered advanced data observability across such a divergent data estate.

> *We greatly appreciate that Dagster treats assets as first-class citizens in its architecture. This capability for comprehensive tracking and management of data assets throughout their lifecycle is crucial for us.*

### Building a developer-first data platform with Dagster

Rather than attempting a big-bang migration, the Fédération Wallonie-Bruxelles adopted a thoughtful, phased approach that prioritized developer experience and organizational change management — and lots of onboarding. “This is a ministry in the public domain, and the data literacy in our organization is not that good yet,” Martin says. “We knew adoption success will be only as good as our support for our users.”

FWB’s strategic migration approach included: 

- **Phased rollout strategy** to minimize disruption and ensure smooth transitions
- **Hands-on user support** with tailored onboarding sessions for each team
- **Comprehensive training programs** covering fundamentals like SQL, Python, Git, and Docker
- **Best practices development** to ensure consistency and quality across all pipeline development
- **Custom Python package creation** to simplify interactions with their unique ecosystem

The technical implementation showcased sophisticated engineering practices that maximized Dagster's capabilities while addressing the ministry's specific requirements:

- **Custom Dagster resources and IO managers** for SQLServer, PostgreSQL, DB2, and various APIs
- **Seamless dbt integration** using Dagster's built-in support for data transformation workflows
- **DuckDB integration** for efficient data processing and analysis
- **Automated project integration** using Git submodules and automated workflows
- **GitLab CI/CD pipeline automation** with testing, validation and deployment on Docker Swarm infrastructure – provided by our IT partner (ETNIC) – across feature branch, staging, and production environments

One of their most innovative implementations demonstrates the power of treating infrastructure as code: "We developed a workflow to automatically integrate dbt projects into our Dagster code locations. This workflow implies the separation of code repositories for dbt projects and Dagster code locations. On each dbt project release, we automatically include the dbt project as a Git submodule into the Dagster code location."

This approach enables their users to leverage existing dbt projects without requiring deep Dagster knowledge, significantly lowering the barrier to entry while maintaining the benefits of unified orchestration.

> *I really wanted our data analysts to feel confident and not intimidated — for them, even using a CLI could be challenging. With Dagster we could give them their own environment, included in the orchestration.*

### Measurable improvements in reliability and productivity

Six months after implementation, the Fédération Wallonie-Bruxelles has achieved transformational results that demonstrate Dagster's impact on both technical operations and business outcomes. The combination of improved reliability, enhanced developer productivity, and proactive data management has fundamentally changed how the ministry approaches data.

FWB’s quantifiable operational improvements include:

- **Pipeline reliability enhancement** through comprehensive monitoring and alerting via MSTeams and NTFY notifications
- **Faster issue resolution** enabled by Dagster's observability features for quick problem identification
- **Improved data quality detection** helping software developers identify and resolve application-level data issues
- **Reduced operational disruptions** through more reliable pipeline execution and monitoring
- **Enhanced decision-making confidence** enabling proactive proposal of new data products to users

The transformation extends beyond mere technical metrics to cultural and organizational changes that demonstrate how the broader value of modern data orchestration brings organizational and strategic benefits:

- **Shift from reactive to proactive** data product development and user support
- **Increased data literacy** across the organization through improved training and documentation
- **Better stakeholder confidence** in data quality and delivery timelines
- **Enhanced collaboration** between technical and non-technical teams
- **Stronger foundation** for future AI/ML initiatives and advanced analytics

The ministry's monitoring approach exemplifies how proper observability transforms operations: "We set up runs and data quality checks monitoring (with notifications) to ensure that our data pipelines are running as smooth and efficiently as possible,” Martin explains.

This is how, he says, the FWB data team went from discovering problems only *after* dashboards broke to preventing issues before they can impact end users — a crucial capability when serving 4.5 million citizens who depend on reliable government services.

### Expanding capabilities and adding AI

The Fédération Wallonie-Bruxelles sees Dagster as the foundation for ambitious next steps that will further democratize data access and, eventually, leverage cutting-edge AI and ML technologies to better serve their citizens.

FWB’s immediate roadmap priorities include:

- **AI-powered user assistance** through MCP service integration connecting their custom Dagster library documentation to an LLM for personalized, context-aware help
- **Enhanced onboarding** exploring Dagster's new dg CLI to further streamline user adoption
- **Advanced analytics capabilities** building on their solid orchestration foundation to support growing AI/ML workloads

The ministry's long-term vision positions Dagster as the central component in a comprehensive data ecosystem that supports unified, efficient data services across all stakeholder needs while promoting data literacy and engineering best practices throughout the organization.

"We plan to integrate an MCP service that connects our custom Dagster library documentation and data discovery capabilities to an LLM, enabling personalized, context-aware assistance for our users," Martin explains. "Thanks to Dagster, dbt, DuckDB and our data lake, we are building the foundation of a data platform that can grow and flex to support the needs of our organization for a very long time to come."

---

## How easyJet Holidays Achieved 15x Faster Data Pipelines with Dagster

> Learn how easyJet Holidays reduced pipeline execution from 2.5 hours to 10 minutes with Dagster, eliminated manual support, and democratized analytics.

Source: https://dagster.io/customers/easyjet-holidays-case-study
Published: 2025-06-24

[easyJet Holidays](https://www.easyjet.com/en/holidays) is the package holiday division of easyJet, offering bundled flight and hotel packages to destinations across Europe and beyond. By implementing Dagster as their modern data orchestration platform, they created a unified control plane that helps their engineering team confidently build, scale, and observe their data pipelines. Adopting Dagster enabled the easyJet Holidays team to make pipeline execution 15x faster, improve data freshness, and automate cumbersome manual processes that previously required dedicated support resources.

> *Currently, Dagster is the heart of the analytics platform and it’s doing a fantastic job. Even if we only ever used it for analytics we’re already getting loads of value, but next we're starting to look at Dagster for a kind of operational data platform.*

### Key Results

- **Pipeline execution time:** 2.5 hours → 10 minutes
- **Cost savings:** Dedicated support resources for troubleshooting → fully re-allocated spend
- **Data freshness:** Self-healing pipelines enhance reliability with more consistent and timely data

### A complex and inefficient data pipeline — and an opportunity

easyJet Holidays began as a subsidiary of easyJet five years ago, operating as a relatively independent startup within the larger airline organization. Initially, [Tom Cronin](https://www.linkedin.com/in/thomas-cronin/) the Head of Data says, his team was “kind of borrowing the airline data platform” and using some of their infrastructure.

The company had already built its own independent data platform using basic AWS native services, Redshift, Glue, and Airflow, but the Holidays subsidiary had very different data needs. “The airline has all of these big, heavy, expensive requirements, and we don’t…It was like using a sledgehammer to drive a thumbtack,” Tom explains. Beyond being designed for vastly larger-scale workloads, the airline’s infrastructure came with a necessary amount of beauracracy that slowed down the smaller, more agile holiday business.

While functional, this vanilla setup created significant challenges that perfectly matched the pain points that Dagster was designed to solve:

- **No unified control plane for observability.** The existing “vanilla” system lacked end-to-end visibility across the data platform. There was no way to track lineage between data assets and it was impossible to see the full impact of failures or changes.

- **Poor reliability and manual intervention.** Pipeline execution times were lengthy, averaging 2.5 hours. There were also frequent pipeline failures that required dedicated support resources to manage them.

- **Siloed teams without collaboration.** In the existing system, analysts couldn't contribute to the data pipeline. There were no standardized workflows across teams, and changes required specialized knowledge of multiple systems

“There were constantly questions like, ‘*Oh, I'd like to make this change. Now, is that a change to the glue job? Is it a change to the SQL script? Or is it a change in my Airflow config?*’ And, by the way, the person asking that question doesn't know what any of those things are because they're an analyst whose job is to create dashboards,” Tom explains. “So immediately we had this huge problem of no one really understanding the end-to-end.”

#### A “mish-mash” system

When Tom arrived at easyJet Holidays as the new Head of Data, he quickly discovered that there was also a need for some good ETL tooling. “We built a load of essentially custom hacky Python code in Glue to do our data extraction. It was a mish-mash that worked — pretty much, anyway — but also it lacked some basic hygiene stuff,” Tom says.  

But he also recognized they needed more than just another ETL tool — they needed a modern data orchestration platform. Failed jobs required frequent manual intervention, fragmented tooling led to extended troubleshooting times, and making changes involved a data engineer parsing through 2000 lines of SQL. In the midst of all these issues, Tom saw more than the need to retool: he saw the opportunity to convert from a legacy stack to a powerful platform built on best practices:

> *I want anyone who writes SQL to be able to contribute changes. Data engineers should be able to write code for pipelines and follow software development practices. At the same time, analysts need the right access and visibility to make changes without having to open a ticket.*

### Why easyJet Holidays chose Dagster

The easyJet Holidays team initially explored a number of solutions on the market. While a number of the tools solved parts of the problem, he quickly realized they needed a more comprehensive solution—a true modern data orchestration platform that could serve as the unified control plane for their entire data platform.

After researching options, Tom discovered Dagster through the data engineering community on Reddit, where engineers consistently praised Dagster’s developer-centric approach and powerful capabilities. As he explored the platform, Tom found that Dagster aligned with his priorities for easyJet Holidays’ new data platform:

**Democratized workflows:** Dagster provided a single pane of glass for easyJet Holidays' entire data estate, with a unified catalog and lineage tracking across all data assets. This enabled the team to standardize workflows while preserving autonomy for different teams, supporting both data engineers and analysts with varying skill levels through one cohesive platform.

**End-to-end visibility:** The platform offered end-to-end visibility for faster debugging and better observability, with built-in monitoring and alerts to prevent data outages. Asset checks and testing ensured data quality throughout the pipeline, while branch deployments allowed the team to test changes safely before deploying to production.

**Code-first approach:** Dagster's code-first approach aligned perfectly with software engineering best practices, providing a clean, Python-based development framework. The local development experience allowed for faster iteration, with full Git integration for version control and declarative automation that significantly reduced manual work for the engineering team.

**Flexibility and scalability:** With native integrations for modern tools like dbt and the flexibility to adapt as their platform evolves, Dagster offered easyJet Holidays the ability to scale without increasing complexity. The platform's design reduced operational overhead while ensuring they could continue to evolve their data capabilities.

> *We needed a single pane of glass for our entire data estate, with everything in Python and Git so our team could follow software development best practices. Dagster did all of this and enabled our analysts—and anyone writing SQL—to contribute directly through pull requests instead of opening support tickets for the data team.*

### A rebuilt data platform with Dagster at the center

The easyJet Holidays Dagster implementation serendipitously coincided with a planned AWS migration, timing that Tom called "quite fortunate" for rebuilding their platform. "We were about to do a big migration moving between AWS accounts. Normally, I would see it as a headache, but I thought, ‘*Hey, this is actually a great opportunity to not lift and shift anything – let’s instead rebuild the platform in the new environment.’*

The team took an ambitious “no mess” approach of bringing zero code from their previous platform, essentially starting fresh with Dagster as the unified control plane for orchestration, observability, and collaboration. They designed a data-centric architecture from the ground up to solve the fundamental issues in their original system:

**Data-centric architecture with assets as first-class citizens**

- Shifted from task-oriented Airflow DAGs to assets that represent actual data
- Implemented a medallion architecture pattern with bronze (raw), silver (cleaned), and gold (business) layers
- Created modular, maintainable SQL transformations with dbt integration
- Organized work around data assets rather than tasks for better maintainability

**End-to-end lineage and observability**

- Mapped all data assets and their dependencies in Dagster's unified catalog
- Gained complete visibility into data flow across the entire platform
- Set up freshness policies to monitor data timeliness
- Enabled faster debugging and impact analysis with comprehensive lineage tracking

**Modern developer experience**

- Created development environments with branch deployments for safe testing
- Set up individual developer databases for local testing and iteration
- Implemented software engineering best practices with Git integration
- Enabled unit testing for data transformations

**Built-in data quality and reliability:**

- Integrated dbt tests as Dagster asset checks
- Implemented retry policies with appropriate backoff
- Added automation conditions to recover from common failures
- Built monitoring to alert only on genuine issues

#### A smooth and straight-forward onboarding process

Tom approached the onboarding process methodically, aiming for a process that could adapt as needed for each data team member’s needs and capabilities. He personally handled the initial setup and built the initial version “with confidence” due to Dagster's intuitive Python framework. Tom tested his proof of concept with real data flows before moving to team-wide onboarding:

**Step 1: Demonstrating end-to-end visibility.** Tom first introduced using Dagster's UI as the single pane of glass for the entire platform. He demonstrated how lineage tracking makes dependencies visible and troubleshooting easier, allowing team members to see exactly how data flows through their systems. He also showed how data assets were now connected across the entire platform, making it possible to draw data insights that their original fragmented toolset never allowed.

**Step 2: One platform, many teams.** Tom began with introducing analysts to simple dbt SQL models in the familiar IDE of their choice. Meanwhile, data engineers learned the more advanced Dagster Python framework for orchestration. Each team got to work with their preferred tools while still contributing to the same platform, using a workflow that enabled collaboration through Git and pull requests rather than tickets. 

**Step 3: Showcasing reliability by design.** Next, Tom used actual failure scenarios to demonstrate Dagster’s resilience. He showed the team how self-healing pipelines automatically recover without manual intervention — and, in the process, demonstrated the value of pipeline visibility. “I basically got it wrong on my first try and made a test downstream fail,” Tom says. “And I think that was a real light bulb moment for a lot of people on my team — ‘*Oh, because it failed, that's how he found the error.’*

> *The Dagster and dbt integration created a clean separation where our data engineers work with Dagster code while analysts just interact with SQL—which means any SQL developer in the organization can contribute to our pipeline without needing to understand the underlying details.*

### Reliability and efficiency, plus developer experience

Since implementing Dagster as a critical part of their data platform, the easyJet Holidays team has experienced a dramatic improvement in data pipeline reliability, operational efficiency, and developer experience. 

- **Dramatically reduced pipeline execution times.** The shift from Airflow and Glue to Dagster and dbt enabled the team to move transformations outside of source systems, expediting execution times from multiple hours to minutes.
- **Faster failure identification and resolution.** Dagster’s advanced dependency mapping, traceability, and asset-based model have drastically reduced the time spent identifying and resolving pipeline failures. When issues occur, the team can now do partial runs of the pipeline for specific assets, greatly reducing troubleshooting time.
- **Modular data platform**. Monolithic SQL scripts have been replaced by modular, maintainable code, structured around assets.
- **Increased collaboration**. With the adoption of Dagster and dbt, anyone who writes SQL can now contribute through pull requests.
- **Improved testing practices**. The introduction of testing environments through branch deployments further encourages contributions and collaboration from analysts and anyone writing SQL.
- **End-to-end visibility**: Dagster acts as a single pane-of-glass for their data estate with system insights and failure tracking that drastically improve troubleshooting and decision-making for the team.

#### Future Dagster-driven feature opportunities

Having established Dagster as their unified control plane for batch analytics, the easyJet Holidays team is now exploring how to leverage Dagster's composable architecture for operational use cases around real-time and streaming data: 

- **Expanded workload support:**
  - Operational data platform with real-time and streaming capabilities
  - AI/ML workflow integration within the same platform
  - More complex data transformations and analytics
- **Deeper integration of platform components:**
  - Closer alignment between analytical and operational data systems
  - Additional data quality tooling and monitoring
  - Enhanced metadata management across the platform

The team values Dagster's horizontal integration capabilities, which provide confidence that they can incorporate whatever new tools emerge in the data ecosystem without being locked into vendor-specific solutions.

As Tom explains, having Dagster as their unified control plane means "whatever new testing framework appears, the worst-case scenario is you just have a Dagster asset that says 'I ran that thing,' but it can do it." This flexibility and future-proofing are key to their long-term data strategy as they continue to scale across the organization without added complexity.

---

## How US Foods Eliminated Data Silos and Achieved Near-Perfect Reliability with Dagster

> Learn how Fortune 500 company US Foods transformed their fragmented data architecture with Dagster, achieving 99.996% uptime across 750+ deployments.

Source: https://dagster.io/customers/us-foods-case-study
Published: 2025-06-17

[US Foods](https://www.usfoods.com/) is one of America's leading foodservice distributors, currently offering over 350,000 products to approximately 250,000 restaurants and foodservice operators nationwide. With 30,000 employees across more than 70 locations, the company provides a broad and innovative food offering alongside comprehensive e-commerce, technology, and business solutions to help their customers succeed. As a major player in the foodservice industry with approximately $24 billion in annual revenue, US Foods was the 10th largest private company in the US until its IPO and now ranks at #119 in the Fortune 500 list — but their original data architecture was failing to scale with the company’s success.

### Key Results

- **Exceptional reliability:** 99.996% platform uptime across nearly 750 production deployments
- **Usability:** Bespoke and completely unstandardized -> modern composable platform
- **Execution excellence:** Fragile, “ad-hoc” system with frequent failures -> Millions of jobs run with 99% execution success rate 
- **Visibility:** duplicate code and data sets -> single pane of glass

Before he joined US Foods as a Machine Learning engineer, [Lee Littlejohn](https://www.linkedin.com/in/leejlittlejohn/) was a restaurant chef. “Running a busy kitchen and building a data platform might not seem terribly connected, but I found that they're actually quite similar,” Lee says. “In either one, success is all about coordination, efficiency, quality under pressure, constantly juggling multiple stations, managing inventory, and ensuring consistent quality while maintaining rapid service.”

![](https://dagster.io/webflow-assets/92ecfd2cd2a8632ed426.png)

Lee's Success with building out a data platform with Dagster

### Technical debt, limited visibility, and siloed teams experiencing cognitive overload

When Lee arrived at US Foods, he found that the existing data architecture was creating significant technical and operational friction, to the point where US Foods was unable to fully leverage its data to drive business value. With a large organization processing massive volumes of data across multiple business units, these inefficiencies were increasingly costly and unsustainable:

- **Technical debt and inconsistent infrastructure**: The existing data architecture consisted of disconnected systems with no standardized approach to data pipeline development or management. Teams were using a mix of legacy ETL tools, custom Python scripts, and manual processes. “We had different teams using different tools, different processes, basically speaking different languages,” Lee says.

- **Siloed team organization**: Each team operated independently with their own environments, approaches, and tooling, creating redundancies and making it difficult to share data assets or establish consistent practices. “We had redundant work happening with duplicate code and data sets because teams simply didn't know what already existed.”

- **Limited observability**: There was no centralized way to monitor pipeline health, track failures, or understand dependencies between data assets. When issues occurred, extensive manual investigation was required to identify root causes: “One change could break something completely unrelated and troubleshooting was a nightmare.”

- **Deployment challenges**: The deployment process was manual and error-prone, with no consistent pattern for promoting code between environments. This led to frequent failures and difficulty tracking changes.

- **Scaling limitations**: As US Foods' data needs grew, the existing ad-hoc approach couldn't scale efficiently. Adding new teams or use cases required significant overhead and created additional silos.

- **Cognitive overload for engineers**: Without clear patterns and organization — and also lacking defined responsibilities and boundaries between teams – engineers spent excessive time understanding complex systems rather than delivering business value.

- **Inadequate dependency management**: Dependencies between data assets weren't explicitly tracked, making it difficult to understand the impact of changes or failures.

> *As a new employee, it was certainly challenging to get a solid handle on how the data platform worked. Jenkins jobs running random scripts on inconsistent schedules, fragmented dags, ingestion that nobody fully understood. Ad hoc scripts living on individual laptops, infrastructure soup…bottlenecks and fragile systems. One change could break something completely unrelated and troubleshooting was a nightmare.*

### Why US Foods chose Dagster

There are parallels between running a kitchen and managing a data platform: both require clear systems, defined responsibilities, and consistent patterns to operate efficiently. Tasked with transforming US Foods' fragile and fragmented data ecosystem, Lee Littlejohn needed a data orchestration platform that aligned with his vision for a more organized, efficient system.

Dagster's asset-based approach resonated with Lee's background as a chef, where the focus is on the final product rather than the steps to prepare it. This alignment between his culinary principles and Dagster's data philosophy was compelling: Dagster's structured approach enabled the creation of clear, repeatable patterns across the platform — similar to how standardized recipes ensure consistent results in a kitchen.

“Anybody who practices a craft loves their toolkit, and engineers are no different,” Lee explains. “We selected Dagster as an orchestrator for compelling reasons:

**Assets over process:** “Dagster’s asset-based orchestration focuses on the data product itself that you're creating, not just the process. This aligns perfectly with our goal of creating clear, visible data products.”

**Built-in observability**: “Dagster has a great UI, and real-time monitoring of pipeline health and performance that gives us unprecedented visibility into what's happening with our pipelines.” 

**Integration capabilities**: “Dagster’s got extensive and extendable integrations that we can connect to virtually any system we need, including AWS, Snowflake, and container-based deployments.”

**Consistency between environments:** “Another huge advantage with Dagster is the parity between local and cloud development. You run the exact same code, same packages on your laptop that will run in production, which makes development and testing a lot smoother.

**Single source of truth**: Dagster provided a unified view of all data assets and their dependencies, establishing a definitive resource for understanding the data ecosystem.  “Dagster gives us VISIBILITY, We can see what's running, what's failed, what depends on what, and so much more.” 

> *Basically anything you can do in Python, you can do in Dagster. It's extremely flexible, and that flexibility is very powerful.*

### Building a modular, consistent data platform

Lee's team designed a comprehensive platform that addressed the full spectrum of the company’s data needs, using Dagster to build a data system that is accessible while maintaining consistency and providing clarity throughout.

#### **Modern deployment architecture**

The team established a containerized, infrastructure-as-code approach that supported clear boundaries between US Foods’ technical teams:

**Poetry for dependency management**. Team-specific dependency groups created clean separation between components while ensuring consistency. "Each team has their own dependency group, which maps directly to a stage in our Docker builds," Lee explains. Meanwhile, dependencies were strictly controlled and explicitly tracked to prevent conflicts.

**Multi-stage Docker containerization**. Lee oversaw a standardized build process that mapped directly to Poetry dependency groups. Base images included only the dependencies needed for each specific team, while build patterns were consistent across all teams. “This was major for reducing cognitive load for developers,” Lee says.

**AWS CDK for infrastructure**. Cloud infrastructure was defined with one stack per team for clear ownership. Infrastructure definitions were treated as code with proper version control and testing, and each team maintained their own stack while adhering to platform-wide standards.

**GitHub Actions for CI/CD**. The new platform implemented automated deployments with team-specific labels. Pull requests were automatically deployed to development environments for testing, and production deployments followed a consistent, automated pattern with appropriate safeguards.

#### **Dagster implementation**

Dagster served as the platform’s central orchestration engine, providing the essential structure and visibility central to Lee’s vision for the org’s new data platform.  Each component maps directly to the next in a consistent pattern, maintaining team identity throughout the system. 

**Code location per team**. One Dagster code location was established for each team to maintain clear boundaries. Teams worked independently within their own code locations, and the Platform team maintained visibility across all code locations.

**Standardized patterns**. Asset-oriented organization enabled US Foods to create consistent approaches to common problems. The team created reusable components for frequent operations, as well as standardized testing and deployment workflows.

**Snowflake integration**. Implemented one database per team per environment in Snowflake, with clear separation between development, staging, and production. Team-based access controls and resource allocation

**Development workflow**. Dagster was central to creating new processes that supported both independence and standardization. Engineers could test their changes locally before committing, and Dagster's local development capabilities allowed for rapid iteration.

**Consistency through modularity**. The platform maintained consistency by establishing clear patterns that mapped across components. Each component of the platform (dependencies, Docker builds, infrastructure, Dagster code locations, Snowflake databases) was organized by team, and this consistent structure reduced cognitive load for developers.

“Our configuration approach accommodates team differences while maintaining consistency, thanks to the Dagster resource construct and dependency injection," Lee says. “We define environment specific resources and configurations once, and then inject them into functions where they're needed.

> As a result, the same code can run in different environments with appropriate configurations without requiring changes to the code itself — so you only code it once and it just works everywhere.

#### Exceptional reliability and operational excellence

Within a year of implementation, US Foods' Dagster-centered data platform has delivered remarkable results, transforming a once chaotic and failure-prone system into a transparent, accessible, and above all, reliable data ecosystem — one that significantly reduces operational overhead. For US Foods, their new data platform centered around Dagster has delivered:

##### Unparalleled reliability 

The new platform’s structured, consistent approach has virtually eliminated downtime and reduced failures to a minimum:

- **99.996% platform uptime**: Across nearly 750 production deployments, the platform has experienced 8 deployment failures — none of which resulted in actual downtime.
- **99% job success rate**: Failures are quickly identified and addressed through comprehensive monitoring. The consistent patterns and clear ownership boundaries make troubleshooting faster and more effective.
- **Proactive issue detection**: Problems are identified before they impact business operations. Automated monitoring alerts teams to potential issues, and end-to-end data lineage makes it easy to understand the impact of any failure.

##### Enhanced developer productivity

The now-cohesive workflow reduces cognitive load on developers and has accelerated development cycles.

- **Standardized development practices**: All teams follow consistent patterns for development, testing, and deployment. Reusable components reduce duplication of effort across teams and new team members can quickly become productive due to the clear, consistent structure.
- **Self-service capabilities**: Teams can deploy independently without platform team intervention, and clear boundaries and patterns reduce the coordination overhead between teams.
- **Clear visibility and understanding**: A Dagster-centered unified control plane has made the data ecosystem transparent and accessible

##### Scalable, future-friendly platform

Migration from the previous system was accomplished with just 6 to 7 engineers, demonstrating the efficiency of applying consistent principles and thoughtful design patterns.

- **Sustainable growth**: The modular, team-based organization allows for expansion without increased complexity. New components can be added without disrupting existing workflows. Clear patterns make it easier to extend the platform to new use cases.
- **Organizational alignment**: The platform has helped bridge the gap between technical and business stakeholders. Clear visibility into data assets improves communication, and consistent organization makes it easier to map technical assets to business processes.

##### Operational efficiency

US Foods succeeded in reducing the overhead of managing a complex data ecosystem:

- **Automated deployments**: The CI/CD pipeline has eliminated manual deployment tasks with a consistent, reliable process for promoting changes to production, reducing time spent on operational tasks.
- **Clear ownership**: Defined responsibilities with enforced boundaries mean that, when issues arise, the responsible team is immediately clear. Coordination between teams “has never been easier.”
- **Streamlined troubleshooting**: When problems do occur, they're resolved more quickly because clear system structure and visibility makes it easy to identify root causes. 

> Out of almost 750 full production deployments, we have had less than eight failures and none of those resulted in actual downtime. I think that's pretty good. And four of those were me and one of my coworkers playing a little fast and loose and trying to get around our own branch protections.

Lee recently did a deep dive with us about around their Dagster use case and implementation. Watch the full session here:

[Video: YouTube video player](https://www.youtube.com/watch?v=hoVvlgUYkM8)

---

## Connect 211's Small Team, Big Impact: Building a Community Resource Data Platform That Serves Millions

> Connect 211 cut pipeline troubleshooting by 80% and scaled from startup to largest health data publisher in 20 states with modern orchestration.

Source: https://dagster.io/customers/connect-211-case-study
Published: 2025-06-10

Built to serve communities in need, [Connect 211](https://connect211.com/) provides a platform that standardizes and publishes health and human services data from 211 call centers across the United States. Connect 211 lets service organizations rapidly launch their own search engine, without needing in-house developers, to make their resources searchable, shareable, and profitable. The company needed a data orchestration platform that helped Connect 211’s users efficiently and affordably connect people with the information they need to live better lives in their communities, from finding food or shelter to accessing health care or education.

#### Key Results

- **Issue response time** down from days -> hours
- **“Game-changing” efficiency:** Low data throughput, high hands-on system -> high throughput, low hands-on
- **Reduced pipeline troubleshooting time by 80%** with improved visibility and error handling
- **Affordability unlocked:** Lower devops costs and better insight into operations and issues made “the overall cost point much more affordable” 

Connect 211’s use case demonstrates how Dagster's improved visibility and error-handling capabilities can dramatically reduce troubleshooting time for complex, multi-source data pipelines. Strong integration with dbt enables comprehensive data quality testing within a unified interface as a part of a well-architected orchestration system that can transform fragmented, legacy data sources into a standardized, high-value data platform.

### Complexity, little visibility, and limited resources 

When Connect 211 set out to create a user-friendly directory for health and human services resources, they faced a challenging technical problem: the project required connecting to hundreds of data silos, many using outdated technology dating back to the 1960s-1990s. 

“We had to connect to all of these different data silos, many of them in various stages of awful technology,” explains Connect 211 CEO Skyler Young. “Then we have a vast orchestration process that connects to all the things, pulls the data in somewhere between hourly and daily. Cleans it, standardizes it, transforms and enriches it, and then publishes to various services.” In other words, he says, data orchestration *is* Connect 211’s business.

Initially, they had hard-coded pipelines that were brittle and difficult to maintain. The organization then moved to Airflow, which was an upgrade but still required more DevOps support than a bootstrapped startup could manage. “Airflow is just a generally heavy lift for a small team,” Skyler says, describing the Connect 211 team’s struggles:

- **Limited visibility:** Error logging and visibility were handled separately for every single component because Airflow could not provide enough insight into their data pipelines.
- **Diagnostic difficulties:** This limited visibility made troubleshooting problems a nightmare, Skyler says. “Because we're orchestrating a bunch of different things in what’s not necessarily a tightly coupled or integrated environment, we had to handle error logging and visibility separately for every component that's being orchestrated.”
- **Complexity at scale:** As the platform expanded to connect with more data sources, they encountered a series of scaling issues and problems that were taking a long time to diagnose".
- **Integration challenges:** dbt was their core transformation tool, and they needed better integration between their orchestration platform and transformation processes.

> *We had a series of scaling issues, and problems that were taking a long time to diagnose. I only had junior level engineers to help me and they were just like, Nope, can't handle it. Gotta do something a little easier. - Skyler Young*\
>  

### Why Connect 211 chose Dagster

Skyler says he looked at Prefect and also briefly considered Mage, which he found to be too new. “Dagster won out pretty easily for being enterprise grade, scalable, and big-feature rich — but also a hell of a lot easier to run and use than Airflow,” he says.

**dbt integration:** “The thing that made Dagster a total win for us is the really tight integration with dbt, which is the nucleus of our data orchestration system.”  

**Error visibility:** "Being able to get the dbt errors in real time, seamlessly integrated with any errors that might be coming from the platform itself, has made a huge difference."

**Industry recommendations:** Multiple data analytics engineering teams they consulted with recommended Dagster, saying "yeah, Dagster's really good."

> *Dagster made data transformation really accessible to us when we were pretty early and inexperienced and not heavily resourced — and now we're the largest data publisher for Health and Human Services data in 20 states now, which is crazy.*

### Building a data federation platform with Dagster at its core

Skyler and his team constructed a robust data orchestration system that can connect health and human services data sources, standardize “some incredibly variable and fuzzy data,” and publish it to a user-friendly directory. Dagster serves as the backbone of this comprehensive data federation platform:

- **Data ingestion pipeline:** They implemented a "vast orchestration process that connects to many things, pulls the data in somewhere between hourly and daily," from various data silos, many using wildly outdated technology.
- **Transformation workflow:** After ingestion, data goes through cleaning, standardization, transformation, and enrichment processes before being published to various services.
- **Publishing structure:** Their project structure reflects a pub-sub architecture with "writers that push data into the system and process it and prepare it for publishing" and "readers" that "aggregate multiple writers together and then publish them to a specific destination."
- **Data quality testing:** "We use dbt as our first line of defense for quality checks," explains Young. "I wrote a dbt Schema YAML that runs tests for all the core relationships and constraints on data types and values. In a full dataset, that's something north of 800 tests that get run after we've done our initial transformation."
- **Elasticsearch integration:** They integrated Elasticsearch to search thousands of resources based on keywords, taxonomies, and geospatial data. This allows them to search thousands or tens of thousands of resources and automatically filter on geographic service areas with millisecond response times.
- **Geospatial processing:** Connect 211 built a specialized service that "parses the usually non-structured or semi-structured service area definitions" from source data and turns them into GeoJSON format, enabling jurisdiction-based searches.

"We have a vast orchestration process that connects to many things, pulls the data in somewhere between hourly and daily,” Skyler says. “Cleans it, standardizes it, transforms and enriches it, and then publishes to various services. It's becoming quite a large kind of data federation platform.”

> *Data orchestration is our primary business, so Dagster has been a total game changer for us.*

### Reliability, visibility, and operational excellence

In just about a year since its implementation, Connect 211's Dagster-based data platform has transformed how health and human services information is collected, processed, and shared. This foundation enables better access to critical community resources and opens new possibilities for the future.

- **Pipeline reliability:** Connect 211’s data pipeline reliability has greatly improved since implementing Dagster, Skyler says. “We’re maintaining higher levels of data throughput with less developer time than we used to. We’re also much faster at identifying and fixing errors.”

- **Improved observability:** Their previous solution required error logging and visibility to be handled separately for every single component. Now, Dagster provides comprehensive visibility across their entire data platform.

- **Dramatically reduced troubleshooting:** When errors occur, the team can now quickly identify the source and take corrective action, rather than spending hours hunting through disconnected logs.

- **Standardized data:** Connect 211 now delivers "a very usable stockpile of standardized data that is ready for engineers to consume and use in reality," addressing the challenges of working with inconsistent source data.

- **Scalable architecture:** Already "the largest data publisher for Health and Human Services data in 20 states," Connect 211 now has a scalable data platform that can continue to grow with them.

- **Foundation for innovation:** Their standardized data platform provides opportunities for advanced applications, including AI-powered entity resolution, data cleaning, and sentiment analysis for improved search interactions.

"At the end of it, what we have is a very usable stockpile of standardized data that is ready for engineers to consume and use in reality, because we had to build it for that purpose. I'm super proud. It's just, it's remarkable," Skyler says.

One of Connect 211's most significant innovations is their service area processing: "One of our biggest successes was building a service that parses the usually non-structured or semi-structured service area definitions from source data and turns them into GeoJSON reliably and accurately," explains Young. "That's the type of enrichment you need to make this data something developers can actually use."

> *Our users are having a huge positive impact in their communities, and we're helping them just do their job easier and better and get supported for the work they do. I love this. It's good work.*

---

## Big Cartel Brought Fragmented Data into a Unified Control Plane with Dagster

> Learn how Big Cartel eliminated data silos and built a scalable, observable data platform using Dagster's unified control plane—from chaos to clarity in 6 months.

Source: https://dagster.io/customers/big-cartel-case-study
Published: 2025-06-03

Built by artists, for artists, [Big Cartel](https://www.bigcartel.com/) provides a platform for creators to make money doing what they love. Its e-commerce platform is designed to help artists, makers, and small businesses build and manage their own online stores through a simple and intuitive user experience and customizable templates. Big Cartel’s creative community roots show in their robust “start for free and upgrade as you grow” pricing philosophy, and their commitment to providing resources and support for artists and makers.

● Visibility: “Data all over the place” -> unified data control plane\
● Usability: Bespoke and completely unstandardized -> modern composable platform\
● Error detection: “Wait until the dashboard breaks and it's discovered eventually by someone" -> Slack-integrated "Data Firehose"\
● Data consistency: YBDMMV (Your Business Data Metrics May Vary) → Accessible, consistent, trusted data

### Accelerating data reliability and developer productivity

Patrick Trainer describes himself as a “data engineering team of one” hired to turn Big Cartel's patched-together data system into a modern and efficient platform.

This is the story of how a one-person data engineering team transformed Big Cartel’s disconnected and inconsistent data silos into a cohesive, observable platform with Dagster at its core, creating a unified control plane that provides complete visibility across Big Cartel’s entire data ecosystem that:\
● Implements Dagster's asset-based approach to achieve reliability by design, eliminating the pain of waiting for dashboards to break and replacing it with proactive monitoring and intelligent alerting.\
● Delivers on the promise that data engineers can have nice things too with local testing, branch deployments, and a clean development workflow that minimizes cognitive load.\
● Can grow along with the organization and serve as the foundation for building new features like customer-facing data products.\
● Uses Dagster as a “Swiss army knife” that efficiently handles current needs while providing a “test bed” for developing new capabilities and features for Big Cartel’s customers

With Dagster, Big Cartel now has a future-proof data platform that scales from internal analytics to customer-facing insights — demonstrating how the right orchestration solution can transform not just your data operations, but your entire business’s approach to data.

### Fragmented systems and limited visibility

Hired to build out Big Cartel’s data platform, Patrick found that “data at Big Cartel was in a fairly immature state.” Data was siloed in different applications with no central organization or unified view. Existing processes relied on one-off Python scripts, ad-hoc SQL queries, or random cron jobs — a setup he describes as “‘bespoke’ and completely unstandardized. There was zero observability into data pipeline performance or reliability and no foundation for building advanced analytics capabilities. Worst of all, he says, it meant his Big Cartel colleagues were getting inconsistent answers to business data queries, depending on timing and source.

One of the first things Patrick tackled was Mage, the data orchestration tool in place when he arrived at Big Cartel: “I hashed it out for a little bit and quickly saw it was tough to use."

**What Patrick was looking for in a data orchestration platform:**\
● Flexibility and breadth: Patrick needed a tool that could handle a variety of use cases beyond just pipeline scheduling\
● Enhanced observability: After experiencing siloed data with no visibility, Patrick wanted comprehensive monitoring of data flows\
● Unified control plane: The ability to connect different systems and provide a single view was essential\
● Modern developer experience: As an experienced data engineer, Patrick valued tools with clean development workflows\
● Easy deployment: The platform needed to be "easy to stand up" without excessive configuration\
● Visualization capabilities: Patrick sought a tool that could clearly illustrate data dependencies to non-technical stakeholders\
● Alerting and monitoring: The ability to proactively detect issues rather than waiting for dashboards to break\
● Integration capabilities: Support for connecting with their planned tech stack (Airbyte, dbt, Snowflake)\
● Future-proof architecture: A foundation that could grow from internal analytics to customer-facing data products

> In terms of observability, there really wasn't anything in place. Everything was siloed in different applications and was really more or less bespoke. Not exactly a great foundation to build on. So I ripped that out and put in Dagster.

### Why Big Cartel chose Dagster

As a data team of just two people with ambitious goals (“Besides me there’s a data analyst, we’re a scrappy duo”), Big Cartel needed a flexible and comprehensive — but not cumbersome — solution that could grow with them.

Patrick had previous experience with multiple orchestration tools, including Airflow, and none of them felt like the right answer. “I’d run the gamut and basically I knew what tools that I didn't want to use,” he says. He had been hearing good things about Dagster and was intrigued by Dagster’s asset-centric approach to data orchestration: “I liked how workflows are organized around data assets rather than just processes or tasks.”



From the start, he liked how easy Dagster was to stand up. Patrick also appreciated how Dagster offered:\
● **Modern, composable platform**: A more modern orchestration experience with strong support for software engineering practices — and data-centric architecture that aligns with modern data engineering practices.\
● **Zero complexity**: Dagster offered the simple deployment and straightforward local development experience Patrick needed as a team of one.\
● **Swiss army knife**: Big Cartel’s data platform would be rapidly evolving, and Dagster offered both flexibility and a wide breadth of use cases beyond basic orchestration, able to serve as a "Swiss army knife" for a variety of data challenges.\
● **Democratization of data workflows**: Clean, understandable visualization of data flows for all of Big Cartel’s data users, both technical and non-technical.

> I liked the flexibility and the breadth of use cases that there are. And with that I saw an opportunity for using Dagster as more than just an orchestrator... Being able to leverage it for all of the roadblocks that I know we will eventually face. And so it's good to have a Swiss army knife of a tool, which is what Dagster is.

### 
**Automation, innovation, and lightening the cognitive load**

Patrick took a clean-slate approach to Big Cartel’s data platform architecture, designing a modern data platform with Dagster at its core. His goal was creating a robust foundation that could scale with Big Cartel's needs while “keeping data as simple as possible” for stakeholders.

**Modern deployment architecture**: Big Cartel deployed Dagster on Kubernetes using Helm charts for container orchestration, which included:\
● Implementing a branch-based deployment workflow with separate environments: "We deploy on Kubernetes with Helm, a branch deployment running two kind-of concurrent branches: a main, which is prod, and then one for staging."\
● Creating feature branches off of the staging branch for development before promoting to production.\
● Automating the entire deployment process through Buildkite: "We use Buildkite to build a Docker image of our location."\
● Using Dagster’s separate deployment model for distinct instance and code location configurations.\
**Development workflow**: Patrick was heavily focused on automation through declarative configuration and pipelines. “It’s all about lessening the cognitive load. Otherwise I know I’m going to mistype something or forget how to do it," he said. So he:\
● Established a streamlined local development process using Dagster Dev to just “spin up local dev right then and there."\
● Created a testing environment that could mimic production: "Other times I'll want to test locally running Kubernetes... to try and mimic our production deployment as much as possible"

**Big Cartel’s "Data Firehose"**: Patrick created a specialized Slack channel that centralizes all system events via Dagster. "I call it the data firehose: successful alerts, unsuccessful alerts, pull requests, meta queries, everything goes into one channel." This has many benefits, including:\
● Using this consolidated view to develop intuition about system behavior: "When we're reading through that... you start to get a gut feel of the patterns... it allows you to internalize what the beat of your system is"\
● Leveraging this approach to detect anomalies: "If you remove all of that prescriptiveness and just put everything in one place, if something eventually goes missing then you'll pick up on that."\
● Using the firehose to understand how new tools affect the system: "As you bring in new tools you can see how that affects that beat of that flow\... See where that piece fits into everything else."

![](https://dagster.io/webflow-assets/a406befe7765f49bb700.png)

**Tech stack integration:** Building out the rest of Big Cartel’s data architecture, Patrick:\
● Connected Dagster with Airbyte OSS for data loading.\
● Integrated dbt for transformation, mapping dbt models as Dagster assets.\
● Incorporated various data sources including Amplitude for product analytics.\
● Used Snowflake as the data warehouse, with comprehensive monitoring.\
● Created a custom "meta layer" of queries to monitor Snowflake usage: "Being able to understand what is running or what is there in Snowflake, what is being used, what hasn't been used in a long time."

> The cognitive load is what I'm trying to minimize. And Dagster's made that simple by allowing you to glue together a lot of different things and push them to where you need them to be pushed.

### 
Accessible, reliable, simplified data.

Within six months of implementation, Big Cartel's Dagster-based data platform has transformed how the company works with data, creating a foundation for data-driven decision making and future growth. At Big Cartel, life with Dagster has meant:

**Visualization for stakeholders**: One of Patrick’s major goals was making data understandable and immediately useful for everyone else at Big Cartel. He says that Dagster’s asset-based approach, focusing on the data products themselves rather than the processes that create them, has made dependencies more explicit and workflows more maintainable — which has had “huge value” across the company.\
● Non-technical users can easily understand data lineage with Dagster’s asset view.\
● Made complex data flows understandable, which has been “super valuable" for Big Cartel’s data analyst.\
● Dagster’s asset visualizations have replaced lengthy technical explanations: "You can see people's eyes glaze over when you're talking to them trying to connect all of these dots. Pictures just hit a lot nicer for them."

**Clarity, both technical and business-related**: Enhanced observability across the entire data platform has reduced cognitive load through centralization and automation, with the Data Firehose at the center. Big Cartel now enjoys:\
● Proactive issue detection instead of waiting for dashboards to break.\
● Consolidated access to information without navigating multiple systems.\
● User trust in data, thanks to consistency in business data queries, regardless of timing.\
● Better institutionalization of knowledge across teams.\
● A clear roadmap for data capabilities that sets appropriate expectations.\
● Improved reliability with monitoring for pipeline failures and Snowflake issues.\
● A local development environment that streamlines testing and deployment.

**Future Dagster features to be implemented**: Patrick says that Dagster helped Big Cartel build a strong foundation for future capabilities, like customer-facing analytics as part of the Big Cartel product offering. The roadmap also includes implementing a data quality layer, and a Tableau integration for managing extract schedules: "I'd like to use that to put that last mile piece of the picture into the graph."

> Everybody wants to use data and everybody wants to have their questions answered, and that’s exactly what Dagster does. You don't have to go to a bunch of different places to get these things. When you ask somebody to pull some data for you, you don't get a mix of different numbers every single time, depending on the day that you do it. Dagster helps us institutionalize knowledge across teams.

---

## How Vanta Eliminated Data Bottlenecks with Dagster

> Dagster is a central piece of everything we do. Without it, we would not have been able to get to the level of self-service we are at today.

Source: https://dagster.io/customers/vanta-case-study
Published: 2025-05-22

[**Vanta**](https://www.vanta.com/) is a leader in trust management and compliance automation, empowering more than 10,000 companies to efficiently achieve and maintain critical security and compliance standards. Adopting Dagster as their modern data orchestration platform helped Vanta drastically reduce data delays, streamline their on-call operations, and significantly expand self-service for developers, data engineers, and analysts.

> Dagster is a central piece of everything we do. Without it, we wouldn’t have been able to get to the level of self-service we are at today.  - Jake Peterson, Head of Data & Analytics, Vanta

| Metric             | Before                    | After                          |
| ------------------ | ------------------------- | ------------------------------ |
| Data freshness     | 7 hours latency           | <30 minutes latency            |
| Troubleshooting    | Multi-tool manual process | Single-click reruns            |
| On-call efficiency | Frequent escalations      | Independent resolutions        |
| Integration speed  | 2-4 weeks                 | <1 week per custom integration |

### Improving developer experience and on-call efficiency with a modern data orchestration platform

The compliance journeys of Vanta’s customers vary in complexity and often span months or years. Vanta uses data to support their customers throughout their journeys, as well as support internal teams including product, sales, and marketing with critical insights.

The Vanta data team is responsible for a large footprint, including supporting a fast-shipping engineering organization. With such a strong focus on delivery, it’s no surprise the data team sought out a solution to increase the efficiency and self-service capabilities of their data platform.

### Challenges of a fragmented stack

Prior to Dagster, the team was experiencing the frustration that comes with cron scheduling across multiple data tools and systems. Their reliance on Snowflake, dbt, multiple ETL providers, and a growing number of additional data sources underscored the need for a central orchestration solution. Several key problems also inspired their search:

**Lack of unified programming model.** The absence of a unified programming model prevented the team from being able to adopt the software development practices needed to extend self-service capabilities to developers and analysts.

**Frequent after hours escalations.** At Vanta, data analysts also participate in on-call shifts, but diagnosing business-critical data errors required complicated runbooks outside of their immediate expertise, resulting in frequent escalations.

**Lengthy troubleshooting.** Alerts were scattered across multiple tools and troubleshooting data errors often required traversing 6 or more tools to find the root cause.

**Difficulty adding new integrations.** Vanta’s ETL providers offered limited integration support. The team frequently built custom integrations using AWS Step Functions–a process that took up to 2 weeks and added significant infrastructure complexity.

> We were looking for an orchestration solution that could give us a single pane of glass for managing daily and on-call operations – and would allow anyone across the team to self-serve. - Jake Peterson

### Why Vanta chose Dagster

After evaluating various solutions, Vanta selected Dagster for its unified programming model, central visibility, and ability to integrate with their existing data stack while providing a foundation for future growth. As heavy users of dbt, the dbt integration was especially important, and the team appreciated its near-instant setup.

> We had slotted weeks to do our dbt integration with Dagster. And then we spun it up and it was one button and we were done. - Jake Peterson

The migration to Dagster was executed thoughtfully in three phases:

- Initial proof of concept and detailed planning
- Sunset legacy systems, minimizing operational disruptions
- Fully centralized orchestration, achieving a single pane of glass

The Dagster support team provided implementation support throughout, ensuring the transition went smoothly. Business users reported the migration went virtually unnoticed.

> The excellent support was an incredibly pleasant surprise and has felt like a true extension to our team, especially during implementation. - Jake Peterson

### The results of a modern data orchestration platform

**Central management of a growing stack.** Because Dagster sits at the center of their work, the data team can seamlessly support a growing number of pipelines, integrations, and data sources without losing central visibility and control.

**Streamlined time to insight.** Business data freshness significantly improved, cutting latency from 7 hours to less than 30 minutes for critical go-to-market systems like Salesforce.

> We’ve moved business-critical data freshness from 7 hours down to 30 minutes or faster, significantly impacting decision-making speed for our go-to-market teams. - Jake Peterson

**Minimized troubleshooting time.** Previously, troubleshooting required navigating multiple tools and performing complex manual retries. Dagster centralized alerts and enabled single-click retries.

> Tracing data issues used to involve clicking through 6 different tools. Now we can instantly rerun and check processes in Dagster, dramatically speeding up troubleshooting and getting insights to business partners sooner. - Jake Peterson

**Reduced escalations.** Dagster reduced operational burden by empowering data analysts to independently manage most incidents during on-call shifts, decreasing the escalation to senior engineers.

> Our analytics team can now confidently manage on-call incidents, significantly reducing off-hours escalations. - Jake Peterson

**Reduced time to new data sources and integrations.** Dagster's out-of-the-box integrations were near-instant for the team to set up. Integrations that previously required extensive custom development and weeks or months of effort are now operational in a week or less with Dagster. Similarly, new data sources can be spun up as needed without losing visibility and control.

> One of the biggest benefits of Dagster is being able to spin up new integrations quickly. We write Python code, test it quickly, and have immediate confidence it’ll work without worrying about boilerplate or infrastructure complexity. - Adam Barber, Data Engineering Manager, Vanta

**Expanded self-service.** Since implementing Dagster, the team has assisted with projects across the organization that would have taken twice as long and previously required a senior engineer. Now they can do the same projects in half the time with junior developers leading important initiatives.

> We recently measured our engineering velocity—a major initiative. Without Dagster, it would’ve taken twice as long. Instead, a junior developer delivered it rapidly. The exact ROI is tough to quantify, but the productivity impact is enormous. - Jake Peterson

**Decreased spend and reliance on legacy tools.** Dagster now handles all of the extract and load for data at Vanta, giving them greater control and visibility than they had through dedicated ETL providers. Additionally, they were able to save substantial spend by migrating from dbt Cloud to Dagster.

> Why use an ETL provider when we get no insight into what's going on? We can run it through Dagster with our own code–and test it–and have complete visibility throughout the process. - Adam Barber

**Improved adoption of best practices**. Dagster’s local development experience and support for software development workflows has enabled the team to improve adoption of best practices like unit testing and branch deployments that were previously hindered by their stack. Additionally, they’ve been able to replace their cron-based system with declarative workflows and sensors.

> With Dagster, we've moved away from cron to declarative orchestration where things run when they need to run and when the data is ready. - Jake Peterson

### Future opportunities

Encouraged by their success with Dagster, Vanta plans to expand the possibilities enabled by the platform, including automated testing in CI. They’re also working closely with the team to explore deeper integration with Sigma, particularly for high-traffic workbooks, and OpenAI.

---

## StashAway Unified Their Global Data Platform with Dagster

> Our team is lean, but Dagster lets us handle thousands of assets at high velocity—we couldn’t have done that with any other orchestration platform.

Source: https://dagster.io/customers/stashaway-case-study
Published: 2025-05-19

[**StashAway**](https://www.stashaway.sg/) is a digital wealth management platform guiding investment strategies for customers across five global markets. They rely on Dagster to orchestrate thousands of data assets across a complex, multi-region Kubernetes environment, enabling them to scale operations efficiently without increasing overhead.

> Dagster dramatically increased our team's velocity. Tasks that previously took us one or two days in Airflow now take about an hour, enabling our team to effectively manage a complex, multi-region infrastructure. - Jonathan Phoon, Lead Data Engineer

### Key Results

- 50% increase in SLA compliance (from unpredictable daily misses to consistent compliance)
- 50% data warehouse savings (migration from Redshift to Databricks, enabled by Dagster)
- 95% faster pipeline creation (from 1–2 days to \~1 hour with custom tools and Dagster)
- Fragmented views across 5 regions → single pane of glass for end-to-end lineage, complete observability, and simplified troubleshooting

### The Stashaway Data Engineering Team

![Jonathan Phoon, Lead Data Engineer at Stashaway](https://dagster.io/webflow-assets/e73ccd48446976a114c7.png)

[**Jonathan Phoon**](https://www.linkedin.com/in/jonphoon) is a Lead Data Engineer with a passion for building robust data pipelines. A self-taught software engineer turned data specialist, he now leverages modern tools like Dagster to streamline workflows and enhance data reliability. His journey reflects a deep commitment to continuous learning and innovation in the tech space.

![Bolin Zhu, Data Engineer at Stashaway](https://dagster.io/webflow-assets/20c120443d32f6a01cb9.png)

[**Bolin Zhu**](https://www.linkedin.com/in/bolin-zhu-010aa0148) is a Data Engineer who thrives on turning chaos into clean, reliable pipelines. He brings clarity to complex systems—and is statistically likely to be the best badminton player in any room full of data engineers.

### Improving data reliability and developer velocity

StashAway migrated from Airflow to Dagster in 2022, and they’ve been a fast adopter of Dagster’s platform innovations ever since.

From adopting software-based assets to automating pipeline creation to centralizing data operations across 5 regional instances through a unified data control plane, StashAway relies on Dagster for the reliability and scalability of its fast-growing data platform without scaling its team size.

Before Dagster, StashAway’s Airflow setup lacked basic software practices like testing environments, unit tests, or version control. With pipelines running independently in five separate global regions, troubleshooting relied entirely on manual Slack alerts, creating frequent delays and confusion about issue severity and urgency.

### Phasing out inefficiencies by scaling up innovation

StashAway first chose Dagster for its unified dashboard and sophisticated alerting capabilities. Since then, they’ve relied on Dagster as a critical component in scaling their data platform to meet growing demand–without growing team size.

#### Adopting software-defined assets

The StashAway team initially used Dagster with dbt Cloud for data orchestration, but workloads were decoupled and required manual scheduling with unpredictable results.

When Dagster first introduced software-defined assets, StashAway eagerly adopted it as a way to accelerate developer velocity and reliability–and simplify their tooling.

Software-defined assets allow Stashaway to:

- **Cut costs** by migrating off dbt Cloud and leveraging Dagster’s native dbt integration with auto-materialization to eliminate scheduling gaps.
- **Improve reliability and stability** with auto-materialization and extra assurance from asset-based freshness checks, enabling them to meet their SLA targets.
- **Adopt sound testing practices** by treating their data workflows like software development workflows.
- **Accelerate developer onboarding**, by enabling junior team members to immediately contribute due to Dagster’s clear documentation, structured asset definitions, and community resources.

#### Accelerating development with CLI Automation

Lead Data Engineer Jonathan quickly took advantage of the efficiencies available with software-defined assets, building a custom CLI scaffolding utility to help them onboard new data sources. The utility automated new data pipeline creation, providing interactive database selection, schema discovery, and deployment to Dagster. The CLI reduced setup time from days to roughly one hour, significantly accelerating new integrations and developer onboarding.

> "Previously setting up a new data pipeline would take a day or two—now it takes about an hour." - Jonathan Phoon, Lead Data Engineer

#### Achieving end-to-end lineage across 5 Kubernetes clusters

StashAway operates 5 different Kubernetes clusters across the globe. As such, pipelines were monitored through five separate regional instances, causing significant friction during troubleshooting and a lack of end-to-end lineage.

Bolin Zhu, Data Engineer, described the setup:

“We were inspired by the concept of Dagster agents. We used that concept to build our own custom run launcher. We have a centralized Dagster web server that talks to five regional agents. The agent launches an in-memory instance that kicks off runs within those clusters.” In doing this, the team achieved an impressive result: end-to-end lineage for all pipelines across 5 regions.

> "When something went wrong, we had to open five different tabs. It was unnecessary friction. Now we have true end-to-end lineage and a single pane of glass to manage all pipelines." - Bolin Zhu, Data Engineer

#### Adoption of Software Engineering Best Practices

Despite the growing complexity of their data platform, StashAway’s team remains lean and efficient by adapting their data workflows to align with the software development best practices, made possible with Dagster.

The team now tests at every step, running unit tests and validating pipelines locally with Dagster before pushing changes to staging–and then confidently to production.

They’ve also standardized their development environments, using tools like Nix for dependency management, poetry for Python packages, and devenv for setup. When a new engineer joins the team, they can quickly spin up a reliable workspace without guessing or troubleshooting.

#### Saving on data warehouse costs

Dagster’s clear lineage and granular orchestration allowed StashAway to identify which workloads needed to run, significantly cutting compute waste. This directly enabled a smooth migration from Redshift to Databricks, resulting in a 50% reduction in warehouse spend.

> "We dropped about 50% in warehouse costs when migrating from Redshift to Databricks, enabled by Dagster." - Jonathan Phoon, Lead Data Engineer

### An ever-evolving data stack with Dagster at the center

StashAway implemented Dagster over time, progressively introducing software-defined assets, automating pipeline creation, and ultimately consolidating regional instances into a single, unified control plane. Jonathan summarized the current state of their experience:

"Dagster allows our lean team to manage our multi-region infrastructure with ease. Its intuitive logging, debugging, and retry capabilities have drastically simplified operations, freeing us to focus on higher leverage work."

Highlights for the team include:

- **Multi-region visibility**, consolidating 5 separate Kubernetes clusters into one centralized Dagster dashboard for unified observability and end-to-end lineage.
- **95% faster pipeline onboarding**, reducing setup from days to approximately an hour with automated pipeline scaffolding tools.
- **Reliable SLA compliance**, ensuring daily data readiness through proactive data quality checks, automatic retries, and improved troubleshooting capabilities.
- **Simplified issue identification and remediation**, using Dagster’s intuitive UI and clear dependency visualization to quickly isolate failures.
- **Enjoyable developer workflows**, with clear reproducible environments and thorough testing at all stages.
- **Improved team productivity and onboarding**, enabling junior engineers to immediately contribute through streamlined workflows and comprehensive documentation.
- **Reduced tool spend**, eliminating dependency on external tools, and allowing for migration to lower-cost options through Dagster’s comprehensive functionality.

### Dagster powering StashAway’s long-term data strategy

Dagster has become foundational to StashAway’s data strategy, establishing trust with data consumers through consistency and reliability. Future integrations planned include Sling and further exploration of SQL Mesh.

> "Our team is lean, but Dagster lets us handle thousands of assets at high velocity—we couldn’t have done that with any other orchestration platform." - Jonathan Phoon, Lead Data Engineer

---

## Three Weeks, 140 Iterations: How Group 1001 redefined their data platform

> Group 1001 used Dagster to rapidly iterate and deploy a robust data platform—140 iterations in just three weeks.

Source: https://dagster.io/customers/group-1001-case-study
Published: 2025-03-17

### With Dagster® powering the data platform, the Group 1001 Innovations team is transforming the business at remarkable speed.

For decades the insurance industry has sought to eliminate the high volume of paperwork and bureaucracy related to the specialized processing of annuities transactions. Yet most companies in the industry are only making slow progress. Group 1001 is bucking the trend, having cracked the code on how to digitize one of the most resource-intensive parts of the business. This transformation is due, in large part, to the work of Gu Xie and the Group 1001 Innovations team, who built a core component of their data platform around Dagster®.

#### About Gu Xie

![Gu Xie, Head of Data Engineering at Group 1001](https://dagster.io/webflow-assets/050615a7891d3dac05e5.jpg)

**Gu Xie** Head of Data Engineering at Group 1001

[**Gu Xie**](https://www.linkedin.com/in/guxie/) is the Head of Data Engineering at Group 1001 focusing on innovations - data enablement that accelerates the insurance organization's ability to execute its strategy. He shares his perspective on Data Engineering on [**Idea to Insight**](https://www.ideatoinsight.com/). Gu's passion is building nimble yet highly reliable data platforms to accelerate an organization's path from vision to execution.

#### Building toward the Future

Today, data is empowering and accelerating the business at Group 1001. But this was no accident. This forward-looking organization accelerated its pursuit of modern technology to handle vast amounts of information, scale more effectively, and develop more reliable and dynamic data.

Fast forward to today. With the adoption of Dagster® and other key technologies, the Company has evolved exponentially. The data engineering team has an even stronger handle on managing the data and has progressed to leveraging human-centered AI and machine learning to assist its employees who power the business.

Group 1001 adopted the Dagster® platform at an opportune time. In 2023, Group 1001’s business increased significantly, and concurrently, the company’s workload increased. With an efficient data platform in place, they were able to maintain optimal efficiency.

### Growth: A Nice Problem to Have?

“You wouldn’t believe the number of PDF files that come into a large insurance organization,” says Gu, “The insurance business is still highly dependent on PDFs. Typically, someone would have a PDF on one screen and a data entry system on another screen, and manually enter the data. We automated all of that.”

Today, Group 1001 is using human-centered AI to read, perform [**Optical Character Recognition**](https://cloud.google.com/use-cases/ocr) (OCR), and define entities in these documents with processes orchestrating the entire end-to-end workflow. The company is using a combination of human-centered AI and ML models to support [**Robotic Process Automation**](https://en.wikipedia.org/wiki/Robotic_process_automation) (RPAs) for thousands of documents every day.

Gu provides a specific example. [**A 1035 Exchange**](https://www.investopedia.com/terms/s/sec1035ex.asp) is a standard insurance industry process for moving funds from one organization to another. Despite advances in OCR, handling a 1035 Exchange typically requires manual intervention and verification. At Group 1001, it is automated. To achieve this, Group 1001 built a custom Robotic Process Automation (RPA) system into their data platform in record time.

“We kicked off a new process in early December of 2023. Just three weeks later, using Dagster®—after 140 iterations—we deployed our custom RPA for the 1035 transfers. Three weeks!”

Within hours, the team processed thousands of transfers.

"We built all this including, using Dagster®, and our engineering resources.

#### The Power of a Platform Approach

The data analytics team also delivered the analytics needs of the business in record time. They mapped key business processes, set up ingestion and transformation, and deployed new dashboards to the business in just two days.

“That’s when we realized the speed we could achieve. It was a real ‘Ah-ha’ moment for the business. What used to take months could now be done in days. That opened the door to projects like custom RPAs, human-centered AI-driven workflows, and even migrating our legacy systems. The team took on the challenge to migrate off large legacy systems in just four months, with two developers.”

*“The velocity we’ve achieved is insane. And it’s not just about speed—it’s about making sure everything we build is maintainable.”*

“We replicated over hundreds of SQL Server tables, migrated tens of millions of records, and built observability around the migration pipeline—all with minimal issues and just two developers,” says Gu, “The velocity we’ve achieved is insane. And it’s not just about speed—it’s about making sure everything we build is maintainable. Crushing technical debt is a priority for us, so we don’t get weighed down by it later.”

#### Org Design Matters

The data team at Group 1001 doesn’t just manage individual pipelines or stand-alone disconnected processes; they have established a true platform approach. There is another key to Group 1001’s success: organizational and team design.

Large enterprises typically aren’t designed for velocity because they lack ownership and accountability, says Gu. At Group 1001, teams own the outcome. Employees deliver reliable data products while prioritizing long-term maintainability and automation to keep that velocity going.

Gu’s team has grown from four employees to twelve - six data engineers, six data analysts. Across the entire organization, there are several data teams working on different projects. Dagster® will be the heart of collaboration across these data teams.

“Data is a multiplayer experience. Everything you produce—whether it’s a report, a table, or a model—is going to be consumed by someone else. Dagster® helps us collaborate by providing a technical interface, while other technologies will handle the functional interface for data discovery and trust.”

In organizations where there’s separation between data engineers and analysts, Gu typically sees significant friction. Problems get lobbed back and forth between teams, which slows everything down. At Group 1001, data engineering and BI teams work collaboratively to drive innovation.

“We pair data engineers and analysts in pods, so they collaborate directly. That removes a lot of friction. The engineer provides the high-quality data, and the analyst models it. It’s outcome-driven, and it works.”

The secret to moving as fast as Group 1001 does comes down to organizational design, says Gu.

“We align teams around outcomes, crush technical debt, and focus on automation. It’s about removing barriers and prioritizing speed without sacrificing quality.”

### Achieving High Development Velocity

“I’ve built data platforms before, so I had known the importance of creating a platform for scale. You have to design for velocity from the start, automate concerns like governance and deployment, and remove friction for development teams. That's how you enable a culture of speed.”

The key is to remove friction and allow teams to focus on delivering value. When you do that, the whole organization benefits.

The opinions expressed herein are those of Gu Xie and not necessarily those of his employer.\
©2024 Group 1001 IP Properties, LLC | Group 1001. All Rights Reserved

---

## Routing LLM prompts with Dagster and Not Diamond

> Improve accuracy and cost-efficiency in LLM workflows by routing prompts with Dagster and Not Diamond's orchestration strategy.

Source: https://dagster.io/customers/routing-llm-prompts-with-not-diamond
Published: 2025-02-14

When developing an AI application, you've likely encountered these common challenges:

- Which model is best suited for my specific needs?
- How can I be certain my application is functioning as intended?

The inherent non-determinism of Large Language Models (LLMs) and the numerous options available make it essential to thoroughly evaluate and test each model to ensure production-ready performance. However, even the most effective model overall might struggle with certain edge cases, while other models might offer significant cost savings without compromising performance for most inputs.

A promising solution to this dilemma is to employ an LLM router, which can learn to dynamically select the optimal LLM for each query, thereby enhancing accuracy by up to 25% and reducing both inference costs and latency by a factor of 10.

### How model routing works

When analyzing any dataset distribution, it's uncommon to find a single model which consistently outperforms all others across every possible query. Model routing addresses this by creating a "meta-model" that integrates multiple models and intelligently determines when to utilize each LLM. This approach not only [surpasses the performance of any individual model](https://docs.notdiamond.ai/docs/benchmark-performance) but also reduces costs and latency by strategically employing smaller, more economical models when performance won't be compromised.

### Routing in a Dagster pipeline

You can now add LLM routing to your Dagster pipelines with Not Diamond by instantiating a `NotDiamondResource` . Specifically,

- create an `asset` with your desired routing options and the prompt in question,
- pass the recommended model to your usage of the LLM provider of your choosing,
- then materialize that asset as needed

Let's walk through a simple pipeline example.

First, we'll define an asset representing a hypothetical dataset called `book_review_data` providing a list of book reviews for our favorite book, "Cat's Cradle" by Kurt Vonnegut.

```python theme=classic
import time

import dagster as dg
import dagster_notdiamond as nd
import dagster_openai as oai


@dg.asset(kinds={"python"})
def book_review_data(context: dg.AssetExecutionContext) -> dict:
    data = {
        "title": "Cat's Cradle",
        "author": "Kurt Vonnegut",
        "genre": "Science Fiction",
        "publicationYear": 1963,
        "reviews": [
            {
                "reviewer": "John Doe",
                "rating": 4.5,
                "content": "A thought-provoking satire on science and religion. Vonnegut's wit shines through.",
            },
            {
                "reviewer": "Jane Smith",
                "rating": 5,
                "content": "An imaginative and darkly humorous exploration of humanity's follies. A must-read!",
            },
            {
                "reviewer": "Alice Johnson",
                "rating": 3.5,
                "content": "Intriguing premise but felt a bit disjointed at times. Still enjoyable.",
            },
        ],
    }
    context.add_output_metadata(metadata={"num_reviews": len(data.get("reviews", []))})
    return data
```

Our goal is to generate a summary of these reviews in a cost effective way through LLM routing.

So we'll define another asset, `book_review_summary` which uses the `NotDiamondResource` for model routing, and the `OpenAIResource` for completion. We call invoke the `model_select` method from our Not Diamond resource passing in our summarization prompt, the subset of models that we want to consider, and the parameter `tradeoff="cost"` parameter to optimize for cost savings.

This call returns a `best_llm` variable, which we can then pass to OpenAI in our usage of the `chat.completions.create` method.

```python theme=classic
@dg.asset(
    kinds={"openai", "notdiamond"}, automation_condition=dg.AutomationCondition.eager()
)
def book_reviews_summary(
    context: dg.AssetExecutionContext,
    notdiamond: nd.NotDiamondResource,
    openai: oai.OpenAIResource,
    book_review_data: dict,
) -> dg.MaterializeResult:
    prompt = f"""
    Given the book reviews for {book_review_data["title"]}, provide a detailed summary:

    {'|'.join([r['content'] for r in book_review_data["reviews"]])}
    """

    with notdiamond.get_client(context) as client:
        start = time.time()
        session_id, best_llm = client.model_select(
            model=["openai/gpt-4o", "openai/gpt-4o-mini"],
            tradeoff="cost",
            messages=[
                {"role": "system", "content": "You are an expert in literature"},
                {"role": "user", "content": prompt},
            ],
        )
        duration = time.time() - start

    with openai.get_client(context) as client:
        chat_completion = client.chat.completions.create(
            model=best_llm.model,
            messages=[{"role": "user", "content": prompt}],
        )

    summary = chat_completion.choices[0].message.content or ""

    return dg.MaterializeResult(
        metadata={
            "nd_session_id": session_id,
            "nd_best_llm_model": best_llm.model,
            "nd_best_llm_provider": best_llm.provider,
            "nd_routing_latency": duration,
            "summary": dg.MetadataValue.md(summary),
        }
    )
```

Finally, we return a `MaterializeResult` with the metadata from our call to both Not Diamond and OpenAI.

Once we materialize these assets we can see the summary of our book reviews, along with the otherassociated metadata!

- `session_id` which we can use in subsequent routing requests,
- `best_llm` as recommended by Not Diamond, and
- `routing_latency` for time (in seconds) taken to fulfill the request.

![Dagster global asset lineage](https://dagster.io/webflow-assets/a4a0af07d05b843150af.png)

Here we demonstrated how perform model routing, but it's worth noting that Not Diamond also supports automatic routing through their model gateway! You can find an example of that in our recent [deep dive presentation](https://youtu.be/iwAhzS4EMkw?feature=shared), and in the [community-integration](https://github.com/dagster-io/community-integrations/blob/3f6374b5a9038266e0ca7b2bdad195e51b528ebb/libraries/dagster-notdiamond/examples/example_book_reviews_summary_proxying.py) repository.

### Routing prompts in complex workflows

You can expand this pipeline in various ways:

- Try submitting dynamic prompts to Not Diamond from previous pipeline nodes or even your data,
- Explore the model gateway to automatically route across LLM providers: OpenAI, Anthropic, Gemini, and more!
- Combine both of the above with [Dagster Pipes](https://docs.dagster.io/integrations/external-pipelines) to build a fully-automated workflow which leverages generative AI for your custom business logic.

### Conclusion

To try out this example or add Not Diamond’s state-of-the-art routing to your Dagster pipelines, [sign up](https://app.notdiamond.ai/?utm_source=wandb\&utm_medium=article\&utm_campaign=weave-not-diamond) to Not Diamond to get your API key and [read the docs](https://docs.notdiamond.ai/docs/what-is-not-diamond) to learn more about adding LLM routing to your AI workflows.

---

## Case Study: Analytiks - Fast-Track AI Projects With Managed Dagster+

> Analytiks used Managed Dagster+ to accelerate enterprise AI initiatives and data infrastructure setup.

Source: https://dagster.io/customers/analytiks-case-study
Published: 2024-11-13

As an [open-source project](https://github.com/dagster-io/dagster) backed by a [commercial platform](https://dagster.io/lp/dagster-plus-trial), Dagster's success relies heavily on our [network of implementation partners](https://dagster.io/company/partners) who help organizations of all sizes harness the power of modern data orchestration. Among these partners, Analytiks has developed a particularly innovative approach that's extending enterprise-grade data tools to previously underserved market segments.

### Revolutionizing Data Solutions for Growing Businesses

![](https://dagster.io/webflow-assets/9cb4b7a6a1e3741e13b1.jpg)

Mathieu Stark          Founder and CEO at Analytiks

When Mathieu Stark founded Analytiks, he noticed a recurring pattern among small and medium-sized businesses: they needed sophisticated data solutions but lacked the resources to implement and maintain enterprise-grade tools. "We work with everyone, from Fortune 500 companies to startups that need to move away from spreadsheets into more sophisticated data systems,"      

Stark explains. "We set up data pipelines, data warehouses, build complex data models, and integrate all our client's data into a unified platform. This transformation unlocks significant business value - from accelerating time-to-insight by 70% through automated reporting to enabling real-time decision-making with trustworthy data."      

"But perhaps most critically, this robust data foundation becomes the launchpad for AI initiatives," Stark continues. "When companies want to leverage generative AI effectively, they need clean, well-organized data that is properly governed. Our unified platform approach means clients can seamlessly feed quality data into their LLM applications, whether they're building intelligent customer service chatbots, automated content generation, or predictive analytics systems. We're seeing clients reduce AI implementation time by 60% because they're not struggling with data preparation and governance - the foundation is already there."

### The DAD Stack: Enterprise Features at Scale

Analytiks has developed a unique solution to this challenge. As a Dagster partner and Dagster+ customer, they've created a model that makes our [enterprise features](https://dagster.io/enterprise) accessible to organizations of all sizes. Their pre-packaged solution centers around what they call the "DAD stack" - Dagster+, Airbyte, and dbt. This combination leverages Dagster+'s powerful metadata tracking and lineage capabilities to provide complete visibility into data flows, while advanced monitoring ensures reliable pipeline operations across the entire stack.

Their innovative approach handles access to Dagster+ features. Rather than requiring clients to sign their own enterprise contracts, Analytiks runs Dagster+ and makes it available to clients through their own instance. "Think of it as a way to get enterprise features at a fraction of the cost," Stark says. "Our clients can start small, pay monthly, and scale up as needed. There's no need to commit to a large annual contract right away."

Their implementation approach showcases many of Dagster+'s key capabilities. The platform provides end-to-end data pipeline visibility, real-time job status monitoring, automated error detection, and comprehensive data lineage tracking. Dagster+'s asset-based approach ensures teams can easily understand data dependencies and impact analysis. At the same time, its metadata system automatically documents data transformations and maintains a clear record of how data evolves throughout the pipeline. This level of observability and control enables Analytiks to maintain high reliability while quickly identifying and resolving any issues.

### Comprehensive Support and Future Growth

The power of this technical foundation becomes most apparent in how Analytiks delivers ongoing support to their clients. "With Dagster+'s lineage capabilities, we can instantly trace any data point back to its source," Stark explains. "If a client notices discrepancies in their reports, we can quickly trace the entire data path and identify exactly where issues might have occurred. This level of observability is transformative for troubleshooting and maintaining data quality."

The advanced monitoring capabilities of Dagster+ have also transformed how Analytiks provides ongoing support. Their team leverages Dagster+'s alerting system to maintain high service levels across all client deployments. Custom alerts notify them of potential pipeline delays, data quality problems, or resource constraints, allowing them to address problems quickly. This comprehensive support, powered by Dagster+'s enterprise features, often costs much less than hiring a single full-time employee while providing access to a much broader range of expertise.

The model has proven particularly valuable for organizations dealing with complex data challenges. Even when working with larger enterprises, Analytiks adapts their approach to meet specific infrastructure requirements while maintaining the same principles of clear organization and ongoing support.

As we continue to expand Dagster's ecosystem, partners like Analytiks play a crucial role in making our tools accessible to a broader range of organizations. Their approach demonstrates how innovative deployment models, combined with Dagster+'s advanced features, can help more teams benefit from enterprise-grade data orchestration while ensuring proper implementation, documentation, and ongoing support.

[Contact the Analytiks team today!](https://analytiks.co/contact-us/)

---

## Case Study: From Disconnected Data to a Unified Platform

> Built-in observability and cataloging let this team open data access to a wider range of data professionals.

Source: https://dagster.io/customers/servco-case-study
Published: 2024-11-12

Founded in 1919, Servco has earned a distinguished reputation for service, innovation, and a steadfast commitment to customer-focused excellence. Based in Hawaiʻi, Servco, originally founded as a two-car garage on the North Shore of Oʻahu, has now diversified into various industries, including automotive, musical instruments, industrial equipment, and venture capital. Servco has grown to become the largest Toyota Group in Australia and also owns Fender Musical Instruments Corporation.

[Tausif Islam](https://www.linkedin.com/in/tausif-islam-05502561/) and [Sam Ikemoto](https://www.linkedin.com/in/samuel-i-09a4a06b/) are data engineers within Servco’s Hawaiʻi Automotive business. They shared insights into their journey from traditional BI to an evolved, code-first data platform that opens access to reliable data for all data professionals across the organization.

![Tausif Islam](https://dagster.io/webflow-assets/dbaacf8c2b4d38640b18.jpg)

[Tausif Islam](https://www.linkedin.com/in/tausif-islam-05502561/) - Director of Data Visualization, Analytics and Automation

![Sam Ikemoto](https://dagster.io/webflow-assets/f8eadf09cbf572b2c427.jpg)

[Sam Ikemoto -](https://www.linkedin.com/in/samuel-i-09a4a06b/) Business Intelligence Engineer

Their work showcases the challenges and triumphs of moving from on-premise, outdated systems to a fully integrated cloud-based environment. Below, we capture some key takeaways for data practitioners.

### The Challenge: Legacy Systems and Limited Data Accessibility

Servco’s Hawaiʻi retail and distribution business spans all the islands. Until recently, it was grappling with challenges common to many traditional organizations: disconnected data, limited accessibility, and tribal knowledge concentrated in the hands of a few. Their systems, built on an on-premise SQL server, were difficult to access, with poorly documented tables and outdated data dictionaries.

This legacy setup created bottlenecks in delivering insights, and the lack of accessible documentation meant that analysts frequently had to rely on the engineering team for data interpretation and access.

"It was always just us doing the reporting work," Tausif noted, highlighting the pressure on the data team and analysts' difficulty in using data autonomously.

### The Solution: Moving to a Cloud-Native, Self-Service Data Model

The transformation began with Servco's decision to move to a cloud-native platform. Tausif and Sam opted for Google Cloud's BigQuery and implemented Dagster, a modern orchestration tool, to replace their legacy SQL server processes. The choice of Dagster was driven by its developer-friendly, Python-first approach and its focus on assets and data lineage—crucial features that would help make data more transparent and accessible.

Dagster's [data catalog](https://dagster.io/platform-overview/data-catalog) capabilities were pivotal to their new approach. "We’re not just providing data anymore; we’re also providing context," Tausif explained. The data team started building semantic models, documenting tables and data lineage to provide analysts with a clearer view of what the data represented, how it was derived, and how it could be used.

This allowed Servco to create a self-service culture where analysts could pull data directly into Excel, Google Sheets, or Tableau without needing to go through the engineering team. As Tausif put it, "We want to provide clean, high-quality data in the most usable and secure format. We’re less focused on what tool our analysts use."

### The Impact: Enabling True Self-Service

The new platform had a significant impact. Instead of gatekeepers, the data team became enablers, helping analysts access well-documented, trusted data directly. This change improved efficiency and enhanced data usage across the organization.

Sam added, "I’m interested to see how things pan out as the models grow. But right now, having lineage and everything in one spot makes my life much easier."

Integrating data cataloging features into Dagster also significantly helped analysts trust and leverage data. "We’re not just a vault of tribal knowledge anymore. People are trusting our semantic models because they’re verified by different departments like finance and auto retail," Tausif shared. The data team also trained 30+ users on using Dagster effectively, aiming for wide adoption among their finance and accounting teams.

### Lessons for Fellow Data Practitioners

**1. Choose the Right Orchestrator:** Servco evaluated other tools but opted for Dagster due to its focus on assets and comprehensive data catalog features. If the orchestrator can also support documentation and lineage, it becomes far more powerful for business users.

**2. Semantic Models Are Key:** Building reusable, well-documented semantic models saves time and reduces redundancy. The data team took a customer-led approach and built models that could serve multiple business units, increasing trust and quality.

**3. Unified Tools for Efficiency:** Having everything—from documentation to orchestration—in one tool significantly reduced context-switching for the team. This approach allowed Sam and Tausif to focus more on adding value than firefighting.

**4. From Orchestration to Governance:** Tausif expressed his vision for Dagster to become a one-stop platform for orchestration, governance, and security, including features like data masking and policy tagging. While not yet fully realized, this vision underlines the growing importance of governance in data workflows.

### The Road Ahead for Servco

Servco’s transformation is ongoing. The team continues to migrate data sources to the cloud, slowly deprecating their on-premise systems. They’re also preparing for an increase in the number of users accessing their data platform and monitoring how their models scale.

The biggest value? "Now, we’re sharing the data," Tausif said. "Our internal customers see the value in accessing the data themselves. And Dagster has helped us make that possible by demystifying the data and making it accessible in a usable way."

---

## Case Study: KIPP - Building a Resilient Data Platform with Dagster

> A solo data engineer at KIPP built a resilient Dagster platform to drive organization-wide data access.

Source: https://dagster.io/customers/kipp-case-study
Published: 2024-10-23

### A Platform Approach for Faster Development

The [KIPP NJ](https://kippnj.org/) & [Miami](https://www.kippmiami.org/) schools are part of the nationally recognized “Knowledge Is Power Program” network of free, open-enrollment, college-preparatory public schools. The network, known as KIPP Team & Family, comprises of 19 schools serving over 10,000 students across Newark, Camden, and Miami, FL.

The network faced a challenge familiar to many growing organizations: managing a complex and fragmented data stack. Data is vital at KIPP, informing everything from student progress and attendance to operational finance and HR. However, with different systems spread across multiple regions, scaling data management became a considerable task.

As [the sole data engineer for KIPP's Newark, Camden, and Miami regions](https://sites.google.com/apps.teamschools.org/datalaunch/team), [Charlie Bini](https://www.linkedin.com/in/cbini/) had to find a solution that could support growth, streamline data operations, and provide much-needed visibility into the data stack.

![Charlie Bini, Data Engineer at KIPP](https://dagster.io/webflow-assets/68981075e15539a98d63.jpg)

Charlie Bini - Data Engineer at KIPP

This is the story of how KIPP Team & Family Schools moved from a disparate collection of tools to a unified data platform powered by Dagster.              

In doing so, Charlie changed how the entire data team works, providing better observability and alerting, a faster development cycle, and easier self-service for team members. Processes that took ”two weeks to a month to develop” can now be done “in a couple of days," says Charlie.      

> People love the idea of what we're doing, and the vision of a cohesive data platform is something that resonates with everyone.

### The Problem: A Patchwork of Tools

Back in 2017, KIPP's data infrastructure was a tangle of multiple technologies—Python scripts, cron jobs, stored procedures, Fivetran, and even browser automation with Selenium. These tools were stitched together to extract data from various systems, including SFTP folders, APIs, and databases. Fivetran acted as the hub, ingesting data and moving it into a SQL Server for reporting. While it worked, the setup was brittle. If one step took longer than expected, the whole process would fail, and the data quality issues would be a constant challenge.

![KIPP's old data architecture before building a data platform on Dagster](https://dagster.io/webflow-assets/ac2dfd8eb8ecac65a3c2.jpg)

     KIPP's old data architecture before adopting a true platform approach.

"Everything was very synchronous," said Charlie. "If our pull from a student information system took too long, the next process would miss its tick, and we'd end up waiting until the next scheduled run." This lack of resilience not only affected data reliability but also consumed Charlie's time in debugging and firefighting.

It was clear to Charlie and the rest of the team that in order to scale and free up time to take on new challenges, they would first need to overhaul the foundation of their data processes, moving from a patchwork of systems that had evolved organically, to a fully featured data platform where key capabilities such as orchestration, metadata management, end-to-end observability, and data quality could be centralized and unified.

### Selecting the Right Orchestration Solution

KIPP experimented briefly with Airflow to improve orchestration, but it didn't provide the experience they were looking for. The complexity and the cost of running Airflow in Google Cloud Composer also made it unsustainable. Charlie knew that to scale, KIPP needed a new approach—one that would bring together all these disparate data processes in a cohesive and manageable way.

In 2021, Charlie began experimenting with Dagster, an emerging orchestrator that promised more visibility and control over data pipelines. With Dagster, KIPP could centralize all its data processes, bringing data extraction, transformation, and orchestration into a unified platform. One of the key benefits was the real-time visibility Dagster provided into pipeline health.

"The visibility into the pipelines is a game changer," Charlie noted. "We know as soon as something fails and why, and it's pretty easy to remedy it." This was a major shift from the old setup, where issues often remained undetected until downstream effects were noticed, such as outdated reports or missing data in dashboards.

### Building the Data Platform

The new data stack brought all of KIPP's data operations under a single, cohesive framework:

![KIPP's updated data architecture after migrating to a data platform built on Dagster](https://dagster.io/webflow-assets/8813e80df694e5372783.png)

     KIPP's new data architecture takes a platform approach, orchestrated by Dagster.

- **Dagster for Orchestration and Cataloging**: The core of the new data platform was Dagster. Dagster orchestrated everything—from data extraction from third-party systems to transformations and data loading into the warehouse. The platform also provided clear lineage graphs, which allowed the team to easily track the flow of data from source to report.
- **BigQuery as the Data Warehouse**: KIPP centralized its data storage in Google BigQuery, providing a scalable and performant environment for storing and querying data.
- **dbt for Transformations**: dbt was introduced to handle [transformations](https://dagster.io/glossary/data-transformation) within BigQuery, making the entire ELT process transparent and manageable with version control.
- **Containerized Ingestion with Kubernetes**: [Data ingestion](https://dagster.io/glossary/data-ingestion) scripts were containerized and run on Kubernetes, making the system more scalable and resilient.

In this new setup, Dagster also manages data quality checks, leveraging asset checks to ensure incoming data conforms to expected schemas. This setup drastically reduced the number of data quality issues, and tickets related to data problems dropped by 30% year over year.

### The Impact: Faster Development, Fewer Issues

The shift to Dagster wasn't just about technology—it changed how the entire data team worked. Data pipeline development, which previously took weeks, was reduced to just days. "It used to take two weeks to a month to develop a new integration. Now, with Dagster, we can get a proof of concept done in a couple of days," Charlie explained.

The time savings weren't just in development but also in maintaining data quality. Thanks to Dagster's real-time alerts and lineage tracking, Charlie and the team could respond to issues immediately. Slack alerts provided instant notifications when a job failed, allowing quick triage and resolution, often before downstream users even noticed a problem.

### Empowering the Team

Another significant benefit of adopting Dagster was the growth in the team's capabilities. With everything centralized in a Git repository and using pull requests for changes, the analysts at KIPP were able to participate more directly in the data engineering process. This shift brought software engineering best practices—like code reviews and DevOps concepts—to the data team, raising everyone's technical skills.

"Our team has grown skill-wise," Charlie said. "Analysts are now more proficient in SQL, Git, and understanding how the entire data stack works together."

The introduction of Dagster's data catalog feature also provided the team with a single source of truth for data assets. Analysts could trace the lineage of data, understand dependencies, and find documentation, all within Dagster. This made onboarding new team members easier and increased overall productivity.

### Conclusion: A Unified, Scalable Data Platform

By adopting Dagster, KIPP transformed its data platform from a fragmented set of ad-hoc tools into a unified, resilient, and scalable solution. The result was a data stack that not only met the organization's growing demands but also empowered the data team to work more efficiently and collaboratively.

Charlie hopes that other KIPP regions will eventually adopt a similar setup, allowing them to standardize data operations across the entire network and reduce redundancy. "People love the idea of what we're doing," he said, "and the vision of a cohesive data platform is something that resonates with everyone."

For other data teams facing similar challenges, KIPP's story serves as an inspiring example of how the right orchestrator can simplify operations, improve data quality, and ultimately help scale data capabilities to support meaningful organizational growth.

---

## Case Study: Mejuri - Building an eCommerce Data Platform

> Mejuri relies on Dagster for a robust data platform that scales with their growing ecommerce business.

Source: https://dagster.io/customers/mejuri-case-study
Published: 2024-09-04

Since 2015, Mejuri has done fine jewelry differently, making it for more than special occasions. While quality, craftsmanship, and design are at the brand’s core, their ethos goes deeper: to empower self-expression and elevate the everyday. The jewelry is designed in-house, and the company only manufactures more based on consumer demand. This allows Mejuri to offer a fresh line of jewelry at an accessible price point while eliminating wasteful overproduction.

The organization has been very successful and is enjoying rapid growth. However, sustaining such a nimble business model means always having one's finger on the pulse of the organization.

This is easier said than done.

Mejuri is a global brand with over 500 employees.  Retail operations include the [online store](https://mejuri.com/stores/hub.) along with 37 brick-and-mortar retail locations across the UK, the USA, Australia and Canada.

### The Mejuri Data Team

![Gabriela Henriques, Data Engineering Manager at Mejuri](https://dagster.io/webflow-assets/0ff7cc644bcfe1b479ba.jpg)

Gabriela Henriques is Mejuri’s new Data Engineering Manager, having joined in January 2024.

She is accountable for the data platform, working with a team of 6 engineers spanning Data Engineering and Analytics Engineering. The engineering team is mostly based out of Argentina, with the rest in Toronto.      

Together, the team oversees the current-state platform, the underlying infrastructure, and DevOps setup, as well as building future platform features and pipelines.  Finally, the team builds and maintains all business reports in Looker.

“Mejuri is a very data-driven organization, across all departments. “ says Gabriela, “Our CEO is looking at the data on a daily basis. We use data for everything from revenue and accounting reporting, as well as in marketing, customer behavior, product trends, and user behavior on our website, as well as tracking in-store activity.”

### Building a Platform on Dagster

Dagster has been running at Mejuri for over two years now. In total, the team handles 47 pipelines across two Dagster production instances.  These ingest and integrate data from both in-house systems and third-party data sources.

Like all eCommerce companies, Mejuri taps into many third-party vendors. Mejuri also has business partners and needs to effectively share data with them. In addition, they have brick-and-mortar stores. All this information needs to be aggregated and analyzed to develop a complete picture of the business.

Data ingestion is done using a mixture of Python scripts and a commercial service (previously Stitch, but transitioning over to Airbyte).

The data management architecture follows a “black-silver-gold” structure on Google BigQuery, with dbt Cloud running the transformations and Dagster orchestrating all the pipelines.  Looker is then the main BI tool.

“Dagster gives us great visibility across all the pipelines that we have running. We have alerting set up so we know the health of all the pipelines. It also makes it very easy for people on my team to spin up new ones.” says Gabriela

Gabriela inherited Mejuri’s Dagster implementation, with the technology being new to her as she had previously only worked on Airflow (via Google Composer, then self-hosted).  The transition was a smooth and positive one nevertheless.

“Dagster offers a lot more features than Airflow, and you can set up pipelines in a cleaner fashion than with Airflow.  That’s the biggest difference I noticed switching between the two.”

![A Mejuri store.](https://dagster.io/webflow-assets/f6a3e9bdb99b26324bfa.jpg)

### Data Engineering and Analytics Engineering: Finding the Right Balance of Autonomy and Collaboration

At Mejuri, as in many data-centric organizations, the Data Engineers on the team focus on building and maintaining the pipelines, while the Analytics Engineers focus on the transformations and reporting.

Finding the right balance of autonomy and collaboration between these functions can be tricky, but here too, Dagster can help.

Handing off data transformation from an analytics engineer to a data engineer is easier for Mejuri with the Dagster-dbt integration.  Analytics engineers can use the dbt Cloud IDE, and the data engineers then productionize the dbt jobs in Dagster.

Mejuri’s next steps include empowering the Analytics Engineers to operationalize these models without having to rely on the Data Engineers.  The Analytics Engineers are able to write in Python, and Dagster gives them more autonomy.

### Running on Dagster+

The Mejuri team runs two environments on Dagster+, offloading any infrastructure concerns to Dagster Labs.  This choice frees the team up to do more rapid development while reducing time spent on updates.  It also unlocks a number of new features.

The team is ramping up ML activities, with models getting integrated into pipelines. When adopting more ML processes, Dagster will help understand the resource demands of the current pipelines and adjust the infrastructure to match the new requirements.

### Looking to the Future

With a solid foundation for their data platform, the Mejuri team is now looking to the future.  They have just completed a major shift to run on Shopify.  With that project mostly completed, Gabriela sees many opportunities for further improvements.

“We are now looking at how we make it easier for Analytics Engineers to work fully independently form the Data Engineers, putting their own models into product autonomously.  We are also looking at how to improve our CI/CD process to make the deployment of new pipelines even smoother.”

With the adoption of more ML-focused pipelines and the rich insights that Dagster+ provides, the Mejuri data team continues to provide critical insights to support the organization’s continued growth.

---

## Case Study: The Lean and Efficient One-Person Data Team of Erewhon

> See how a solo engineer built a fast, efficient data stack at Erewhon using Dagster orchestration.

Source: https://dagster.io/customers/erewhon-case-study
Published: 2024-08-15

### Erewhon: Where Organic Meets High-Tech

In the age of digital transformation, data is the lifeblood of [retail](https://dagster.io/use-case/retail-e-commerce).  As retailers seek to build best-in-class digital shopping experiences, establishing streamlined and integrated data operations becomes mission-critical.

![Erewhon store](https://dagster.io/webflow-assets/3a7b6dbb22774c97bd4b.jpg)

![Erewhon store deli counter](https://dagster.io/webflow-assets/d1b436eb76e6da7b4c93.jpg)

![Erewhon store inside view](https://dagster.io/webflow-assets/3f648788c11782e6cb29.jpg)

Enter [Erewhon](https://erewhon.com/): LA's glitzy, upscale organic grocery chain that's more institution than store. Picture celebrity-curated smoothies, crave-worthy organic fare, and a cult following that demands nothing but the best. Family-owned and operated but not your average mom-and-pop shop—it's a cultural hub where organic purity meets cutting edge. Erewhon members' hunger for a premium experience has birthed a growing technology team in an otherwise “traditional” industry.

![Sean Pool, solo data engineer at Erewhon.](https://dagster.io/webflow-assets/77c448246046d23ac50c.jpg)

This case study explores how [Sean Pool](https://www.linkedin.com/in/sean-pool-2b828aa4/)— a solo data team—successfully implemented Dagster to jumpstart Erewhon’s overall data strategy and reimagine the scope of what they could do with data.      

Through adopting Dagster, Sean transitioned Erewhon rapidly from limited low-code solutions to modern cutting edge tools while up-skilling himself as a data engineer.    

> "Data engineering is essential at Erewhon, enabling us to deliver personalized experiences, accelerate business growth, and automate the boring stuff." - Sean Pool

#### The Data Challenge: Growing Pains and Ambitious Goals

Sean joined Erewhon as the first data hire, initially tasked with creating a straightforward analytics and reporting system using PowerBI. However, as the company's data needs expanded, it became clear that a more powerful and scalable solution was necessary. Erewhon's existing data infrastructure, based on low-code solutions and basic BI tools, was facing several limitations.

The current systems struggled to handle the growing volume of data from multiple sources, including legacy databases, events from the [Erewhon mobile ordering app](https://erewhon.com/shop), software APIs and more. Data silos across the organization made it difficult to gain a holistic view of the business and customer behavior, and many reporting and analytics tasks were time consuming and manual.

Moreover, Erewhon had ambitious plans to leverage data for business growth. The company aimed to implement sophisticated use cases such as data-driven app features, advanced marketing technology, personalized customer experiences, and streamlined process automation. However, the existing infrastructure fell short of supporting these initiatives effectively. Sean, drawing from his experience as an analytics user of data platforms at Fortune 500 companies, recognized the potential value these advanced systems could create. This insight became the driving force behind Erewhon's data transformation journey.

With this north star in mind, there were significant challenges: budget constraints, a lean team structure with Sean as the sole data team member, and the need to upskill from BI and analytics to more advanced data engineering concepts. Despite these limitations, the solution emerged as a carefully crafted, cutting-edge data stack. By composing the right set of tools, Sean found a way to overcome the resource constraints quickly while still delivering a powerful, scalable data platform that could support Erewhon's growing needs and ambitious data initiatives.

This transformation culminated in the implementation of a code-based system capable of handling a number of different functions. The new platform seamlessly integrated automated reporting, self-serve analytics, marketing automation, targeted advertising, mobile app feature support, systems integration, and [AI workflows](https://dagster.io/solutions/ai) leveraging internal data. Essentially, Sean had built what could be described as a "monolithic business-focused data platform."

> "The secret sauce was choosing the right tools to decrease time to value and build efficiently. For me, some of those time savers are Dagster, dbt, and dlt. These tools, combined with a little bit of Python and SQL, allowed me, as a solo practitioner, to build enterprise-grade data operations at a fraction of the cost. We ended up with a flexible, scalable system that not only meets Erewhon's current needs but also prepares us for future data challenges." - Sean Pool

![Erewhon's data engineering platform.](https://dagster.io/webflow-assets/221097e94d6f90246c91.png)

   A diagram representing the data platform architecture at Erewhon, showcasing the flow from data ingestion to processing and utilization. Click to enlarge.

#### Transitioning to High-Code Tools

The data team at Erewhon began with basic BI tools and gradually took on more complex data engineering tasks. With his background in business intelligence (BI) and finance, Sean recognized the limitations of low-code solutions for advanced data use cases. The catalyst to begin building this system was the integration of Braze, a popular data-powered marketing platform. After exploring various options, he identified a homegrown system built on Dagster as the clear ideal solution. Dagster's capabilities in streamlining data pipelines, version control, and modular declarative design aligned well with Erewhon’s requirements.

Erewhon’s data platform is deployed on Google Cloud Platform (GCP). Software engineering best practices are followed with CI/CD, and Slack bots monitor and report critical processes using Dagster’s integration.

> "As a non-engineer, at first the idea of creating a ‘code-base’ was unapproachable and scary. The first few hours and days were a struggle, but I soon gained confidence with help from the endless library of internet resources available." - Sean Pool

#### Implementation and Learning Curve

Implementing Dagster was a significant learning experience for Sean, who did not have a formal software engineering background. Sean dedicated considerable time to learning Python and advanced data engineering concepts, using resources like YouTube tutorials, AI coding assistants, and [Dagster University](https://courses.dagster.io/).

As Sean's Python skills advanced, he explored various data engineering tools, with Dagster emerging as the ideal orchestration engine. Its intuitive design, modular architecture, and innovative integrations aligned perfectly with his project needs. Sean appreciated Dagster's asset-based pipeline approach, built-in data quality checks, and refined visual UI. Despite the initial learning curve, Dagster's comprehensive documentation and supportive community facilitated a smooth implementation. This experience both solved the orchestration needs and deepened Sean's understanding of data engineering principles.

> "For years, my work was fragmented across various drag-and-drop interfaces, spreadsheets, and disconnected systems. Now, with Dagster, I can make swift adjustments and enhancements on the fly through simple code changes in one central location. Visualizing everything in the Dagster UI is a game-changer – it's truly revolutionized my workflow!" - Sean Pool

#### Achievements and Impact

This data platform approach enabled Erewhon to rapidly bring modern data strategies to the business. The system unified disparate source data from across the company, and enabled modern software tools to be easily integrated in order to put data to work. Dagster’s modular design allowed the team to add new features and use cases incrementally, ensuring the system could evolve with the company’s needs. It began as the customer data platform to power marketing integration with Braze, but after just 9 months many other functionalities and features were quickly added.

The data team also integrated AI and machine learning capabilities into Erewhon’s data pipelines, which has created many additional planned use cases.

> "I had seen how powerhouse data teams operate and deliver value at Fortune 500 companies, and wanted to bring those techniques to Erewhon on a small budget. With the right composition of tools this is more democratized than ever, and Dagster is a clear accelerant to my goals as a data practitioner." - Sean Pool

#### Overcoming Challenges

The data team at Erewhon operates in a lean environment with limited resources. Managing all aspects of the data infrastructure, from development to deployment, tested Sean’s skills and pushed him out of his comfort zone.

Not all challenges were technical. As the solo data engineer, Sean had to secure buy-in from other organizational stakeholders, who were reasonably concerned about the upfront cost of implementing a high-code data stack. From his evaluation this custom solution is magnitudes cheaper for Erewhon when compared to the recommended leading industry SaaS tools.

Sean's strategy for overcoming these challenges involved demonstrating the value of this approach through practical outcomes.

By successfully integrating the customer engagement platform Braze and automating marketing efforts, he showcased the benefits of their data platform. This helped secure the necessary support and resources to continue developing and expanding the system.

Another challenge was the learning curve associated with advanced Python concepts and the specific requirements of Dagster. He invested time in self-education, using resources like Youtube, ChatGPT, and Dagster University to build their skills and knowledge. This dedication paid off, enabling him to develop a powerful and flexible data platform.

#### Key Benefits of Dagster

Dagster is proving to be an invaluable tool for Erewhon. Its modular and version-controlled design allowed the team to build and maintain complex data pipelines efficiently. This was crucial for a lean team, ensuring that business logic was preserved and easily maintainable. Dagster's integration capabilities enabled the team to incorporate AI and machine learning tools into Erewhon's data workflows, enhancing the company’s data-driven decision-making processes.

One of the most significant benefits of Dagster was its accessibility for data practitioners from non-technical backgrounds. Sean's success demonstrates that individuals without a formal software engineering background can effectively use code-based tools like Dagster with the right resources and dedication. This democratization of data engineering tools opens up new possibilities for lean teams and organizations with limited technical resources.

Dagster also provided cost-effective solutions for Erewhon. By opting for open-source tools and avoiding expensive third-party solutions, the team kept operational costs low while managing significant data volumes. Cost efficiency was particularly important for Erewhon, and this “startup data team” needed to deliver the value of its initiatives without incurring substantial additional costs.

> "One underrated aspect of Dagster is the highly opinionated nature of the framework. I believe that as a new developer with fresh eyes, being exposed to state-of-the-art ideas and opinions on how to practice data engineering was invaluable. The recommended integrations make so much sense and simplify many things." - Sean Pool

#### Conclusion

Erewhon and Sean’s efforts show the potential of Dagster for lean data teams and data engineers from non-technical backgrounds. By adopting Dagster, Sean built a powerful and efficient data platform, driving significant business value and innovation. Sean’s journey demonstrates that individuals without a formal software engineering background can successfully leverage high-code, open-source tools to build sophisticated data solutions with the right resources and dedication.

The case study underscores the importance of these tools in democratizing data engineering and empowering lean teams to achieve their data goals. Dagster's modular design, integration capabilities, and cost-effectiveness make it an ideal solution for organizations looking to enhance their data infrastructure and drive data-driven decision-making. Sean’s success in building a data platform highlights the value of Dagster in enabling data-driven decisions, regardless of the team’s size or technical background.

---

## Case Study: Artemis - Powering the Crypto Markets

> Artemis built a Dagster-powered platform for reporting on the $2.5T crypto market.

Source: https://dagster.io/customers/artemis-case-study
Published: 2024-07-29

![Alex Kan, software engineer at Artemis](https://dagster.io/webflow-assets/824d5e6b663d402226a2.jpg)

We explore how Artemis, a premier institutional data platform for digital asset fundamentals, leverages Dagster+ to serve thousands of customers, from financial institutions to individual investors and crypto protocols.

We got to chat with [Alex Kan](https://www.linkedin.com/in/alexanderkan/), Software Engineer at Artemis, and a member of the 12-person team, half of which are heavy Dagster users, including data engineers and data scientists.      

In 2008, a paper was published called "[Bitcoin: A Peer-to-Peer Electronic Cash System](https://bitcoin.org/bitcoin.pdf)," by Satoshi Nakamoto.  Since then the world has witnessed the wild and sometimes rocky ascent of cryptocurrency.

From its early days on the fringes of the digital economy, crypto has established itself as a legitimate and significant part of the global financial system. As of 2024, there are over 22,000 different cryptocurrencies in existence, of which 9,124 are considered active [<sup>\[1\]</sup>](https://coinweb.com/trends/how-many-cryptocurrencies-are-there/). The total market capitalization for the cryptocurrency sector now [exceeds $2.5 trillion USD](https://www.coingecko.com/).

![Total aggregate cryptocurrency value, worldwide, as provided by Artemis.](https://dagster.io/webflow-assets/c64bc49bc21ee44909bd.jpg)

   Total Crypto Market Cap Chart total market cap & volume of cryptocurrencies globally, a result of 14,951 cryptocurrencies tracked across 1,161 exchanges. Source: <https://www.coingecko.com/en/global-charts>

Stablecoins have also seen massive growth, with transfer volume peaking at over $150B a day in May 2024.

![Total Stablecoin value, as provided by Artemis.](https://dagster.io/webflow-assets/922175d9d85217edfa13.png)

   Source: <https://app.artemis.xyz/stablecoins>

The [cryptocurrency protocol stack](https://www.nansen.ai/guides/what-is-on-chain-analysis-and-why-is-it-useful-for-crypto-traders) generates a lot of public (onchain) data, but accessing that data in a consumable fashion is quite complex. There is no public repository of fundamental metrics like you might find for a centralized exchange like the NASDAQ or the NYSE.

And unlike traditional capital markets, which have structured quarterly earnings reports, crypto, makes real-time reporting possible.  Anybody can do diligence on a protocol's financials by accessing the public blockchain.

[Blockchain nodes](https://www.kraken.com/learn/what-are-blockchain-nodes-clients) are the backbone of blockchain networks, responsible for validating transactions and maintaining the blockchain ledger. This has made it easier to participate in the cryptocurrency ecosystem, , but financial reporting is still complex.

But indexing the raw data from blockchains like Ethereum and Solana is still complex.  These datasets are often dozens of TB in size.

An ecosystem of reporting firms has set about making real-time crypto data available to the masses. Artemis is one such crypto data provider.  Think of it as Bloomberg for the crypto markets.

#### Artemis: “The Bloomberg of Crypto”

![The Artemis UI.](https://dagster.io/webflow-assets/1945716c125854fbff22.png)

   The Artemis UI.

The Artemis team aggregates data from many blockchains in the crypto space and provides the cleansed, structured, aggregated data to a range of data consumers, including institutions in traditional finance, research analysts and crypto native investors at liquid token hedge funds, and retail investors. Artemis also works closely with the crypto protocols themselves who use their data in their workflows.

Artemis delivers its data through its [Artemis Terminal](https://app.artemisanalytics.com/), an Artemis API, and data shares on Snowflake. In addition, Artemis provides Excel and Google Sheets plugin integration to pull data directly into a spreadsheet.

Most equity research analysts or quants do not have data engineering backgrounds or a data platform team, so the service Artemis provides is highly valuable to them, making crypto data instantly consumable.

Currently, Artemis provides dozens of terabytes of data to several thousand customers. The larger blockchains have thousands of transactions per second (TPS), while smaller networks may only see a dozen TPS. The architecture of each chain also determines how big each transaction record is and what data gets logged.

The complexity is not only in the volumes of data, but rather the lack of standards across blockchains. Aggregating and benchmarking the data across providers takes a lot of data processing.  Different chains provide different features (like smart contracts). And unlike traditional financial markets, there is no “market close”—crypto is traded 24/7/365.

Behind the scenes, Artemis taps into the indexing services that are now commoditized in the industry, with many providers who will run the blockchain nodes—the backbone of blockchain networks— as a service and index the data.

#### An MDS tech stack

Broadly speaking, Artemis leverages an MDS stack, favoring composable, often open-source technologies.

- Most of the data is ingested directly from [Snowflake Shares](https://docs.snowflake.com/en/user-guide/data-sharing-provider), Snowflake’s marketplace for datasets from a variety of data providers
- Where data is ingested from other APIs or a node for an unsupported chain, this is done directly in Dagster
- Data modeling is done in dbt core
- Jobs run on [AWS’s ECS service](https://docs.aws.amazon.com/ecs/)
- Hightouch is used for reverse ETL
- BigQuery is also used for some more niche data sources
- Hex is used for data sandboxing and reporting
- Django and Next.js then support the front-end app

This streamlined stack lets the team iterate and ship quickly across the entire application.

#### Moving off Apache Airflow

Artemis ran Airflow as a managed service for the first year of its existence.  They chose to run it on [MWAA](https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html) because they were scrappy and were committed to AWS. However, the system fell short on reliability (due to the Airflow scheduler), and the non-engineers who contributed to the code base (such as data scientists and researchers) found the system hard to work with.

[Anthony Yim, Co-founder and CTO at Artemis](https://www.linkedin.com/in/anthonyyim), further explains: “What was also tough is managing data lineages especially when a model failed to build. We would have to rerun the whole lineage rather than a specific asset.”

“Debugging failures was hard since there was no single pane of glass to view assets and statuses.In addition, the UI on Airflow often just didn't work or was very cryptic. Finally, as an organization that needed to step up its data pipeline game, we wanted to use DBT, and Airflow didn't have a tight integration with DBT tools (at least not back then).”

Another challenge of Airflow: the lack of a dbt integration.

It was clear that Airflow would not support the team's next stage of growth and was increasingly adding drag to their development cycles. The team looked for a more fit-for-purpose system that provided better lineage, tagging, and a more intuitive framework for pipelines.

#### Building on Dagster+

The Dagster framework, with its asset orientation, proved much more intuitive and a better fit.

“Software-defined Assets were a novel approach.” says Alex, “Also, not having to write custom Airflow Operators bc of Dagster's builtin I/O managers was huge.”

The tagging and built-in cataloging and lineage make data collaboration much smoother, and medium-code practitioners (people with a data science or analytics background and focus who nonetheless write code) found Dagster’s framework much more accessible.

Again, because they lack a full-time DevOps team, Artemis selected a hosted option in Dagster+, providing many enhancements around user management, cataloging, and cost observability.

While Alex joined the team after the transition, he had prior experience working on and contributing to Dagster and was able to rapidly bring new value to the team. He expanded the Dagster+ setup to multiple code locations and organized assets in groups for better scheduling and alerting.

#### Opening the black box: open source dbt models

Analysts and investors naturally need to trust the data they are working with. To address concerns and answer analyst questions about methodology, Artemis took the bold step of publishing the open-source code to their dbt models, [which you can find here](https://github.com/Artemis-xyz/dbt).      

![The open-sourced dbt models by Artemis](https://dagster.io/webflow-assets/d2b3655330ccb2bb2f4d.png)

This approach of openness aligns with the culture of decentralized digital currency, and other projects have also openly shared their business logic, such as Dune’s [Spellbook](https://github.com/duneanalytics/spellbook).

#### A platform for growth

Today the Artemis team has a streamlined data operation that will support their growth for the coming years, as digital assets continue to expand, become more mainstream, and continue to evolve. Whatever digital currency the world decides to adopt next, Alex and the Artemis team will be ready to help investors grasp the fundamentals and make informed investment decisions.

---

## Case Study: How Petal Incrementally Adopted a Data Orchestrator

> Fintech company Petal gradually adopted Dagster to scale its platform without disrupting daily operations.

Source: https://dagster.io/customers/petal-case-study
Published: 2024-07-24

Adopting a modern data orchestrator may seem like a big leap, one that could cause your data operations to go dark for weeks or months.  But this is not the case.  Dagster can be adopted incrementally, adding value to our data operations from day one, and then scaling based on your needs.  Here we explore one such example.

### Meet Petal

[Petal](https://www.petalcard.com/) is an innovative FinTech firm that provides access to credit by looking at creditworthiness beyond the traditional credit scores used by most providers. Petal focuses as much on helping consumers save and build up good credit as it does on facilitating purchases.

[Liem Truong](https://www.linkedin.com/in/liem-truong/), is an Engineering Manager at [Empower](https://empower.me/), Petal’s parent company, and works on the Petal business.  He is part of a small team of three data engineers.  They support 2 product engineering teams, a growth team and teams managing existing customer accounts, as well as the internal data teams.  Petal is increasingly tapping into ML for underwriting models, and their data ecosystem continues to expand.

![The Zephyr AI logo](https://dagster.io/webflow-assets/e5536d3a56812afab1ed.png)

The data team uses a traditional ELT process to support downstream stakeholders: business analysts and dashboarding, data science, and finance.

Importantly, Petal has partners across the [financial services](https://dagster.io/use-case/finance) ecosystem, including VISA, WebBank, Credit Karma, Equifax, Experian, and TransUnion. These partners rely on timely and accurate data feeds from companies like Petal.      

The Petal team is building on AWS and uses several capabilities from the AWS toolkit.

Upstream, the team replicates data from their operational datastore using AWS [Database Migration Service (DMS)](https://aws.amazon.com/dms/) using CDC. Data is also received from external partners, such as daily reports from Petal’s card processor. A large volume of daily data comes from credit reporting bureaus. With around 300,000 customers, the data scales up rapidly.

A loading framework acquires the data via SFTP or from S3, applies parsing mechanisms on each job, then lands raw data to S3 and loads to Redshift. From here dbt handles the transformation into data assets for downstream stakeholders.

#### Moving beyond Jenkins

Prior to Dagster, Petal was running a number of CRON schedules triggered through [Jenkins](https://www.jenkins.io/).  However, the Jenkins setup needed to be updated, and the team missed the functionality of a modern data orchestrator.

“We never really had an orchestration tool. We relied on a bunch of CRON jobs stitched together with Jenkins. We wanted to break away from that and adopt a true data-centric tool to provide more context-aware orchestration.” recalls Liem.

“The main part we wanted to derive value from was event-based triggering from one model to the next. We wanted to have a dependency graph and streamline the overall pipeline. From the dbt side, we had the lineage and we got a lot of value from that, but it was lacking in our orchestration process.  Now we can see it end-to-end.”

##### An incremental adoption approach.

The team has been building out a lot of the constructs, but adapting them incrementally.  They retained much of the code from the original ingestion flow, dropping it into the Dagster framework.        

“The team is taking bits of our pipeline and creating the Assets and Ops to run the pipelines on Dagster” says Liem.        

Over time, the plan is to adopt more and more of the Dagster capabilities.        

![The Petal Credit Cards](https://dagster.io/webflow-assets/a322e76a6d83504a7654.svg)

While Liem did not personally select Dagster as the foundation of the data platform, today he manages the team that works on Dagster.   Petal adopted [Dagster+ Serverless](https://docs.dagster.io/deployment/dagster-plus/serverless) to delegate the infrastructure concerns.

##### Weaving in dbt

Petal adopted dbt Core early in their design. The team is leaning on the dagster-dbt integration to shift all dbt jobs over to Dagster and fully leverage the scheduling capabilities.

##### Incrementally shifting collaboration

In a similar way, the team is incrementally using Dagster for collaboration.  So far, the data cataloging has been done through the dbt docs, but as the team builds out the Dagster catalog, Liem sees this as being a foundation for collaborating with internal and possibly external stakeholders.

#### Setting up options for the future

In conclusion, Petal is a great example of how a small data team can lay the foundations for future functionality without having to invest a lot of time today or disrupt current pipelines.  Dagster has the breadth of functionality to replace homespun or simple cron-based scheduling processes and gives you options for adopting capabilities like event-driven scheduling, data cataloging, data quality tests, and so much more.

If you want to try out Dagster Serverless, you can [sign up for a free trial today](https://dagster.cloud/signup).

---

## Case Study: A Start-up’s Rite of Passage - Establishing the Data Platform

> How a growing startup built a scalable data platform using Dagster to future-proof its data operations.

Source: https://dagster.io/customers/zippi-case-study
Published: 2024-07-05

By their very nature, startup companies innovate and iterate on a core idea, fine-tuning their business model, positioning, and pricing. They also iterate and innovate when it comes to their data operations.

[Brazilian fintech firm Zippi](https://zippi.com.br/) provides a great example of how Dagster helps engineering teams incrementally adopt and innovate while also building a reliable and adaptable data platform.

We met up with [**Renan Veras**](https://www.linkedin.com/in/renan-veras-0a2776127/?originalSubdomain=br), Data Engineer on the Zippi data team, to discuss the business and the evolution of the data platform.

> "Dagster+ helped us deliver faster insights with less downtime, all while reducing our maintenance costs. We have greater visibility of our data assets and our pipelines are easier to troubleshoot and understand."

### About Zippi: Microfinance for Micro-Entrepreneurs

Access to credit is extremely scarce for the 40 million gig economy workers in Brazil and many other emerging economies across Latin America.

Volatile earnings and a difficult-to-access work history mean that these individuals cannot even apply for loans at most financial institutions. When faced with emergencies or financial difficulties, micro-entrepreneurs struggle to secure short-term funding.

Zippi solves that by building financial products tailored to micro-entrepreneurs and small businesses, providing the critical cash flow needed to keep the business going. Zippi issues working capital lines with a streamlined application process. Zippi has tens of thousands of active loans, each averaging 1,000 Brazilian real (\~$180 USD).

Zippi was a [YC Combinator graduate of 2019](https://www.ycombinator.com/companies/zippi).

##### From Pipelines to Platforms

Like most startups, Zippi began by implementing the critical pipelines needed to run the business.

Renan breaks these down as:

- Loan underwriting (the primary pipeline based on a proprietary Machine Learning model). This includes fraud analysis and identity verification (a.k.a “KYC procedures”).
- Business Analytics
- Customer behavior analysis

To achieve this, the team built out a first set of pipelines primarily focused on data movement:

![The Zephyr AI logo](https://dagster.io/webflow-assets/4480476ee30fbac8f23b.png)

In this pipeline, data is ingested from SaaS applications into an S3 data lake, ported to Redshift using Hevo, transformed using dbt, and analyzed using HEX or Metabase.

In addition to the above, the team implemented [Metaplane](https://www.metaplane.dev/) for data observability and the automation of data quality checks.

For the main credit analysis pipeline, the team ingests data from the Brazilian government and [OpenFinance](https://www.bcb.gov.br/en/financialstability/open_finance), a new service for financial institutions. Data volumes rapidly grew from small datasets to many terabytes.

##### ML models for loan approval

Like most lending companies, Zippi is developing its own ML models for creditworthiness, identity verification, and risk assessment. Ultimately, the ML model decides who should get approved for the loans.  Such ML models are often the ‘secret sauce’ for a [fintech](https://dagster.io/use-case/finance) company as they are based on—and fine-tuned with—the proprietary datasets the company accumulates.

Operationalizing such ML models with the basic ELT pipeline was challenging, says Renan:

“We moved to this ML-driven model, to rate the creditworthiness of loan applicants.  We needed to assign a score to each applicant to support the Credit team.  The challenge was to integrate the model to make predictions within the same pipeline that makes the model transformations.  We had no way to integrate the model as it would both write tables and then dynamically read back from those same tables.  Our pipelines were very linear and did not permit that kind of dynamic operation.”

##### Emerging data silos

Already at this small scale, long-term issues related to data discovery and collaboration started to emerge.

“We need all data practitioners at Zippi to be working closely together.” says Renan, “ As we were organized previously, people had different contexts about the company. Analysts on the business team were not aware of what was happening on the risk team or the growth team, and had no visibility into the data they were using.”

Moving to a centralized platform and a “single pane of glass,” Zippi could foster collaboration and autonomy among data practitioners and stakeholders. This would also free up the data engineering team to work on infrastructure and other critical parts of the growing data systems.

##### Moving to Dagster

The team adopted Dagster incrementally, initially focusing on operationalizing the ML model.

![The Zephyr AI logo](https://dagster.io/webflow-assets/b78b0abcd4bf5a2107b8.png)

The pipeline now became:

- ETL as a service -> S3 data lake -> Redshift data warehouse
- Transformations run on dbt Cloud orchestrated through the `dagster-dbt-cloud` integration
- Dagster runs the ML logic for the loan underwriting analysis returning the score per customer, creating a data asset for the credit team back in S3
- Analysis and visualization by the analytics engineers in [Metabase](https://www.metabase.com/) and [Hex](https://hex.tech/)
- Data observability and quality checks still ran on Metaplane.

The ML model, built on [sklearn](https://scikit-learn.org/stable/), would run directly in the Dagster instance.

The team could now continue to innovate and test out the ML model while supporting the critical operations of the business.

##### Next step: orchestrating the pipelines end-to-end

It rapidly became apparent that Dagster could provide a lot more value to the engineering team than simply running the ML models.  The next step was to orchestrate the dbt transformations through Dagster, using the [dbt integration](https://docs.dagster.io/integrations/libraries/dbt).

To track the entire lineage of their data, the team used [Dagster's dbt integration](https://dagster.io/integrations/dagster-dbt).

![The Zephyr AI logo](https://dagster.io/webflow-assets/1a557481ce8b3310480c.png)

“The ETL service did ingestion automatically, but we developed another process using an external Python script running in a HEX Notebook that would check the success of the ETL process from S3 to Redshift. Then we asked, ‘Why are we doing this? We have Dagster to orchestrate everything.’” explained Renan.

The team set about connecting the entire data stack within a single platform, eliminating the many local scheduling tasks and one-off Python scripts. The data platform was starting to unify.

##### Running on a Platform.

Today the team has full observability across the data platform, monitoring, and alerting in Slack.

It became a lot easier to diagnose and troubleshoot.

“We had issues with dbt execution monitoring.” says Renan. “dbt Cloud alerts were very limited, with no context on the error. The team would spend many cycles searching through dbt logs to identify the source of an error. In Dagster, we could parse out the error from the execution, and we had more information to troubleshoot quickly.”

Now the state of each data asset is immediately visible, and key assets are easy to locate in the Dagster catalog.

While the team used Dagster+ and dbt Cloud side by side for a while, they eventually concluded that most of the workload could be run directly on Dagster using dbt Core.

##### Building for the future

With the data platform foundation in place, Renan sees the team building out more integrations, and automating more steps of the process.

Another focus is remodeling the current pipelines following a reorganization at Zippi and a change in the data team structure. This will allow the Zippi centralized data team to better serve internal stakeholders.

Eventually the team would like to take Metaplane out of the equation, running all data observability and data quality checks on Dagster+, further simplifying the stack.

Next, the team will focus on improving analysts' ability to work independently with data, implementing a semantic layer, and supporting the modeling work. Again, Dagster’s self-service features, such as catalog mode, will support the effort.

Additionally, the team is excited to explore more machine learning challenges in the future. Leveraging Dagster, they are confident in their ability to build a robust MLOps stack to streamline the development, deployment, and monitoring of their machine learning models. This will not only enhance their predictive capabilities but also ensure that they can scale their ML efforts efficiently.

##### A common roadmap for incremental adoption

Zippi’s incremental adoption of Dagster is typical of startups that need to iterate rapidly and illustrates a typical incremental roadmap to moving towards a centralized data platform, reducing data silos while fostering autonomy and collaboration among data practitioners.

Organizations can adopt Dagster at their own pace and model their data architecture around the asset-centric framework, accelerating the development lifecycle, fostering collaboration, safe in the knowledge the solution can grow with their own data needs.

---

## Case Study: BenchSci - A Leap Forward with Dagster

> BenchSci uses Dagster to accelerate biomedical research and bring therapies to market faster.

Source: https://dagster.io/customers/benchsci-leap-forward-with-dagster
Published: 2024-02-20

Speed is a given within the pharmaceutical industry, with the race to market not only depending on the quickness of an organization but also its ability to innovate, understand, and adapt. Bringing groundbreaking medical treatments from concept to reality presents a significant challenge: managing the tide of data from countless research avenues and clinical trials.

[BenchSci](https://www.benchsci.com/) stands out in this race, employing cutting-edge AI technology to rapidly bring life-saving medicines to market. Their ASCEND technology is a science-first disease biology GenAI platform that acts as an AI assistant to preclinical research and development scientists to discover and evaluate experimental data, optimize research strategies, and mitigate potential risks to ensure the efficiency and effectiveness of scientific experimentation.

In a deep-dive conversation with the BenchSci analytics team, we learned how they're transforming the industry, with Dagster playing a pivotal role in their journey to expedite drug development.

### The Analytics Team

With a diverse team of six, comprising product analysts and data analysts, BenchSci's analytics unit goes beyond mere number-crunching and reporting. They provide critical data insights into the performance of BenchSci’s core product suite. Their toolset, which includes [Dagster](https://dagster.io/platform-overview), forms the backbone of a system designed to distill vast data sets into actionable insights.

##### From Major Challenges at BenchSci to Adopting Dagster

###### The Pre-Dagster Challenges

Before building on Dagster, BenchSci faced several key challenges that impeded its analytics data operations:

- **A Growing Set of Heterogeneous Tools:** While the teams' toolset was still relatively straightforward, it was clear that, as the team built out their platform, the current orchestration approach would be a bottleneck.
- **A Lack of Observability:** To properly manage the expanding number of data sources and growing sophistication of their processes, the team needed a single pane of glass that gave them precise insights into the current state of their analytics data pipelines.
- **Cost Management:** The team was aware that many processes could be better optimized for spend, but this required both observability and the right framework for pinpoint control and conditional execution.

In response to these challenges, BenchSci sought a solution to bring order and efficiency to their data management processes.

###### Choosing Dagster for Advanced Data Orchestration

BenchSci ultimately selected Dagster because of its ability to offer:

- **Effective Data Management**: Dagster promised to manage the data load while clarifying and directing BenchSci's analytics workflows.
- **Isolated Development Environments**: With Dagster Cloud’s CI process, each team member can seamlessly spin up their own ephemeral deployments for sandboxing and testing, merging changes to production with confidence.
- **Seamless Integration**: It could integrate seamlessly with their existing and future tools, providing a single source of truth for their data.
- **Event-Driven Automation**: Dagster's core [Software Defined Asset](https://dagster.io/blog/software-defined-assets) abstraction – whether a database table, report, or ML model – was crucial for managing modern data workflows. This aspect of Dagster was particularly appealing as it aligned with BenchSci's vision for efficient and responsive data handling.
- **Ease of Deployment**: Compared to other solutions, deploying Dagster was much easier and resulted in a more stable and easier-to-manage setup. Dependencies in other solutions they evaluated caused major headaches and lost time for the team.

Each team member has their own dev environment within which they work. With Dagster’s setup, they can just clone tables from production into their dev datasets. This saves the team a lot of time and computing costs.

If an error does occur upstream, the team now notifies the stakeholders of the impact on the dashboards.

###### Strategic Decision: Dagster Over Other Options

The decision to choose Dagster over alternatives was a strategic one. Specifically, BenchSci valued Dagster's approach to data assets and its suitability for event-driven automation, logging, and retries in data-rich environments. This approach allowed BenchSci to address specific areas of redundant compute to eliminate costs.

##### Dagster's Impact on BenchSci

The deployment of Dagster revolutionized BenchSci's data analytics operations, streamlining complex workflows and enhancing data reliability.

"Dagster acts as a traffic controller," said the team, emphasizing its role in connecting disparate data sources and analytics tools.

Because the framework encourages the optimization of data pipelines—and provides the right observability—running on Dagster allowed the team to achieve a marked reduction in computational costs and data errors. This is achieved by only materializing assets (i.e. running compute) when there is a clear benefit of doing so. Cost reduction was also achieved by reducing test runs, full-batch replication, and processing of poor-quality data.

Insights into how the data platform was performing further enabled BenchSci to tackle the complexity of its data ecosystem head-on.

By integrating and managing data from diverse sources through a coherent orchestration platform, Dagster provided BenchSci with a comprehensive cataloged view of its data assets.

###### Analytics As A Product

Dagster's robust data orchestration greatly supported BenchSci's transition to viewing analytics as a product. It enabled the analytics team to derive actionable insights from platform usage and business decisions.

“We’ve been focused in the last year on ‘analytics as a product’. We can serve the teams with a hands-off approach, where stakeholders can help themselves. We break away from handling request after request. Now we can just let Dagster run, and only step in when we need to.” says the team.

With plans to integrate Dagster's capabilities further into its analytics framework, the team's vision for a data-centric, agile, and interconnected future is well on its way.

##### Conclusion

BenchSci's deployment of Dagster highlights an essential trend in modern business: [the critical role of advanced data orchestration](https://dagster.io/blog) in meeting today's complex demands. Their success story exemplifies how Dagster enables organizations to swiftly understand and trust their data, adapt to new challenges, and monetize their data assets. Additionally, BenchSci's story demonstrates the growing importance of sophisticated data management tools in an era where the strategic utilization of data is critical to business success.

---

## Case Study: Abstracting Pipelines for Analysts with a YAML DSL

> SimpliSafe's team uses YAML DSL and Dagster to empower analysts while reducing engineering overhead.

Source: https://dagster.io/customers/simplisafe-case-study
Published: 2023-11-28

In this case study, we share how the data engineering team at SimpliSafe redesigned its analytics workflows to support dozens of analysts and hundreds of business users.

By deploying Dagster’s composable, fully-featured framework and automating deployments, the engineering team developed tremendous leverage, supporting a pool of analysts many times the multiple of the engineering team.

By eliminating overhead and removing the analysts’ dependency on the central team, the engineers can focus on expanding the platform and building new features to boost data quality and contain costs.

### SimpliSafe: Home security means data at scale

[SimpliSafe](https://simplisafe.com/) is a 1200+ strong team that provides home protection services and security technology to combat everything from intruders to fires, water damage and more to over four million people throughout the United States and the United Kingdom.

As a major supplier of in-home IoT technology, SimpliSafe generates and manages an impressive amount of data. The engineering team pipes 5TB of data daily from fifty heterogeneous data source types.  They run 450 data pipelines for pulling data from sources and another 350 for data aggregation.

The analytics team’s central Athena instance manages 40Tb of AWS S3 data across 1,700 tables, some exceeding 1T records. Between the analysts and the business users, there are 300 users of the data platform.

With just six people on the engineering team to support all critical business processes, it is vital to set up the business users to work with as much autonomy as possible. Doing so lets the engineering team focus on enhancing the data processes instead of getting tied up in maintaining and fixing things.

In this case study, we will share how the data engineering team at SimpliSafe built a data platform on Dagster and EKS, abstracting away the pipeline creation steps from the analysts to remove any dependencies on the central data engineering team when it comes to defining and deploying new data pipelines.

> “EKS and Dagster, they just work. It’s amazing. In the morning, I open the UI, and I see all the pipelines that ran overnight.” - [Daniel Nuriyev](https://www.linkedin.com/in/danielnuriyev/)

##### The need for a data platform

![Python](https://dagster.io/webflow-assets/eeb0700a3e368d718df7.jpg)

   The need for a robust data platform was apparent early on to [Daniel Nuriyev](https://www.linkedin.com/in/danielnuriyev/), Senior Manager of Data Engineering at SimpliSafe. But it was one particular fire drill that drove the point home.  

When Daniel joined SimpliSafe, the team had set up data flows on Streamsets.  SimpliSafe has seasonal spikes in the business. During one of these spikes, Streamsets stopped working, creating a major challenge for Daniel and his team. He had to scramble to rewrite the data management processes for the most critical pipelines in Python at a breakneck pace. Though Daniel’s quick thinking helped to get the team back on track, the event exposed some serious weaknesses in the old set-up, which would not scale in a reliable way.

After the unpleasant fire drill, the team started to map out the foundations of a better data platform.

##### Reviewing the available options

With a revised set of specifications in hand and the vision of abstracting away the complexity from the analysts, the data engineering team evaluated 25 possible solutions.  Knowing they would need to contend with complex scheduling, upstream dependencies, and define functions at run time, they looked for a composable, flexible solution with strong deployment options.

The final decision came down to an evaluation of Dagster and Prefect, with Dagster winning out based on the more complete feature set, which not only met SimpliSafe’s requirements but provided options for future development.

The SimpliSafe team started work on the new architecture in January 2022, with engineers rotating through the build.  Over a three-week period, the team set up Dagster, EKS, and built a basic YAML parser, with deployment handled by AWS CDK.

##### Abstracting away the pipeline design for analysts with a YAML DSL

SimpliSafe has dozens of data analysts who need to rapidly define and put into production new pipelines for transforming and reporting on critical business data.

The upstream data sits in heterogeneous sources, including databases (MySQL, MongoDB, etc.) and business SaaS applications, some of which have rate-limited APIs. In addition, other data teams provide data for analysis by exporting it from their source systems and dropping it in a shared location.

The analysts are typically conversant with SQL but not Python, so Daniel and the team set out to build a Domain Specific Language (DSL) - an internal interface that would allow the analysts to define the sources, schedules, and transformation required in a format that would be easy to learn and quick to deploy.

##### From YAML to deployed pipelines

Building on Dagster and Amazon Elastic Kubernetes Service (EKS), the SimpliSafe team developed an abstraction layer that let the analysts install and run a local instance of Dagster, then specify in YAML the properties of the pipeline they needed: source data, schedules, dependencies, transformations required, and final destination(s) for the new data.

From setting up their local instance to learning the process of specifying pipelines in Python, most analysts are up and running within a day, says Daniel.

Once the pipeline works locally, analysts submit a pull request (including the YAML file and any SQL files required) to put their pipeline into production.

From here, there is a streamlined approval process (an engineering review, analytics approval, and signoff by the analytics manager), and the pipeline is added to production.

The YAML file is stored in a discrete Dagster code location and translated on the fly to Dagster Ops and Assets.

```yaml theme=classic
ingest_method: reloaded
schedule: 0 0 * * *
error_level: error
description: AST Call Log
resources:
  cpu: 1
  memory: 4Gi
steps:
  - name: load
    type: source
    resource: mysql
    config:
      database_name: prd_replica
      table_name: ast_call_log
      connection_secret: analytics-prd_replica
      batch_size: 1000000
  - name: hash_columns
    type: transform
    op: default_transform
    needs:
      - load
    config:
      hash_columns:
        - agent_device
        - agent_name
        - callerid
  - name: target
    type: sink
    resource: athena
    needs:
      - hash_columns
    config:
      table_name: ast_call_log
      primary_key: call_id
  - name: target_pii
    type: sink
    resource: athena
    needs:
      - load
    config:
      table_name: ast_call_log
      primary_key: call_id
      pii: True
```

Here is a sample of an analyst’s YAML file that connects to MySQL and pipes data into Athena:

YAML

Analysts can include SQL in their YAML specification file. This does not pipe data from a source but aggregates data for analytics.

```yaml theme=classic
ingest_method: reloaded
error_level: error
dependencies:
   assets:
     - datalake_agg__order_detail
     - datalake_agg__node_revisions
description: >
  This table is for order component details
steps:
  - name: free_components
    type: link
    op: ctas
    config:
      table_name: _free_components
      query_file: _free_components.sql
      temp: True
  - name: monitoring_orders
    type: link
    op: ctas
    config:
      table_name: _monitoring_orders
      query_file: _monitoring_orders.sql
      temp: True
  - name: sdl_order_component_details
    type: link
    op: ctas
    needs:
      - free_components
      - monitoring_orders
    config:
      table_name: order_component_details
      query_file: sdl_order_component_details.sql
validation:
  asset: datalake_agg__sdl_order_component_details
  steps:
    - name: order_count
      type: generic_sql
      config:
        sql: >
          SELECT
              order_count
          FROM datalake_agg.sdl_order_component_details
    - name: today_lower_count
      type: generic_sql
      config:
        sql: >
          SELECT
            (a.count-b.count) as count
          FROM datalake_agg.sdl_orders_audit as a,
          datalake_agg.sdl_order_component_details as b
          HAVING (a.count-b.count) > 0
```

YAML

If analysts need to include more complex logic, they always have the option to inject Python code:        

```yaml theme=classic
ingest_method: appended
skip: False
appended_schedule:
  schedules:
    - hour_of_day: 1
error_level: error
resources:
  memory: 16Gi
  cpu: 1
description: >
  Create a non pii version of datalake_pii_agg in datalake_agg
steps:
  - name: query
    type: source
    resource: athena_query_extract
    config:
      query: SELECT * FROM "datalake_pii_agg" where _partitioned__date = {START_DATE}
      output_type: dataframe
      big: True
      schema:
        app_group_id: string
        conversion_behavior_index: Int64
  - name: hash_columns
    type: transform
    op: default_transform
    needs:
      - query
    config:
      hash_columns:
        - email_address
        - phone_number
  - name: cast_columns
    type: transform
    op: cast_column_types
    needs:
      - hash_columns
    config:
      columns:
        app_group_id: string
        conversion_behavior_index: Int64
  - name: target
    type: sink
    resource: athena
    needs:
      - cast_columns
    config:
      table_name: users
      primary_key: id
```

YAML

“*Essentially, we developed a better version of dbt,*” says Daniel, “*We have tens of analysts at the company, and we never ran into a problem; they all are happy.*”

Finally, data is queried through the Athena service or by using Tableau.

##### Limitations of the YAML DSL approach

There are some limitations to this approach. First, not everything can be abstracted away in YAML. When rare edge cases do emerge, Daniel and the team can assist. If the analyst has some familiarity with Python, they can code and add their own Dagster Assets to the pipeline.

The second limitation is that not everything can be tested locally, especially inter-pipeline dependencies. For this reason, SimpliSafe has a staged deployment process and testing prior to moving to production.

##### Maintaining the platform and order in the data

The engineering team includes six engineers (including Daniel), but Dagster and pipelines are just part of their overall workload. Supporting the Dagster platform involves having one engineer on call to support users when needed. Otherwise, the team works on connecting new data sources, troubleshooting, and building out the overall platform.

The SimpliSafe team stays current with the rapidly evolving Dagster project with quarterly updates and so far has not encountered any major rework due to new releases.

To make the entire data process reliable, Daniel has avoided a Data Lake approach. Instead, he has maintained high quality by controlling data flow into the warehouse. Data is well structured in Athena, allowing each team to have a discrete table prefix. Each new data source is carefully structured by the engineering team before it is ingested.

Each analyst team’s work is segregated with Athena table name prefixes and separate Dagster folders with their own pipelines.

“*Teams can create a mess for themselves in their data, but not for the other teams,*” says Daniel.

The team also builds jobs to purge old data from the databases and enforce naming conventions on the tables. Dagster supports this effort because any engineer can implement business logic.  Database maintenance depends on a number of Python checks added to Dagster jobs and they use a combination of jobs and sensors to remain efficient.

##### The benefits of a solid data platform

With a solid platform in place, the SimpliSafe engineering team has tremendous leverage, spending minimal time troubleshooting issues and supporting a pool of analysts several times the size of the engineering team.

With less time spent on reactive efforts, Daniel and the team can focus on enabling the organizations with new data sources, optimizing the existing processes, and building new features to boost data quality and contain costs.

---

## Case Study: Catalyst Cooperative - Liberating Public Utility Data with Dagster

> Catalyst uses Dagster to clean and share energy data with researchers, policymakers, and journalists.

Source: https://dagster.io/customers/catalyst-cooperative-case-study
Published: 2023-04-21

US government agencies release hundreds of gigabytes of data on utilities and power generators annually. However, due to its voluminous size, scattered nature, and complex structure, it is mostly inaccessible to researchers and advocates.

To overcome these challenges, the [Public Utility Data Liberation (PUDL) Project](https://catalyst.coop/pudl/) converts government-issued spreadsheets, CSV files, and databases into an integrated resource. PUDL utilizes an [open-source data pipeline](https://github.com/catalyst-cooperative/pudl) that automatically extracts, cleans, combines, validates, and publishes US energy data from various agencies, including the Energy Information Administration (EIA), the Federal Energy Regulatory Commission (FERC), and the US Environmental Protection Agency (EPA). This helps energy researchers and advocates focus on innovative analysis rather than data preparation.

We met up with [Bennett Norman](https://www.linkedin.com/in/ben-norman-040766ab), Data Engineer at Catalyst Cooperative, to discuss the technical choices the team made as the project grew and why they picked Dagster to rapidly iterate on the pipeline that generates PUDL’s key data assets.

### About Catalyst Cooperative:

In 2017, a team of clean energy advocates in Colorado founded Catalyst Cooperative with the goal of making valuable public energy data readily accessible and user-friendly for those working to decarbonize the energy system.

Today, [Catalyst Cooperative](http://catalyst.coop) is an entirely remote data analysis cooperative consisting of seven employee-owners distributed throughout North America. Their primary focus lies in the US electricity and natural gas sectors, and their work caters to non-profit organizations, academic researchers, journalists, climate policy advocates, public policymakers, and smaller business users producing novel research and policy recommendations.

##### The early days of the PUDL project:

The primary project of Catalyst Cooperative is [PUDL](https://github.com/catalyst-cooperative/pudl), which is both a data pipeline and a set of datasets analysts can query. The PUDL pipeline currently involves integrating, cleaning, and transforming data from three distinct government agencies to provide a comprehensive overview of energy generation in the United States. Since the agencies have yet to adopt common identifiers for the various components of the energy system like utilities, plants, and generators, the project requires substantial effort to ensure record linkage across the datasets.

Initially, Python and pandas were the sole tools used for [wrangling](https://dagster.io/glossary/data-wrangling) and loading data to SQLite and Parquet files.

To automate the ETL pipeline, the team set up a cloud VM and executed the pipeline nightly on a cron schedule. Following a successful nightly run, the updated SQLite database would be pushed to production.

Users could access these SQLite tables through a [front-end data portal](https://data.catalyst.coop/), but accessing interim outputs, additional analyses, and [denormalized](https://dagster.io/glossary/data-denormalization) versions of the data required downloading all of the normalized data and installing the `pudl` package. This made it harder for less technical users to analyze the data.

“For users to access this additional layer of functionality, they need to be familiar with Python and virtual environments. This creates a barrier for less technical users."

![The Catalyst Cooperative data pipeline prior to moving to Dagster](https://dagster.io/webflow-assets/3408565b0ac5a38bc63e.png)

       Pre-Dagster architecture,    

#### The limits of a DIY Python approach

Eventually, the need for an orchestration tool became more apparent:

“There are a lot of gnarly data dependencies between tables, and we got to a point where it just got so entangled and complicated, it was hard to move forward and add datasets,” says Bennett.

The team faced several challenges that hindered their productivity, including:

![An illustration of a power pylon](https://dagster.io/webflow-assets/56dc5d4bd346eb86e3ef.jpg)

- Burdensome process for adding a new data source and combining data sources.
- Lack of parallelism in their setup.
- Lacked uniform tooling for adding new datasets, analyses, and handling IO.
- Had to rerun the entire ETL when making changes to portions of the ETL which slowed development.
- Difficult to access interim outputs from the ETL for testing or analysis purposes.
- Difficult to understand the complex dependencies between data assets in the existing codebase.

Ultimately, the team wanted to adopt an easy-to-use data engineering solution so they could focus on data cleaning instead of developing and managing custom tooling.

##### The right orchestrator: imperative or declarative?

It was clear to the team that there must be a data tool out there that had already tackled many of these issues.

“There had to be a common engineering tool that could handle the infrastructure so we could focus on the areas we are experts in." says Bennett.

The team investigated [Airflow](https://dagster.io/vs/dagster-vs-airflow), but it seemed like a higher barrier to entry compared to newer orchestration tools. Both Prefect and Dagster, provided easier-to-understand concepts for creating DAGs, validating configuration, and handling IO.

The team decided against [Prefect](https://dagster.io/prefect), due to it being too unstructured:  “Prefect was almost too flexible for our purposes.  We wanted more opinionated Data Engineering abstractions.  Many of the data-aware abstractions in Dagster —such as data validation with [Pandera](https://pandera.readthedocs.io/en/stable/), data lineage, viewing metadata as it changes run-to-run, and taking a declarative approach — were a much better fit for our use case." The team documented their head-to-head evaluation [in this GitHub issue](https://github.com/catalyst-cooperative/pudl/issues/1487).

##### Migrating to Dagster

In September 2022, the team embarked on a project to migrate their system and began by familiarizing themselves with Dagster abstractions and how to optimize their usage. Mapping existing PUDL python objects that are shared among assets to Dagster Resources presented minimal challenges, but mapping existing Pydantic classes that validate the ETL configuration to Dagster types required some effort. However, Bennett notes that this task has become significantly more straightforward following the release of [Pydantic Config and Resources](https://dagster.io/blog/pythonic-config-and-resources) in Dagster 1.3.

During the conversion process, Bennett received support from the Dagster team and the Dagster community, enabling him to quickly overcome any obstacles. One of the more significant challenges was creating a DAG composed of partitioned hourly emissions data from EPA and non-partitioned power plant data from EIA. Although incorporating Dagster partitions into a DAG of non-partitioned assets presented challenges, the team successfully utilized a combination of dynamic outputs and asset graphs to achieve their goal.

“We discussed which approach would be best - declarative or imperative, or a combination of the two? Once we committed to a declarative approach, it became much easier to convert our code over to Dagster and map over our existing assets.”

##### Building with Dagster

Since the initial conversion, contributors have been able to add new data sources with relative ease.  “It’s been super positive so far,” says Bennett.

“It is nice to be able to decide how to persist each asset - some assets feel like an important step but do not need to be persisted to the database, while others can be loaded to the database with a SQLite IO Manager - we can just switch things on and off.”

“We mostly publish cleaned versions of the tables.  But occasionally our users ask for the raw data or maybe a partially cleaned table that is fit for their purpose. It is now easy for us to make that interim asset available to them.”

“To be able to create the DAG just based on asset names is super convenient.  With the imperative approach, you had to think about where these functions should be calling each other, but with Dagster’s declarative approach, you don’t have to worry about it.  You just load everything into a Dagster `Definition,` and it will create the DAG for you.”

![The Catalyst Cooperative data pipeline after to moving to Dagster](https://dagster.io/webflow-assets/775cebda794b1501d802.png)

     Post-Dagster architecture.    

Dagster has improved Catalyst’s workflow by:

- Simplifying the process of adding new datasets to the database thus enabling hundreds of new data assets to be made available to users.
- Accelerating development iteration cycles.
- Reducing mental effort in designing integrations for new or updated data sources.
- Eliminating the need to install the PUDL library will remove a significant barrier that currently prevents less technical teams from utilizing the data.

> “Adopting Dagster made it easy to treat all of our datasets as Software-defined Assets and persist them to the database, which can be easily distributed.”

Working with Dagster, Catalyst Cooperative feels more prepared to scale up the project, integrate more diverse datasets, and make them available to their users. They think adopting Dagster and distributing user-friendly data products instead of software will expand their reach and hopefully accelerate the decarbonization of the electrical grid. They are excited to see the Dagster community expand, add additional functionality, and develop best practices.

Interested in trying Dagster Cloud for Free?

        Enterprise orchestration that puts developer experience first. Serverless or hybrid deployments, native branching, and out-of-the-box CI/CD.        

             [Try Dagster Cloud Free for 30 days](https://dagster.cloud/signup?utm_source=dagster_blog\&utm_campaign=catalyst-cooperative-case-study)

---

## Case Study: Empirico - Enabling Large-scale, Multi-cloud Computing with Dagster

> Empirico uses Dagster to abstract infrastructure in large-scale, conditional multi-cloud environments.

Source: https://dagster.io/customers/empirico-case-study
Published: 2023-04-10

### The team at Empirico runs their data platform on Dagster, abstracting away infrastructure concerns from data scientists and enabling large-scale computing through conditional multi-cloud processing.    

[Empirico](https://www.empiricotx.com/) is a drug discovery and development company based in San Diego, CA, and Madison, WI. The organization specializes in speeding up the pace of drug discovery using human genetics and data-centric approaches.

![Zach Romer, Senior Data Infrastructure Engineer at Empirico.](https://dagster.io/webflow-assets/4a62d806135e5da2a4a7.jpg)

To achieve this, Empirico has built an expert team, including data engineers, data scientists, bioinformaticians, and Biologists. To support this work, they also built a world-class Data Infrastructure team.

We caught up with [Zachary Romer](https://www.linkedin.com/in/zachary-romer/), Senior Data Infrastructure Engineer at Empirico therapeutics, to discuss how Dagster fits into their architecture. You will find Zach on the < a href="/slack">Dagster Slack as @zach.

##### Improving drug discovery through human genetics and big data

Our genomes are 99.5% identical but all of us are born with a collection of genetic variants that can impair or enhance the function of genes and impact our health. By combining genetic data with health information from millions of individuals, we can learn about the effects of altered gene function and design drugs to mimic beneficial ones.

>    "People complain about compute costs, but it's a drop in the bucket compared to having multimillion-dollar labs with PhDs and lab equipment. What are a few EC2 instances at $10K a month compared to that?" - Zach Romer

##### Exploring large datasets

Most of the scientists on the Empirico team are experts in the science but are also skilled in data science and programming. This said, most of the team does not have expertise in cloud computing or productionizing analytical pipelines. Nor does Empirico want them worrying about where and how their experiments are computed.

This is where Zach’s team comes in.  The data infrastructure engineering team makes it easy for scientists to run analyses at scale without worrying about the infrastructure.

In addition, clinical data is heavily regulated, and regulations vary between regions and dataset providers. As a result, Empirico is limited in where data can be stored and processed, meaning that in many cases, technology choices are limited.  While Empirico would run part of the workload on Databricks on AWS, some of the restricted hosting environments would not support Databricks, so Empirico had to adopt a multi-compute environments approach.

To accommodate all this, the team built a bespoke interface (a Scala-backend & React-frontend application) which becomes the thin shell over the cloud compute layer.   Once the computation is run on the cloud, results are returned to the data science team either as raw files or using data visualization in the app. This approach allows the biostatisticians to focus on requesting the analysis of specific datasets without having to worry about the complexities of where and how the computation occurred.

On the cloud compute layer, Zach and the team build around standard big data tools like Spark, Pandas, Databricks, and NumPy.

Working with heterogeneous [data at scale](https://dagster.io/glossary/data-scaling) is a challenge, and Empirico is routinely running analyses across trillions of rows.

The analyses are complex and ever-changing. The cloud infrastructure is also complex and changing.  The company is keen to use the most performant tools, but these need to be deployed and maintained.  The team has to be vigilant for runaway compute costs, especially when experiments are designed by end users who may not appreciate the demand of a particular analysis.

As both the complexity of the experiments and the sophistication of the environment grew, it was clear to Empirico that they had to implement a more powerful data platform that would unlock some new dynamic capabilities.

##### Moving to modern orchestration

Zach and the team evaluated many orchestration solutions.  They prototyped Airflow since it was the de-facto incumbent, but parallelizing across a number of dynamic tasks was challenging.

“You had to do this crazy bending-over-backward exercise of templating files and templating DAGs." says Zach. “There was no way to fan-out without knowing how many tasks would be run before creating the DAG. We knew it would [fan out](https://dagster.io/glossary/fan-out) over thousands of tasks to run for a couple of weeks, potentially being restarted multiple times, so we really needed to be able to dynamically generate the fan out."

The team had also considered some tools that are more niche to bioinformatics, but they found these tools to be lagging in terms of functionality and fell short on collaboration features.

After hearing Nick Schrock describe Dagster - both the framework and the philosophy - Zach gave it a go. Within 30 minutes, he could demonstrate it to the team and rapidly prototype it.

The Empirico team found Dagster was the best solution for their needs based on several criteria:

- Python was the lingua franca of the team, and easier to find Python developers
- Tutorials and docs were easy to follow, and the examples “just worked”
- The UI helped them work efficiently
- The notion of Software-defined Assets matched their mental model.
- The framework seemed built to enable software engineering best practices such as dependency injection and upfront input validation
- There was a thriving, responsive, and growing community of users, with direct support from the Dagster Labs team
- Dagster had proven high development velocity, and was highly responsive to the community’s input.

##### Dagster as a Data Platform

The team is now using Dagster to build a data platform, to handle the projects requested in the web application and to distribute them to the most appropriate compute environment.

This centralizes the calls to the back-end compute environments and gives the data engineering team control over what compute is used, abstracting away the compute layer from the data scientists, and simplifying their workflows using autocompletes in the App.  It ensures that the rules around data sovereignty are not accidentally broken.  The engineering team can properly optimize for performance and retain observability across the entire platform.

“This has been working really well so far.  We are able to introduce new compute providers on the backend without interrupting anybody’s workflows.” says Zach.

Using StepLaunchers, the Empirico team can decide what compute layer each project is going to run on.

##### Running Big Data over multiple environments

Some genetic datasets are very large, representing trillions of data points.  These datasets are also tightly regulated and data sovereignty must be observed at every step of the analysis. This often means analyzing the data on dedicated third party compute environments with zero egress, returning only the analysis conclusions and none of the data itself.

“We may be crunching hundreds of TB resulting in trillions of rows of data being [exported](https://dagster.io/glossary/data-export), which is not extreme on the Big Data side of things.  To ensure experiment reproducibility, our code has to be the same across all of these environments, so we had to figure out how to distribute our compute across many environments, based on a number of conditions such as the data set being analyzed and the technologies supported by each hosting environment.” said Zach.

##### Enter the StepLauncher abstraction.

To solve this challenge, Zach turned to Dagster's StepLauncher abstraction, which provided hooks for executing an op externally to the Dagster instance.

By creating a custom StepLauncher based on the `DatabricksPySparkStepLauncher` (as found in the `dagster-databricks` library), the team was able to transparently move op code to the remote execution environment and trigger it from a Dagster step worker.

The custom StepLauncher can then poll the remotely-executing job for status updates while ingesting both logs and Dagster events streamed back to the Dagster step worker from the remote process. When the remote execution completes, the step also completes, and the Dagster graph execution continues with any subsequent steps.

![](https://dagster.io/webflow-assets/3a4520072372a34694e4.jpg)

     StepLauncher Context  

Today the team uses conditional branching paired with StepLaunchers to select the compute environment dynamically.  This allows the team to juggle datasets, some of which have to be analyzed on the restricted platform and others that sit on Empirico’s cloud environment.  The user selects the data sets and Dagster branches to launch jobs accordingly, combining and returning the results to the user.

Data Engineers can now just worry about writing workflows using Dagster-native code and components. Using branching, the platform determines where to run the compute based on the user’s ask at run time (such as how urgent the analysis or which datasets are required).

![](https://dagster.io/webflow-assets/651fbbc1b5062c1d6941.jpg)

     Graph for content-dependent compute platform selection  

##### What’s next at Empirico

Using Dagster as the ‘traffic control center’ for large-scale analyses has transformed how Empirico can support big data analyses.

The team now plans to:

- Complete the transition of legacy assets to Dagster for observability and sharability
- Explore how static partitioning might improve tracking of large-scale analyses
- Use dynamic cluster pools and async remote platform jobs to create a job queue, which would eliminate boot-up time by keeping the cluster ‘warm.’

---

## Case Study: Orchestrating Data Science at Zephyr AI

> Zephyr AI uses Dagster to analyze massive healthcare and DNA datasets for actionable AI insights.

Source: https://dagster.io/customers/zephyr-ai-case-study
Published: 2022-10-27

On April 14, 2003, the Human Genome Project (HGP) announced to great fanfare that it had mapped the human genome. This represented a giant leap in our understanding of functional biology and ushered in the era of personalized medicine.

A less well-known fact is that the HGP had only mapped 85% of the genome. The complete genome was only finished in January 2022.

Genetic data, it turns out, is difficult.  And at around 30GB to 70GB per single sequence file (FASTQ and BAM formats), it’s also a lot of data.

Just ask the data team at [Zephyr AI](https://www.zephyrai.bio/), a Washington DC based company working on AI-driven precision medicine and drug discovery.

### Meet the Zephyr team

Founded in 2021, the team specializes in predictive analytics by sifting through patient data and finding patterns or “interrelationships.”  They achieve this by ingesting large amounts of complex, disparate molecular, experimental, and clinical data and applying novel machine learning algorithms to draw new insights in the field of patient care, drug development, and healthcare administration.

“To build predictive models of therapeutic response (like in cancer) or of chronic disease progression (like in diabetes), you need three fundamental components: domain knowledge from which you can select the features and outcomes for your desired prediction task, data scientists who can build test and scale machine learning models, and real-world data to evaluate the performance of your models.   From the start, Zephyr has been focused on excelling across these dimensions,” said Zephyr AI Chief Technology Officer [Jeff Sherman](https://www.linkedin.com/in/jeff-sherman-30352ab2/), “the Zephyr ecosystem allows experts across areas of domain knowledge to form dynamic teams and creatively tackle new prediction tasks.”

By comparing a small amount of genetic information from a patient’s tumor to a large collection of tumor genotypes, Zephyr AI can provide insights into the best drug or treatment plan. This concept of "learn from many, apply to one" forms the foundation of precision medicine and is central to Zephyr's approach and mission.

We caught up with [Fahad Khan](https://www.linkedin.com/in/fahad-khan-4174a390/) and [Jordan Wolinsky](https://www.linkedin.com/in/jordan-wolinsky/), both senior software engineers working on Zephyr AI’s data platform, to learn about the process of collaborating across teams and ingesting and managing such large volumes of data in complex data science pipelines.

![Fahad Khan](https://dagster.io/webflow-assets/bea2268e7b60073d1333.png)

[Fahad Khan](https://www.linkedin.com/in/fahad-khan-4174a390/)Senior Software Engineer at Zephyr AI

![Jordan Wolinsky](https://dagster.io/webflow-assets/2056141e895b80b0dcee.png)

[Jordan Wolinsky](https://www.linkedin.com/in/jordan-wolinsky/)Senior Software Engineer at Zephyr AI

##### The challenge: Different teams, different datasets, different tools, and lots of data

Both genomic data and health records represent large datasets and get analyzed by domain specialists using bespoke tools.

“For our team, it’s mostly a matter of getting the different experts across the different domains to build their tools or their [data transformation](https://dagster.io/glossary/data-transformation). And then kind of hooking it all up,” says Fahad.  “As we work on a project, it’s a case of making sure we have everything set up with the observability, the assets we need, and then being able to push that out with one-click deploys, so the data is constantly refreshed and ready to go.”

The data team was tasked with pulling together very large datasets from disparate sources into two production pipelines:

![a conceptual image of dna](https://dagster.io/webflow-assets/a1b6fc7cfb867989a757.png)

**The bioinformatics oncology pipeline**, which takes a de-identified patient's raw DNA sequence (140 to 160 gigabytes per patient) and gets processed to identify the relevant mutations that drive their cancer.

![An illustration of a medical record](https://dagster.io/webflow-assets/50c0e25b00fd43d34fea.png)

**The predictive analytics pipeline**, which analyzes electronic health record (EHR) systems and claims data. This pipeline runs a terabyte of Parquet files or 9 billion rows of raw data.  

The data scientists receive the data assets produced, will run their own computations and predictive analytics workloads, and return the trained models for inclusion in the production pipelines.

Combining these large datasets from disparate domains and tools would quickly overwhelm naive task schedulers. The different toolsets make it hard for teams to collaborate. And without a single control plane for the pipelines, it is difficult to understand data lineage and manage data processing.

##### From early days to building on Dagster

In the very early days of the company, the data team was manually running data science algorithms on open-source biology data.  They were applying [preprocessing](https://dagster.io/glossary/data-preprocessing) (i.e. thresholding) and post-processing but had no orchestration in place. The team was small (three developers and two computational biologists), and the platform was still fairly simple, with just one repository and a single ambitious hypothesis that they were looking to prove before translating it into more complicated models.

When they reached a point where they had a large library of functions in place, they needed to bring some structure to the process.

The data platform team needed to engineer a system that would allow for one-click deploys so that changes could be rapidly implemented and reliably deployed at scale.

> “I feel like there are a lot of problems that we didn't encounter because we adopted Dagster so early.“ - Jordan Wolinsky

The data team had experience with both Dagster and Airflow, but at this early stage, Dagster provided a number of key benefits:

- A shared Python-based framework for developing data pipelines with powerful abstractions
- A single pane of glass for observability
- A centralized control plane with shared logging and governance

While the observability and parameterization functions boosted developer productivity in the immediate term, Dagster’s value was also in providing structure for the ongoing pipeline development process, which proved invaluable as multiple technologies came into the picture.

A major requirement for the kinds of high-stakes clinical decisions Zephyr is building predictive models for is transparency—another key feature of Zephyr’s core enabling technology that was made easier to implement using Dagster.

“I feel like there are a lot of problems that we didn't encounter because we adopted Dagster so early.“ said Jordan.

As the size and complexity of the projects grew, the team realized another benefit: The teams were able to maintain one master orchestration view in Dagit yet manage many distinct deployments with isolated codebases.  With this framework, each team maintains its own deployment in its own repository, avoiding dependency conflicts or merge conflicts with other teams. With Dagster, dozens of teams can work in parallel on separate workflows without stepping on each other's toes.

The team deploys on K8s using AWS EKS clusters, maintaining a staging/development and production environment..  They are organized by business focus area and maintain deployments through CI/CD with Helm.

Fahad came onto the team shortly after Dagster was implemented in May 2021.  He was familiar with Airflow but rapidly adapted to the Dagster way of working.  Fahad had to build a new data pipeline for new data types but found he could reuse the previous abstractions without worrying about the underlying pipeline functions.

This greatly accelerated the speed of development and Zephyr AI’s ability to create new pipelines very quickly.  With Dagster, “you're just writing Python code instead of being a yaml developer, which is what I used to feel sometimes when I'm just writing data pipelines.” says Fahad.

##### The value of software-defined assets

One of Dagster’s key differentiators is the use of Software-Defined Assets, a core abstraction that provides a declarative approach to managing and orchestrating data.

“We’ve become big fans of software-defined assets. That's been a big win for us. Just being able to hit ‘materialize all’ and then get all of our data is crucial.” says Jordan.

In a joint meeting in October of 2022 between the data science, data platform, and product teams, the product team was describing their future vision. Their ultimate goal was to process patient data and provide predictive analytics directly to physicians.

“\[It] sounded like a lot to build, but the day after we get all the requirements, we showed them Dagster, the assets, and the lineage and how the declarative aspect of it makes it very easy to know when assets are out of date or when they need to be updated.” said Fahad

In this sense, Dagster becomes a collaborative tool for understanding the assets available, and sharing their current status and lineage.

“There is a lot of value in not just explaining what transformations we're doing, but also what the result of that transformation is, especially to someone who is purely seeing it as data, like a product person,” says Fahad.

> "There is a lot of value in not just explaining what transformations we're doing, but also what the result of that transformation is, especially to someone who is purely seeing it as data, like a product person," - Fahad Khan

Zephyr AI has started using Dagster to publish metadata about the software-defined assets directly to Slack channels using the [Slack integration](https://docs.dagster.io/integrations/libraries/slack/dagster-slack). This posts a message with a link to completed assets.

“So if it's an S3 bucket, there's a link to an S3 console. Or if it's going to MLFlow or a Jupyter Notebook, just link out directly to it,” says Fahad.

Today the Zephyr AI team has ten people actively developing on Dagster, but all teams in the company interact with the system in some fashion.

##### Getting creative with Dagster: Perl, S3, Shell commands, and Python

Dagster is a great development framework for building pipelines using a Pythonic syntax, so it provides a good deal of familiarity and flexibility to Python developers.

Dagster also turns out to be a versatile tool for orchestrating tasks beyond the realm of Python.  When a particular piece of functionality was easier for the data science team to manage in Scala, it was initially orchestrated with a [Kubernetes cronJob](https://kubernetes.io/docs/concepts/workloads/controllers/cron-jobs/),  Jordan came up with the idea of [orchestrating the job in Dagster](https://docs.dagster.io/integrations/libraries/k8s/dagster-k8s) using a Docker image in 10 to 15 lines of code, giving the team all the metadata, run history, and observability on those jobs.

Once data engineers get a handle on the new toolset, they can start to re-architecture their pipelines to make more efficient use of their infrastructure in a way that is specific to their workload.

One example is the work Fahad has done with the bioinformatics pipelines: connecting various Java Programs, C++, and Perl scripts that process DNA sequencing data.

Fahad explains: “A lot of file-based tools assume that the file is going to be on your local file system. You can't really tell them to go to S3 and pull data. And they're also invoked using Shell commands that call out to some process.”

But Dagster provided a framework for seamlessly weaving these tools into a pipeline.  This is made easier using Dagster’s [I/O Managers](https://docs.dagster.io/guides/build/io-managers) - user-provided objects that store asset and op outputs and load them as inputs to downstream assets and ops.

“It goes back to the fact that it's all on Python. We can use stuff like [`pathlib`](https://docs.python.org/3/library/pathlib.html) to manage file paths. What the I/O Managers can do is basically make the S3 file download completely transparent. And then the I/O manager is a component that you don't really have to think about when you're writing pipelines.”

“Now all you're doing is you're getting paths in and paths out. You call whatever shell command you want on it. It creates a very nice developer experience because you're just writing reusable functions in Python that operate on file paths.”

What the Zephyr team loves about working with Dagster is that they can identify incremental opportunities to get more out of the platform.

From the Zephyr perspective, “we are actively debating what can be internalized into a Dagster op (like a function doing sklearn training) or externalized (like a function that delegates to a Docker image that needs to run on Spark running on Kubernetes).” says fellow Zephyr AI engineer Ripple Khera.  “We are interested in some bridge of the two that would allow for code to be written inline in a Dagster op but transparently executed on a declared execution engine (like Spark on Kubernetes, or EMR). We would love to consume new features from Dagster that make it easier to write code that leverages external execution engines.“

Moving forward, the team also plans on incorporating model training pipelines written in R.

![A photograph of the Zephyr AI team on a cruise.](https://dagster.io/webflow-assets/e9aefcaab7ed960b56c2.png)

     The Zephyr AI team taking some time off from solving complex bioinformatics challenges.

#### Scaling jobs on K8s

With the volume of data Zephyr is handling, scaling jobs is a key consideration.

With Dagster, the Zephyr AI team has the option to swap out the K8s executor. They are not just running in different processes but running in a cluster of pods. One 80GB genetic sequence file  gets broken up into 24 different portions of the genetic sequence.  Each sub-sequence gets sent to individual ops, processed, and then merged back together.

“We're getting a lot of speed-ups and performance benefits just from using those,” says Fahad.

In essence, the Zephyr AI team has [built their own mapreduce-like job on top of Kubernetes](https://gist.github.com/ripplekhera/f7a8aea1fdf7c76d2e391e84248e2a2e). It's just a DAG whose execution is completely abstracted away, and they don’t have to worry about it when writing the actual code.

#### What the future looks like:

> "Most of the work now is on the actual data, understanding the data and combining it, bringing it together. It’s a nice place to be, not fighting the tools but using the tools." - Fahad Khan

The Zephyr AI engineering team is now focused on how many different types of data sources they can merge into one common data set. Part of doing that is bringing together even more types of tools across different types of environments to build the holistic model for analyzing patient clinical data alongside genetic profiling.

“We've mostly solved that by building abstractions through Dagster. We can build up a pipeline that's running Perl 5 all the way through with no issue and then can do the same thing with Spark. And it doesn't feel much different,'' says Fahad, “Most of the work now is on the actual data, understanding the data and combining it, bringing it together. It’s a nice place to be, not fighting the tools but using the tools.”

For Zephyr, the next big data science feats will be validating their predictive models in even larger real-world data sets and continuing to expand and improve their models to bring better outcomes and a better health care system to more people.

![The Zephyr AI tech stack circa 2022](https://dagster.io/webflow-assets/2bc83ee6b48f479b5cf2.png)

---

## Case Study: How EvolutionIQ Rebuilt its ML Platform for Enormous Productivity

> EvolutionIQ rebuilt its ML stack on Dagster to gain developer productivity and avoid common data pitfalls.

Source: https://dagster.io/customers/evolution-iq-case-study
Published: 2022-08-24

Over the last year, EvolutionIQ overhauled its data platform. In doing so, they positioned the company for growth, strengthened developer morale, accelerated development cycles, and slashed new client onboarding from several months to less than a week. Here’s how they approached this transformation and what they learned along the way. Spoiler alert: they use Dagster.

### When your data pipelines *are* your business

The data pipelines at most businesses power internal processes such as business intelligence and customer insights.  But at some companies, the data pipelines *are* the business.

This is the case at [*EvolutionIQ*](https://evolutioniq.com/), the New York City-based company providing ML insights to some of the world’s largest insurance carriers.  EvolutionIQ uses advanced, proprietary machine learning and natural language processing to understand medical recovery and treatment like a healthcare expert – and then deliver daily guidance on extremely complex bodily injury claims.

Founded in 2019, EvolutionIQ has raised $26M to develop an entirely new class of software that acts as an AI partner to reprioritize thousands of insurance claims daily, enabling adjusters to focus their time on a subset of cases ready for immediate action. Their software products for disability, general liability, and workers’ compensation are now deployed with some of the world’s largest insurance carriers and third-party administrators.

A key challenge for the data team is to test and iterate on the ML models, onboard new clients, and build out the data platform, all while running a steady, reliable service for established clients.

Today, EvolutionIQ is running on a modern, reliable data stack.  They manage a single code base and have a disciplined development lifecycle that carefully protects client data while giving the team the flexibility to deploy new innovations across multiple environments.  Testing and security is built into the design, and the data platform facilitates collaboration across teams.

But like all tech startups, the company had to build up to such a tech stack by innovating and demonstrating the validity of their model along the way.

![Tom Vykruta](https://dagster.io/webflow-assets/0e2fb6bece54c221ac5b.png)

Tom Vykruta

![Stanley Yang](https://dagster.io/webflow-assets/38314d1cb44b6fd96fc0.png)

Stanley Yang

![Karan Uppal](https://dagster.io/webflow-assets/7820aa1afd80cfbf8646.png)

Karan Uppal

We sat down with EvolutionIQ’s co-founder and CEO [Tomas Vykruta](https://www.linkedin.com/in/tvykruta/), along with [Stanley Yang](https://www.linkedin.com/in/yangstanley/) (Principal Engineer) and [Karan Uppal](https://www.linkedin.com/in/karanuppal/) (VP of Engineering), to discuss this challenge, how they adopted Dagster, and the lessons they learned along the way.  Tom and Stanely are both former Google engineers, while Karan sharpened his AI engineering skills at Bloomberg.

#### The approach in the early days

In its early days, EvolutionIQ built out several instances of its core data pipeline, one per customer.   Each pipeline was a copy of the most recent version at the time, with some customizations per client.  This early approach clearly was not going to scale in the long term, but it got the company through the first couple of years and the first handful of clients.

There was a lot of iteration, with just five engineers on the team handling all aspects of the process.

The core AI model was refined by working with one client’s data, and the learnings then being manually reapplied to the other clients' pipelines.  This may seem laborious, but the team’s focus at the time was first to demonstrate the performance of the model, not to build out a large, repeatable process.

Orchestration, such as it was, was done with Cron-based Python scripts designed to run the pipeline end to end.  In the morning, each customer would dump data into EvolutionIQ’s systems, and the Cron table would kick off scripts based on an estimate of when - hopefully -  the data had arrived.

“We were entirely dependent on data arriving as expected and everything finishing on time,” said Stanley.

Naturally, in dealing with insurance claims, the EvolutionIQ team has to maintain strict confidentiality and data sovereignty, so each customer ran on a separate GCP virtual private cloud.

Over time the team built out four separate code bases which were not fully tested and were mostly undocumented.  At this stage, only the founding team really knew where everything was and how things worked.

#### Hitting a key growth stage

As the number of clients expanded, small challenges became much larger, and it was clear that the business would not scale.

- **Testing headaches:** There were no options for testing the pipeline other than to run the whole thing end to end.  A full test would take two hours. “We were wasting hours of time rerunning the pipeline; it was ridiculous.  This also was a massive consumption of compute resources as each test required a run of the whole batch,” said Stanley.
- **Slow at developing new features:** Even if a test was successful, there was not enough regression testing to make it safe to port the changes from one pipeline to the next.  Besides, the team was getting so mired in maintaining the many heterogeneous pipelines and separate code bases that they had less and less time to work on innovations.
- **A lack of extensibility:** The architecture at the time was to run the pipeline as a single monolithic job and go straight from data processing into Postgres. Few data assets were generated along the way, making intra-run checks impossible.  The data team also had no process for generating the data assets required by other teams who might develop new business capabilities in—say—customer success or business intelligence.
- **Developer frustration:** Ultimately, the biggest challenge with the old model was ramping up new hires and retaining good engineers. “The problems were actually more about the people than about the size or number of datasets” says Tom, “As you scale, you need to be able to hire specialists, rather than the jacks-of-all-trades developers needed in an early stage start-up. Specialists expect more structure and want to focus on a narrower part of the platform. So you need to get your house in order before you can move to that level.”  EvolutionIQ learned this the hard way. After they joined, new developers were surprised by how difficult it was to get started. There was no standardized onboarding, and each pipeline was customized, making quality assurance a challenge.  With a complex system, there is a lot of institutional knowledge, and new hires would need to learn the whole setup before they could contribute. The engineering team was both hindered by productivity barriers and low morale as a result.

#### When to move from individual pipelines to a data platform

Clearly, something had to give.

“We needed to create a framework for how we transform claims and make it repeatable, manageable, and scalable,” says Tom. “We would not exist today as a company if we didn’t move to a single unified codebase, with a real data platform beneath it.“

> “We would not exist today as a company if we didn’t move to a single unified codebase, with a real data platform beneath it.“

“For AI companies, if you don’t do this, you will end up having a mini team for each client, and the company is not going to scale. Your margins will suffer, and your people will not be happy as they can’t innovate on a platform. You face all sorts of limitations if you don’t make that investment early on,” he said.

But when leading a tech startup, the CEO has to know *when* to make the call on major initiatives. Too early, and the team won’t have all the insights needed to make the right tech decisions. Too late, and you are falling behind, dragged down by tech debt. “It’s easy to do this too early, and it’s easy to do this too late.  When you reach 20 employees as a tech company, the time is probably right,” says Tom.

With this in mind, Tom set forward an internal initiative: *DataStack 2.0*.  The goal: take EvolutionIQ to the next stage of maturity, move past the sandboxing phase and prepare for scale.

###### Unify behind a vision

It’s hard to get people to commit to change, so Tom started out by aligning the team behind a common vision.

Tom pitched this as much more than an engineering upgrade. The effort was to get the whole company leveled up and ready for the next phase of growth.

Every insurance carrier that EvolutionIQ works with has a slightly different representation of claims data. By mapping this data to a unified schema on a well-architectured data stack, EvolutionIQ would be able to develop major new capabilities: run analytics across the data of all clients, create an industry profile of the best and worst performers, enable the marketing team to create valuable insights, shed tech debt and innovate faster.  The firm would also reduce onboarding overhead and address developer attrition.

Tom shared the vision with the engineering team, the technical leaders, and eventually to the entire management team.

###### Pinning down the requirements

With the vision shared, a core group set to work and did three reviews of possible architectures over three years. In total, they iterated over 10 possible frameworks, each with its own POC.

The team had a number of non-negotiables.

- **InfoSec and data sovereignty** were major considerations. Databricks, for instance, required that they ship some data into the Databricks environment, which was not an option for EvolutionIQ’s clients. Decoupling the DAG from the other components was critical to meeting regulatory requirements.
- **Cloud-agnostic**: Although the team was fully established in GCP, they wanted the flexibility of moving to other clouds if necessary.
- And the platform had to support **a unified development framework in a common programming language**.

#### Selecting the orchestration layer

EvolutionIQ’s *DataStack 2.0* was going to need a solid orchestration layer, so the team evaluated the leading contenders: [Airflow](https://dagster.io/vs/dagster-vs-airflow), [Prefect](https://dagster.io/prefect), and Dagster.

Karan Uppal did the research, asking, “What does the right pipeline look like?”  He questioned what engineers wanted to control and what should be handled by the tool out of the box.

A key focus for the orchestration layer: *to optimize engineer productivity*. A productive engineer is able to work on things that are both personally satisfying and most important to the business.

There were other obvious criteria for the team: observability of the pipeline runs; [versioning](https://dagster.io/glossary/data-versioning); ability to repeat the pipeline easily; developer ergonomics; Python native; understanding of dependencies; extensibility.

The team needed an orchestration tool that could deliver the required functionality today but also demonstrated a strong commitment to a roadmap aligned with EvolutionIQ’s direction.

With requirements in hand, the team had to pick between the most widely adopted orchestrator (Airflow) or take a chance on something that was not used as widely.

Typically the team would opt for the proven, familiar solution.  But when the idea of developing on Apache Airflow was floated, the team was divided.  The solution had trouble giving users what they wanted, and EvolutionIQ’s use case was demanding.

Prefect seemed like a decent open-source option, but the team was underwhelmed by the vendor’s development and deployment philosophy as well as the firm’s long-term focus: it was clear from their hiring that the priority was on commercial exploitation before engineering or community efforts.

#### Enter Dagster…

The way things were designed in Dagster gave the team confidence they could expand the capability going forward.  “For example, if you want to hold code and data together, DVC does that. But Dagster does that and other things. Airflow does not do that out of the box. You have to build that around it.” says Stanley.

Because the Dagster framework is very flexible, EvolutionIQ was able to implement a multi-tenant environment with separate I/O managers using Terraform.

EvolutionIQ does adhere to a disciplined deployment (dev | stage | prod), and Dagster supported this use case beautifully.  It was clear EvolutionIQ would not have to run a new Dagster deployment per client.  They could develop a single unified code base that could be deployed and configured for each client while being fully isolated and run under separate Kubernetes service accounts.

The EvolutionIQ team was confident they could design an architecture whereby they assign one service account with access to a single client, conservatively avoiding any leakage between clients, and repeat the design across environments.

When it came to testing, Dagster provides a strong suite of debugging capabilities, and pipelines could be re-run from one of any number of stop-points. Dagster’s interface would clearly be a great addition to the team's ability to observe pipelines and support clients if any issues arose.

> "I've been building AI solutions at Google for ten years, and I now realize that a DAG for a feature store is absolutely the future. It enables incredible scale, consistency, and reliability. As a company, we'll innovate more, and that has immeasurable value to advancing our machine learning models and quality of the product." — Georg M Goerg (head of ML at EvolutionIQ, formerly Google AI)

Finally, the team was impressed by the support and responsiveness on Slack from the Dagster Labs engineers and the community.

#### … but, not so fast.

Was Dagster an overnight success with the whole development team?  Not quite.

The first few months of implementing *DataStack 2.0* were tough, and the orchestration layer was only one piece of it.  The overall effort was a major ask: to maintain the stability of all client pipelines while redesigning the core platform.

Adopting any new technology is a hurdle. Luckily when it came to Dagster, EvolutionIQ had Stanley Yang as an internal evangelist.

Stanley invested time in sitting down with each team member as they built familiarity with the new toolset, reviewing how best to use Dagster. He coached the new engineers through any questions but also demonstrated the value of the orchestration capability and why this would get the team to a better place.

Stanley also kept a direct connection to the Dagster team to help iron out any issues.

Here’s Stanley’s take on the experience:

“The adoption curve is more of a mindset change. Dagster is quite flexible - which is good and bad - there are not always hard recommendations on how to do something yet. There is no single approach to how you write pipelines, so it’s easy for a less experienced engineer to go off the rails. In retrospect, we should have reached out for best practices and architectural review early on, then established a workflow around Dagster.“

Selling the team on using the framework was harder when faced with a short task that could be quickly executed in a tool they might be familiar with, like dbt.

“Sensors, repo, and schedulers all sit in the same code as your pipeline. This is also both good and bad.  In some instances, when you are just running a transformation, it can be harder to explain to the team why the overhead of running in Dagster is warranted rather than running directly in dbt. This is challenging when you have customer demands and short periods to deliver a result.” says Stanley.

As with any powerful, versatile platform, it takes some investment to learn and tailor it to a specific context or need and to achieve the right developer experience in the long run. It is also important to create room for iteration.

For instance, by running dbt tasks from within Dagster, the team was able to have all executions tracked in a single system, which made it easier to understand what had happened, debug problems, and trigger downstream steps that depended on the dbt transformations.

“We learned a lot from our first prototype, and our understanding and appreciation of Dagster evolved as we used it, moving from a single client to multiple ones.  Our second \[build] was good,” says Stanley.

> “We learned a lot from our first prototype, and our understanding and appreciation of Dagster evolved as we used it."

While it took time to bring all engineers on board and build familiarity with the system, the leadership team approached this in such a way that the adoption of Dagster provided incremental gains along the way, which helped achieve buy-in.

#### Putting new wings on the plane

Every data team whose outputs are critical to the day-to-day running of the business faces the same issue.  How to put changes into production while minimizing the disruption to customers?

“Like putting new wings on the plane mid-flight,” as Tom describes it.

EvolutionIQ has customers in production, customers in a pilot phase, and new clients just starting.

So the approach was to build the framework for new client deployments and move accounts gradually: unify around a single code base, establish a deployment model, then clean up the tech debt gradually.  The bar for changing things for established customers is much higher than it is for new ones, and you certainly can’t risk deploying in a half-finished state.

So they battle-tested on the newly developed pipelines, then reapplied the packets to the older clients.  Having started on this journey about one year ago, the team is now migrating their final account to their *DataStack 2.0.*

#### Running on *DataStack 2.0*

The change took time and effort.  Now, things are much better for the team.

Thanks to this new vision and new data stack, EvolutionIQ went from a two-month implementation timeline to getting new customers up and running in just a few days, mapping each clients' data to EvolutionIQ's unified schema.

“We now run in a multi-tenant environment on a single Dagster deployment.  This did not come out of the box, but Dagster is quite flexible, so we were able to build this out,” says Stanley.

- **Built for testing and debugging**: Today the team has all the benefits of observability into the pipelines. Testing of new functionality, deploying ML model updates, and debugging are all radically improved. Now, if the team had a flaky job, they could work locally, kick it off from halfway through the pipeline, and rerun it. If needed, they could go back to the parent ID to see what had run. It was night and day compared to the prior setup.  “It was like going from the cavemen shell script to a properly built platform.” says Stanley, “We now almost have breakpoints in our code and can just rerun from there. That’s a leveling up of the development workflow.” The team’s approach to breaking down the pipeline has also changed. They can now focus on building subcomponents that can be developed and tested individually.
- **Better accountability**: And working on a single platform improved accountability. With Dagster as the single plane of glass across all clients, engineers have shifted the mindset from dealing with the pipeline of individual clients to managing a multi-tenant platform. Dagster provides a unified framework and a centralized tool upon which the team could have a shared view and a shared responsibility for the quality.  “Today, the engineers take accountability for the data pipelines for ALL clients.  If any one of them is failing, it’s because the platform itself failed.” says Tom, “Now you own a system, you don’t own a client.“
- **Moving to a higher level**: As such, developers are now focused on more high-level problems: how to get the pipeline running faster and smoother? How can we sample the data?  They have moved from “my pipeline is broken” to “how can we improve the process and the platform as a whole?”  The more strategic engineers on the team are now able to zoom out and look at the runs holistically and ask themselves where they might have the most leverage in improving performance. They ask, “How can we simplify, sample, or reduce things down earlier in the pipeline and improve downstream processing?”

> They have moved from “my pipeline is broken” to “how can we improve the process and the platform as a whole?”

- **Accelerate adoption and open the data platform up to other practitioners**: Onboarding new hires has become easier. They have a clearer framework to focus on and the I/O of each node helps paint a clearer picture of the pipeline structure. Today EvolutionIQ has four teams working hands-on with Dagster (data engineering, operations, applications, machine learning), and they are able to do so very autonomously. Having mapped the ingested data to a single unified schema, teams can write pipelines without help from data engineering. For example, the engineering team can make assets available to the Applications team that consumes the data artifacts, moving them to their own Postgres instance. It brings the teams together but also allows the teams to work independently, reducing dependencies.

![Karan Uppal and Wrenny](https://dagster.io/webflow-assets/77f77fe3c47c7db97394.jpg)

   With a new data platform in pace, Karan can now relax with Wrenny.  

#### Lessons learned

Here are EvolutionIQ’s key best practices for implementing the orchestration layer using Dagster:

- Adopt a data platform approach early on to bring structure to the work, and allow the engineering team to focus on higher order issues.
- Create a vision with senior executive support, then have hands-on champions who can coach other engineers through the adoption of new tools.
- Carve out time for learning the tool and establish internal best practices for how you will implement Dagster - there are many ways to do something, but you benefit from standardizing.  Tap into the Dagster team and the Dagster community on Slack.
- Do not focus on a big-bang release. Aim for incremental gains as you transition to the new tech stack.

Every start-up will face the same step-up moment that EvolutionIQ did: moving from prototyping and experimentation to a fully-fledged data platform.  This is true whether your pipelines support internal processes or deliver critical customer-facing services. When you get to that stage, your choice of orchestration platform will be critical. Dagster gives you the flexible programming framework and robust development lifecycle needed in today's demanding data flows.

#### What’s next

So, where does the EvolutionIQ team go from here?  [David Loewenstern](https://www.linkedin.com/in/dloewenstern/), Staff Engineer and original architect of *DataStack 2.0,* explains:

“*DataStack 2.0* moved EvolutionIQ’s client delivery into a scalable, robust, cross-client data platform, but a key component remained in the sandbox: the training, tuning, and evaluation of new machine learning models. *DataStack 2.1* will move these processes into the data platform by using Dagster for orchestration, coupled with [Vertex AI](https://cloud.google.com/vertex-ai) for feature and tuning management. This will make the use of feature stores, metric stores, model selection and hyperparameter tuning across clients as seamless and scalable as delivery.”

#### Bonus content: EvolutionIQ's interview with Seattle Data Guy

[Video: Customer story video](https://www.youtube.com/watch?v=26SbDekulzY)

     >    Watch EvolutionIQ's September 2022 interview on [Seattle Data Guy](https://www.youtube.com/c/SeattleDataGuy)

---

## Case Study: Incrementally Adopting Dagster at Mapbox

> Mapbox adopted Dagster while keeping Airflow, improving developer workflows and team efficiency.

Source: https://dagster.io/customers/incremental-adoption-mapbox
Published: 2021-05-18

\<img  src="/posts/incremental-adoption-mapbox/mapbox-logo-black.png"  alt="Mapbox logo"  width="384"  height="86"

The Geodata teams at Mapbox are responsible for continually updating the map ofthe world that powers our developer data products and services.

Like many teams using Airflow, we found that continued development in Airflow was painful and costly.

Our data sources range from hard drives that get delivered from our vendors inthe mail once a month, to public-facing APIs that we poll every day, toreal-time feedback on thousands of addresses from the 700M end users who touchour maps every day.

We process this data using about a hundred different data pipelines and flowsto create maps, road network data for navigation, point of interest datasets,and search indices for addresses. The core of our address data processing isthe [conflation engine](https://www.mapbox.com/blog/115m-improved-addresses-and-support-for-7-new-countries)and correction system, which combines over a billion addresses with road,parcel, and building datasets to get maximum coverage of existing addressesand calculate the most correct address points possible.

Mapbox has historically relied heavily on Airflow for orchestration, and manyteams, including ours, run their data pipelines on their own Airflow instances.

But like many teams using Airflow, we found that continued development inAirflow was painful and costly.

To fully test their orchestration code in Airflow, our engineers would typicallyneed to run a bunch of deploys to production, since setting up the dependenciesto run DAGs locally and ensuring that they were correctly configured was toocumbersome. This led to unacceptably slow dev cycles — not to mentionsignificant cost, since all our dev work had to be done on our productioninfrastructure.

We needed a solution that would allow for incremental adoption on top of our existing Airflow installation.

In an environment like ours, where the outputs of data pipelines arebusiness-critical, SLAs are strict, and many teams are involved in creatingdata products, we can’t stop the world and impose a new technology all at once.Luckily, Dagster comes with a built-in Airflow integration that makesincremental adoption on top of Airflow possible.

We started using Dagster because we needed to improve our developmentlifecycle, but couldn't afford to undertake a scratch rewrite of our existing,working pipeline codebase. Dagster lets us write pipelines using a clean setof abstractions built for local test and development. Then, we compile ourDagster pipelines into Airflow DAGs that can be deployed on our existingscheduler instances.

This is especially critical for us since our Dagster pipelines need tointeroperate with legacy Airflow tasks and DAGs. (If we were starting fromscratch, we would just run the Dagster scheduler directly.)

We started by writing new ingestion pipelines in Dagster, but we’ve since moved our core conflation processing into a Dagster pipeline.

Let's look at a simplified example, taken from our codebase, of what thislooks like in practice for normalizing, enriching, and conflating addressesin California.

Our Dagster solids make extensive use of Dagster's facilities for isolatingbusiness logic from the details of external state. For example, one solid,`export_conflated`, reads enriched and normalized addresses from the upstreamsolids and then conflates them into a single deduplicated set of addresses,taking the best address across multiple sources.

```python theme=classic
@solid(
    required_resource_keys={"conflate_emr_step_launcher", "pyspark"},
    config_schema={
        "export_table": Field(str, is_required=True),
        "execution_dt": Field(str, is_required=True),
        "export_csv_enabled": Field(bool, is_required=False),
        ...
    },
)
def export_conflated_addresses_solid(
    context,
    iso_3166_1: str,
    iso_3166_2: str,
    input_database: str,
    ...
):
    spark_session = SparkSession.builder.enableHiveSupport().getOrCreate()

    execution_date = get_execution_dt(context.solid_config["execution_dt"])
    export_csv_enabled = context.solid_config["export_csv_enabled"]

    enriched_source_1 = spark_session.sql(
        f"SELECT geojson FROM {input_database}.{source_1_enriched_table} "
        f"where dt = '{source_1_delivery_dt}' and state = '{iso_3166_2}'"
    )

    ...

    enriched_unioned = enriched_source_1.union(enriched_source_2).union(
        enriched_internal_source_3
    )
    final_df = conflate(spark_session, enriched_unioned)

    export_data_to_conflate_table(
        TableOperations(spark_session),
        tableOps,
        final_df,
        output_database,
        export_table,
        execution_date,
        iso_3166_1=iso_3166_1,
        iso_3166_2=iso_3166_2,
    )
    yield Output(execution_date, output_name="conflated_dt")

    if export_csv_enabled:
        csv_exported = export_csv(
            spark_session, final_df, output_database, execution_date
        )
```

Python

The body of this solid is mostly Pyspark code, operating on tables that havebeen constructed by upstream solids. In a pattern that probably feels familiar,we constuct some source tables, join them, and then export them to a conflatedtable.

You'll note that we use Dagster's config schema to parametrize our businesslogic. So, for example, we can set `export_csv_enabled` to control whether a.csv of the output data frame will be exported or not.

We also use Dagster's resource system to provide heavyweight externaldependencies. Here we provide an `conflate_emr_step_launcher` resource and a`pyspark` resource. By swapping out implementations of these resources, wecan control where and how our Pyspark jobs execute, which lets us controlcosts for development workflows.

Our step launcher is actually a fork of the open-source Dagster EMR steplauncher: we've modified it to handle packaged Airflow DAGs and dependencies,as well as to create ephemeral EMR clusters for each step rather thansubmitting jobs to a single externally managed EMR cluster. This wasstraightforward for us to implement, thanks to Dagster's pluggableinfrastructure.

What's really exciting about this is that our solid logic stays the sameregardless of where we're executing it. Solids are written in pure Pyspark, andthe step launcher implementation controls whether they execute in ephemeral EMRclusters or on our production infrastructure. The code itself doesn't changebetween dev and test.

This means that as we're developing our solids, we can work with them in Dagitfor incremental testing and a tight dev cycle.

![Developing Pyspark solids in Dagit](https://dagster.io/webflow-assets/33c2386372c047577ce5.png)

*Developing Pyspark solids in Dagit*

After we construct our Dagster pipelines from our solids, we compile them intoAirflow DAGs. Here, for example, we take a Dagster pipeline,`repository_addresses_pipeline.addresses_pipeline_v1` (containing the examplesolid above). We compile it to Airflow using`dagster_airflow.factory.make_airflow_dag`, and then edit the compiledtemplate to hook our new DAG up to an existing Airflow Task, `create_index`.

```python theme=classic
default_args = {
   "retries": 2,
   "start_date": datetime.datetime(2020, 6, 1),
   "on_failure_callback": alert_pagerduty,
   ...
}

### Make airflow dag/tasks from Dagster pipeline
dag, tasks = make_airflow_dag(
   dag_id="addresses_pipeline",
   dag_description="processing of addresses",
   module_name="repository_addresses_pipeline",
   pipeline_name="addresses_pipeline_v1",
   mode="production",
   ...,
   dag_kwargs={"default_args": default_args, "schedule_interval": "@once", ...},
)

### Add in airflow operator task
create_index = PythonOperator(
    task_id="create_index", dag=dag, python_callable=index, provide_context=True
)

### Link dagster dag with the airflow task above
### This ensures the Dagster pipeline executes prior to generating the search index in the Airflow task
for task in tasks:
   if task.task_id != "create_index":
       task.set_downstream(create_index)
```

Python

In production, we do some even fancier things -- for instance, hooking up ourcompiled DAGs to our existing library of Airflow callbacks for custom alerting.

![Integrating the Dagster solids into an existing Airflow DAG](https://dagster.io/webflow-assets/02a9b307e766d08e42a3.png)

*Integrating the Dagster solids into an existing Airflow DAG*

Our compiled Dagster pipelines sit with all our other Airflow DAG definitions,and they're parsed into the DagBag and scheduled with all the rest.

That means we can develop and test a Dagster pipeline locally using Dagstertooling, including Dagit, and then monitor production executions and view thedependencies on Airflow tasks using Airflow.

We started adopting Dagster by writing new ingestion pipelines in Dagsterinstead of in Airflow, but we’ve since moved our core conflation processinginto a Dagster pipeline.

With Dagster, we've brought a core process that used to take days or weeks of developer time down to 1-2 hours.

This has made a huge difference for developer productivity. Just to give a senseof scale, one of the goals of this project was to reduce the human time ittakes to conflate all sources in a region (i.e. state or country) down to a dayor less -- this previously took days or weeks of human effort. With the newDagster pipeline and other performance improvements, we were able to reducethis to an average of 1-2 hours. This improvement in productivity was also seenin creating new ingestion pipelines for new address data sources.

With Dagster, developers can orchestrate their pipelines locally in test,swapping in ephemeral EMR clusters to test Spark jobs instead of running on ourproduction infrastructure. Because we can now run on dev-appropriateinfrastructure, testing is so much less costly -- more than 50% -- that in thefirst few weeks of this project our engineering manager was worried there wassomething wrong with our cost reporting.

Testing is so much less costly that our engineering manager was worried there was something wrong with our cost reporting.

Dagster has let us dramatically improve our developer experience, reduce costs,and speed our ability to deliver new data products -- while providing a pathfor incremental adoption on top of our existing Airflow installation and lettingus prove the value of the new technology stack without a scratch rewrite.

*If the kind of work we're doing on the Mapbox data teams sounds interesting to you,* [*Mapbox is hiring!*](https://www.mapbox.com/careers/)


# Guides

## 10 Data Ingestion Tools to Know in 2025: Open Source + Paid

> Ingestion capabilities are important to collect structured, semi-structured, and unstructured data. By ensuring data arrives in a consistent, well-organized manner, organizations can eliminate bottlenecks associated with data processing.

Source: https://dagster.io/guides/10-data-ingestion-tools-to-know-in-2025-open-source-paid

### What Is Data Ingestion? 

Data ingestion involves importing, transferring, and loading data for further analysis and storage. It is the preliminary stage of handling vast amounts of data from diverse sources, enabling efficient data processing and analytics. 

This process can either be automated or manual, depending on the scale and complexity of the dataset involved. Data ingestion ensures data is collected in a usable format for subsequent actions such as transformation and analysis.

Ingestion capabilities are important to collect structured, semi-structured, and unstructured data. By ensuring data arrives in a consistent, well-organized manner, organizations can eliminate bottlenecks associated with data processing. 

This is part of a series of articles about data mesh.

### Key Features of Modern Data Ingestion Tools 

#### Scalability and Performance

A scalable tool adaptively handles increased data loads without performance degradation or latency issues. Performance includes both processing speed and the efficient use of computing resources. Planning for scalability ensures that future growth does not compromise performance, keeping systems responsive and capable of handling variable data loads.

#### Support for Diverse Data Sources and Formats

Data ingestion tools must accommodate various data sources and formats, ensuring data consolidation. Support for diverse data types, such as databases, CSV files, logs, and APIs ensures data gathering across platforms and applications. An inclusive tool reduces the need for bespoke conversion or pre-processing logic, supporting a pipeline from source to analysis-ready data.

#### Data Transformation Capabilities

Many ingestion tools provide built-in data transformation capabilities (or integrate with external transformation engines), allowing adaptations for analysis post-ingestion. Transformation typically involves data cleaning, filtering, enrichment, and reformatting to ensure compatibility with different analytical systems. While basic ingestion features collect data as-is, transformation capabilities prepare data in a suitable format, enhancing its utility.

#### Fault Tolerance and Reliability

Fault tolerance ensures continuous data flow despite potential setbacks like system failures or network disruptions, maintaining uninterrupted processes. A reliable data ingestion system provides mechanisms for automatic recovery, redundancy, and consistency checks. Consistent delivery and processing limit downtime and data loss, crucial for preserving system integrity.

#### Security and Compliance

Security in data ingestion includes encryption, access controls, and identity management to safeguard data movement from unauthorized access. Compliance involves ensuring data handling adheres to relevant regulations, such as GDPR or HIPAA, protecting sensitive information throughout ingestion processes. Secure data transmission improves trust, while compliance ensures adherence to legal and ethical standards.

### 10 Notable Data Ingestion Tools 

#### Open-Source Tools

##### **1. Airbyte**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fcbd-864c3306.png)

‍

Airbyte is an open-source data platform that simplifies the movement of data across various systems, enabling organizations to collect and integrate data from multiple sources into their target destinations. It allows users to sync both structured and unstructured data across cloud environments while maintaining control over security and privacy. 

**License:** MIT, ELv2

**Repo:** <https://github.com/airbytehq/airbyte>

**GitHub stars:** 16K+

**Contributors:** 1K+

**Key features of Airbyte:**

- **Extensive data source integration:** Supports over 500 data sources, including structured and unstructured data.
- **Custom connectors:** Provides a no-code or low-code environment for building custom connectors quickly.
- **AI and machine learning integration:** Simplifies AI workflows by enabling easy ingestion of unstructured data into vector databases like Pinecone and Weaviate.
- **Flexible deployment options:** Offers self-hosted, cloud, and hybrid deployment models to accommodate different infrastructure preferences and business requirements.
- **Security and compliance:** Ensures secure data handling with features like encryption, audit logs, and compliance with standards such as ISO 27001, SOC 2, GDPR, and HIPAA.

‍

![](https://dagster.io/images/guides/689a176e1b9aca99c6e8fcce-6da83e90.png)

##### **2. Apache Kafka**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fcb4-e0554bd8.png)

‍

Apache Kafka is an open-source, distributed event streaming platform used for building high-performance data pipelines, real-time analytics, and mission-critical applications. It enables organizations to publish, subscribe, store, and process streams of events in a fault-tolerant and scalable manner. 

**License:** Apache-2.0

**Repo:** <https://github.com/apache/kafka>

**GitHub stars:** 29K+

**Contributors:** 1K+

**Key features of Apache Kafka:**

- **High throughput:** Capable of delivering messages with network-limited throughput and latencies as low as 2ms.
- **Scalability:** Scales clusters to handle petabytes of data and trillions of messages, with elastic storage and processing capacity.
- **Permanent storage:** Provides durable and fault-tolerant storage for data streams across distributed clusters.
- **High availability:** Stretches across availability zones and geographical regions to ensure consistent performance and disaster recovery.
- **Built-in stream processing:** Supports stream processing features such as joins, aggregations, and exactly-once processing.

##### **3. Apache NiFi**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fcb1-14d9f330.png)

‍

Apache NiFi is an open-source data ingestion platform that automates the flow of data between systems. It enables organizations to capture, transfer, and process large amounts of data from various sources in real time. NiFi addresses challenges like network failures, data inconsistencies, and evolving data formats. 

**License:** Apache-2.0

**Repo:** <https://github.com/apache/nifi>

**GitHub stars:** 5K+

**Contributors:** 400+

**Key features of Apache NiFi:**

- **Automated dataflow management:** Automates the flow of data between systems, ensuring integration across diverse platforms.
- **Scalability and performance:** Supports high throughput and can scale to handle large volumes of data, managing spikes without impacting performance.
- **Flexible data routing and transformation:** Offers data routing, transformation, and mediation between systems with minimal configuration effort.
- **Real-time monitoring and control:** Provides real-time monitoring of dataflows with the ability to track and modify data movement visually via an intuitive interface.
- **Back pressure and error handling:** Includes built-in back pressure mechanisms and error handling to maintain data flow even under system strain.

‍

![](https://dagster.io/images/guides/689a176e1b9aca99c6e8fccb-374f1ac6.png)

##### **4. dlt**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fc9f-94544680.png)

‍

dlt (Data Loading Tool) is an open-source data framework to simplify the creation and management of modern data pipelines. It emphasizes collaboration and accessibility, allowing data teams to build efficient, reusable pipelines with minimal effort. Dlt is tailored for dynamic workflows and excels in adapting to schema changes or evolving data formats. 

**License:** Apache-2.0

**Repo:** <https://github.com/dlt-hub/dlt>

**GitHub stars:** 2K+

**Contributors:** 80+

**Key features of dlt:**

- **Declarative pipelines:** Simplifies pipeline creation using a configuration-driven approach that eliminates the need for complex scripting.
- **Change data capture (CDC):** Supports incremental data updates for database connectors, ensuring efficient processing without full data reloads.
- **Lightweight and portable:** dlt is a regular Python library and be used anywhere Python runs, without the need for complex infrastructure, containers, or backends.
- **Version control and collaboration:** Seamlessly integrates with Git for version control, enabling collaborative development.
- **Customizable:** Pre-built connectors are open-source and customizable. It’s also possible to create custom connectors using the Python API.

‍

![](https://dagster.io/images/guides/689a176f1b9aca99c6e8fcec-d12b8f16.png)

##### **5. Meltano**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fca8-b7b55879.png)

‍

Meltano is an open-source data integration platform built to empower data teams with flexible and customizable tools. It supports the entire data lifecycle, from extraction to analysis, enabling teams to manage their workflows in a streamlined and modular way. 

**License:** MIT

**Repo:** <https://github.com/meltano/meltano>

**GitHub stars:** 1K+

**Contributors:** 100+

**Key features of Meltano:**

- **Modular architecture:** Allows users to mix and match components for data extraction, loading, and transformation, tailored to their specific needs.
- **ELT workflow automation:** Simplifies the ELT process with pre-built connectors and transformation tools compatible with dbt and other frameworks.
- **Pipeline orchestration:** Provides built-in orchestration capabilities to schedule, monitor, and manage pipelines effectively.
- **DataOps friendly:** Supports iterative development, testing, and deployment, enhancing collaboration and operational efficiency.
- **Open-source and extensible:** Community-driven with the ability to extend functionality through plugins and custom components.

‍

![](https://dagster.io/images/guides/689a176f1b9aca99c6e8fcf8-7c9294bf.png)

#### Commercial Tools

##### **6. Fivetran**

![](https://dagster.io/images/guides/689a176e1b9aca99c6e8fcd1-5521d009.png)

‍

Fivetran is an automated platform that enables data replication from over 500 sources. It handles all aspects of data extraction and loading, ensuring that data pipelines remain reliable and low maintenance. 

**License:** Commercial

**Key features of Fivetran:**

- **Automated data replication:** Automatically extracts and loads data from over 500 managed connectors, ensuring consistent data availability.
- **Low-impact, high-volume replication:** Minimizes the performance impact on source systems while handling large volumes of data with low latency.
- **Incremental updates and historical loads:** After an initial full load, switches to incremental updates, ensuring that only new and updated data is synced to destinations.
- **Schema automation and drift handling:** Automatically creates and updates schemas, handling schema changes to ensure data is always organized and ready for analysis.
- **Custom connectors:** Supports custom connectors for sources not covered by pre-built connectors, enabling data ingestion from virtually any source.

‍

![](https://dagster.io/images/guides/689a176f1b9aca99c6e8fce9-e6e6d060.png)

‍

##### **7. Stitch**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fcb7-e65064a7.png)

Stitch is a cloud-first ETL platform to move data from a variety of sources to chosen destinations. With support for databases like MySQL and MongoDB, as well as SaaS applications like Salesforce and Zendesk, Stitch automates the process of extracting and replicating data, helping centralize data for analysis without the need for extensive engineering. 

**License:** Commercial, client licensed as open source

**Key features of Stitch:**

- **Data replication:** Replicates data from databases and SaaS applications to cloud data warehouses, ensuring data is available for analysis.
- **Wide range of supported sources:** Connects to hundreds of data sources, including popular databases like MySQL and MongoDB, and SaaS tools like Salesforce and Zendesk.
- **Cloud-first architecture:** Designed for cloud environments, ensuring fast data movement with minimal infrastructure overhead.
- **Automated ETL process:** Automates the extraction and loading of data, reducing manual work and enabling faster access to centralized data for analytics.
- **Schema management:** Handles schema changes automatically, ensuring that data structures in the destination are always aligned with the source systems.

‍

![](https://dagster.io/images/guides/689a176f1b9aca99c6e8fcef-fb1cf38c.png)

##### **8. Amazon Kinesis**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fcae-c27a8545.png)

‍

Amazon Kinesis is a fully managed, scalable platform for ingesting, processing, and analyzing real-time streaming data. With Kinesis Data Streams, organizations can capture large amounts of data from multiple sources such as website clickstreams, financial transactions, social media feeds, and IT logs. 

**License:** Commercial 

**Key features of Amazon Kinesis:**

- **Massively scalable data ingestion:** Handles gigabytes of data per second from hundreds of thousands of data sources, making it suitable for high-throughput use cases like clickstreams and transaction logs.
- **Real-time data availability:** Ensures data is available to analytics applications and services like AWS Lambda or Amazon Managed Service for Apache Flink within 70 milliseconds.
- **Serverless architecture:** Operates in a serverless mode, removing the need for server management. On-demand mode automatically scales to handle workload fluctuations.
- **High availability and durability:** Synchronously replicates data across three AWS Availability Zones, ensuring data durability and protection against loss. Data can be stored for up to 365 days.
- **Dedicated consumer throughput:** Supports up to 20 consumers per data stream, each with its own dedicated read throughput, allowing multiple applications to simultaneously process the same stream.

‍

![](https://dagster.io/images/guides/689a176f1b9aca99c6e8fcf2-7159f5bd.png)

##### **9. Google Cloud Dataflow**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fcab-2ea31aa1.png)

‍

Google Cloud Dataflow is a fully managed, scalable platform for real-time data ingestion, processing, and analytics. It allows teams to build streaming and batch data pipelines that process data as it's generated, supporting real-time AI/ML use cases, ETL (extract, transform, load) workflows, and advanced data integration. 

**License:** Commercial

**Key features of Google Cloud Dataflow:**

- **Real-time data ingestion and processing:** Supports the parallel ingestion and transformation of data from multiple sources, including Pub/Sub, Kafka, and databases.
- **Streaming AI and ML integration:** Supports AI/ML models with up-to-date data for more accurate predictions. Pre-built templates and MLTransform tools simplify building streaming pipelines for use cases like fraud detection and threat prevention.
- **Scalable and serverless architecture:** Automatically scales resources based on workload demands.
- **Low latency data processing:** Processes and delivers streaming data with minimal latency, making it suitable for real-time anomaly detection, dynamic pricing, and operational monitoring.
- **Pre-built templates for fast deployment:** Offers pre-designed templates for common streaming and batch processing tasks, enabling users to deploy ETL pipelines or integrate data into BigQuery, GCS, and Spanner with minimal setup.

‍

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fca5-687a031e.png)

‍

##### **10. Confluent Platform**

![](https://dagster.io/images/guides/689a176d1b9aca99c6e8fca2-c12c9eaf.png)

‍

Confluent Platform is an event streaming platform built on Apache Kafka. It provides tools and integrations to simplify building, deploying, and managing real-time data pipelines and event-driven applications. It enables organizations to move and process data across cloud and on-premises environments.

**License:** Commercial

**Key features of Confluent Platform**:

- **Kafka-based foundation**: Built on Apache Kafka, it provides high-throughput, low-latency data streaming.
- **Schema management**: Includes Confluent Schema Registry to enforce and evolve data schemas across applications.
- **Enterprise security**: Offers encryption, role-based access control, and audit logging to meet security and compliance requirements.
- **Stream processing**: Integrates with ksqlDB for real-time data processing using SQL-like queries.
- **Multi-cloud and hybrid support**: Enables deployment across cloud providers and on-premises environments.
- **Monitoring and management**: Includes Control Center for real-time monitoring and management of Kafka clusters.
- **Connectors**: Provides an array of pre-built connectors for databases, cloud services, and other systems.

‍

![](https://dagster.io/images/guides/689a176f1b9aca99c6e8fcf5-d692f6df.png)

### The Role of Data Orchestration in Data Ingestion

Data orchestration is essential in coordinating and managing the flow of data throughout the ingestion process. While data ingestion focuses on collecting and transferring data from various sources into a centralized system, orchestration handles the overall pipeline to ensure that these tasks occur in the correct order, on schedule, and with minimal disruptions. 

It ensures that data from multiple sources is efficiently processed, cleaned, and delivered to downstream systems such as data lakes, warehouses, or analytics platforms. Data orchestration involves automating the sequencing of tasks involved in ingestion, such as extraction, transformation, and loading. This automation reduces manual intervention, making it easier to manage large and complex data pipelines. 

Additionally, orchestration tools monitor the data pipeline for errors, providing automatic retries or error notifications if a task fails, ensuring the entire ingestion process remains resilient and consistent. Orchestration tools also assist with resource management. They ensure optimal utilization of resources by allocating computing power, storage, and bandwidth according to the needs of ingestion tasks. 

### Orchestrating Data Ingestion with Dagster

As a data-centric data orchestrator, Dagster allows data teams to execute their ingestion pipelines as a cohesive part of their wider data platform, enabling tighter integration between data ingestion and downstream products that depend on this data.

With a built-in toolkit for data ingestion thanks to our [Embedded ELT](https://docs.dagster.io/integrations/libraries/embedded-elt) feature and integrations with leading providers like [Fivetran](https://docs.dagster.io/integrations/libraries/fivetran) and [Airbyte](https://docs.dagster.io/integrations/libraries/airbyte), Dagster allows you to orchestrate your ETL workloads on your platform of choice.

Other features like [Dagster Pipes](https://docs.dagster.io/integrations/external-pipelines) and [Asset Checks](https://docs.dagster.io/guides/test/asset-checks) also help streamline some of the most challenging aspects of running data ingestion at scale, like setting up parallel processing on remote, distributed infrastructure and enforcing data quality checks at the point of ingestion.

To learn more about building data ingestion workloads with Dagster, see this rundown of ELT Options in Dagster.

---

## 5 Things You Need to Achieve Data Visibility

> Data visibility refers to how accessible, understandable, and useful data is within an organization.

Source: https://dagster.io/guides/5-things-you-need-to-achieve-data-visibility

### What Is Data Visibility?

Data visibility refers to how accessible, understandable, and useful data is within an organization. It entails ensuring data is readily available, comprehensible, and actionable for decision-makers. In an organizational context, data visibility allows teams to efficiently manage, process, and leverage data for various functions and decisions.

Data visibility encompasses several facets, including accessibility, quality, and timeliness of data. It involves providing a view of data assets to stakeholders, ensuring data integrity, and enabling data-driven strategies. Enhancing data visibility is critical for an organization's operational efficiency and strategic decision-making.

This is part of a series of articles about [data quality](https://dagster.io/learn/data-quality).

### Key Components: What You Need to Achieve Data Visibility

#### 1. Data Discovery

Data discovery involves identifying and cataloging data assets across an organization. This process helps uncover where data resides, its structure, and its relevance to business processes. Data discovery relies on automated tools to scan systems, databases, and applications, identifying data sources and providing metadata for each.

By enabling users to locate relevant data quickly, data discovery supports improved analytics and decision-making. It is a foundational step in achieving visibility, as it ensures that no critical data is overlooked.

#### 2. Data Classification

Data classification organizes data into categories based on its type, sensitivity, or business relevance. This step helps organizations prioritize data handling based on its importance and associated risks. For example, sensitive customer information may require stricter controls than public data.

Through consistent labeling and categorization, data classification ensures that users understand the context and proper use of data. It also supports compliance with regulatory requirements and security protocols.

#### 3. Data Integration

Data integration involves combining data from disparate sources to create a unified view. This process typically includes data extraction, transformation, and loading (ETL) or extraction, loading, and transformation (ELT), to ensure compatibility between different systems and formats. By consolidating data, integration eliminates silos and enhances cross-departmental visibility.

Integration not only streamlines operations but also provides a broader perspective, enabling better insights and more effective decision-making.

#### 4. Data Access Management

Data access management focuses on controlling who can view, modify, or share specific data. It involves implementing permissions, roles, and authentication mechanisms to protect sensitive information while ensuring appropriate access for users.

By balancing security with usability, data access management prevents unauthorized use while enabling stakeholders to access the data they need for their tasks. This component is essential for maintaining trust and compliance within an organization.

#### 5. Data Governance

Data governance establishes policies, standards, and processes for managing data throughout its lifecycle. It ensures data quality, consistency, and compliance with regulatory and ethical guidelines. Governance frameworks define roles, responsibilities, and procedures for data-related activities.

Governance practices enhance data reliability and transparency, enabling organizations to use data confidently in strategic initiatives. By providing oversight and accountability, governance supports sustained data visibility and organizational efficiency.

### Benefits of Data Visibility

Enhanced data visibility provides significant advantages for organizations, empowering them to optimize operations, enhance decision-making, and maintain compliance. Here are the key benefits:

- **Improved decision-making:** With accessible and actionable data, decision-makers can rely on accurate and timely information to guide strategic and operational decisions, reducing guesswork and increasing confidence in outcomes.
- **Increased operational efficiency:** Transparent data flows allow organizations to identify inefficiencies, streamline processes, and optimize resource allocation, reducing costs and improving productivity.
- **Enhanced data quality and accuracy:** Visibility ensures data integrity by identifying and addressing inconsistencies or errors, leading to more reliable and trustworthy datasets.
- **Better compliance and risk management:** By providing a clear view of data lineage and access logs, organizations can ensure adherence to regulatory requirements and mitigate risks associated with non-compliance.
- **Stronger collaboration and innovation:** Easy access to shared data fosters cross-departmental collaboration, enabling teams to work cohesively and innovate by leveraging insights.

***Related content: Read our guide to data observability (coming soon)***

### Challenges in Achieving Data Visibility

While data visibility provides compelling benefits, it can represent a major challenge in large organizations.

#### Fragmented Data Sources

Fragmented data sources pose significant challenges to achieving data visibility, leading to inconsistencies and inefficiencies. These scattered data pools complicate data integration and access, resulting in unreliable insights and hampering decision-making processes.

#### Managing Large Volumes of Data

Managing large volumes of data is a prevalent challenge, especially with the growing data generation and storage capacities. To ensure data visibility, organizations must implement efficient storage solutions and data access strategies to handle vast datasets without compromising performance.

#### Ensuring Data Accuracy and Consistency

Ensuring data accuracy and consistency is critical for achieving reliable data visibility, impacting decision-making and operational outcomes. Data inaccuracies can lead to misguided strategies, while inconsistencies hinder trust in data insights across the organization.

#### Data Silos Within Organizations

Data silos present a notable obstacle to data visibility, creating isolated data pockets that limit information sharing and collaboration. These silos often arise from disparate systems or departments, leading to fragmented data landscapes and reduced operational agility.

### 5 Data Visibility Best Practices

#### 1. Implementing Unified Data Platforms

Unified data platforms bring together disparate data sources into a centralized ecosystem, offering a single point of truth for organizational data. By consolidating systems and datasets, businesses can eliminate redundancies, reduce inconsistencies, and streamline data access, providing stakeholders with a clear and reliable data landscape.

Such platforms support scalability, accommodating data growth without compromising performance. They also simplify integration, allowing for smoother collaboration across teams. With all data in one place, organizations can perform analytics, derive insights, and enable better strategic planning. Unified platforms also reduce the complexity of data management, freeing up resources to focus on innovation and operational improvement.

#### 2. Leveraging AI and Machine Learning

AI and machine learning (ML) technologies enhance data visibility by automating processes such as data categorization, anomaly detection, and trend analysis. These tools enable organizations to uncover patterns in data that might not be apparent through manual analysis, providing deeper insights and enabling predictive analytics to guide decision-making.

By using AI and ML, businesses gain the ability to process vast amounts of data in real-time, supporting timely responses to market changes or operational issues. Additionally, these technologies reduce human error by standardizing data processes and ensuring consistent application of analytics models. Integrating AI and ML into data visibility practices fosters greater efficiency, agility, and accuracy, empowering organizations to stay competitive and innovative.

#### 3. Establishing Data Governance Policies

Data governance policies establish the rules, standards, and accountability measures needed to ensure data quality, security, and accessibility. These policies define clear roles for data ownership and management, ensuring that every piece of data has an assigned custodian responsible for its integrity and compliance.

Governance frameworks also provide guidance on access control, helping organizations safeguard sensitive information while promoting transparency. They play a crucial role in meeting regulatory requirements, minimizing the risk of fines or reputational damage from non-compliance. A well-defined governance structure ensures data consistency across the organization, fostering trust in data-driven decisions and enabling long-term strategic alignment.

#### 4. Continuous Monitoring and Quality Assurance

Continuous monitoring and quality assurance are essential for sustaining data visibility over time. Regularly evaluating data for accuracy, consistency, and relevance ensures that organizations can rely on their datasets for informed decision-making. Monitoring tools enable real-time detection of issues, such as system outages or data discrepancies, allowing for immediate resolution to avoid disruptions.

Automating monitoring processes can enhance efficiency by minimizing manual intervention while ensuring thorough oversight. Quality assurance measures, such as validation checks and regular audits, further reinforce data reliability. By embedding these practices into everyday operations, businesses can maintain data integrity, enhance operational performance, and build trust in their data assets among stakeholders.

#### 5. Enhancing Collaboration Across Teams

Enhancing collaboration across teams is a cornerstone of achieving comprehensive data visibility. When teams share information freely and leverage collective insights, organizations can overcome departmental silos and foster a culture of transparency. Shared access to unified data platforms ensures that all teams are working with the same accurate and up-to-date information.

Cross-functional collaboration also accelerates problem-solving and innovation, as diverse perspectives contribute to more robust solutions. Collaboration tools, such as dashboards and shared analytics platforms, enable seamless communication and real-time updates, ensuring alignment across departments.

### Improving Data Visibility with Dagster

Dagster is the unified control plane for complex, heterogeneous data platforms, allowing data teams to have a central hub for orchestrating and managing their data.

Thanks to its Integrations with the most popular data tools and the ability to import external assets into its asset graph, Dagster makes it easy to have visibility into external processes, without the need for a complete migration.

With its data asset-centric approach to orchestration, Dagster helps data teams effortlessly track data flows and lineage, while also providing a Data Catalog that makes it easy to search for and find critical data, with built-in metadata and tagging support.

To learn more about how Dagster can help your organization improve data visibility and break data silos, see this [deep dive on the Dagster+ Catalog](https://dagster.io/blog/data-catalog).

‍

‍

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## A 6-Step Data Engineering Workflow and 6 Ways to Optimize It

> A data engineering workflow involves a series of structured steps for data management, from data acquisition to applications for organizational data users.

Source: https://dagster.io/guides/a-6-step-data-engineering-workflow-and-6-ways-to-optimize-it

### What Is a Data Engineering Workflow? 

A data engineering workflow involves a series of structured steps for data management, from data acquisition to applications for organizational data users. It focuses on collecting raw data, transforming it into usable formats, and storing it in databases or data lakes. The workflow ensures data is accessible, clean, and ready for analysis, supporting various business intelligence processes. This involves using tools and technologies to automate and optimize data-related tasks.

This workflow can be complex but is crucial for unlocking data's potential. It helps organizations handle vast amounts of data efficiently, making it available for decision-making processes. Automating parts of this workflow saves time and reduces errors, enhancing data quality. These workflows are evolving with technological advancements, often incorporating machine learning and real-time data processing principles.

### Data Engineering Workflow vs. DevOps Workflow 

Data engineering and DevOps workflows serve different purposes but share similar principles. Data engineering focuses on data management processes, whereas DevOps aims to streamline software development and deployment. 

Both paradigms rely on automation to improve efficiency, reduce errors, and optimize resource use. However, their tools and methodologies differ based on their specific domain needs. In practice, data engineering workflows emphasize data ingestion, transformation, and storage, while DevOps workflows prioritize continuous integration, delivery, and deployment of software. 

Despite these differences, both data engineering and DevOps strive to enhance an organization's productivity and deliver value to users through automation.

### What Does a Typical Data Engineering Workflow Look Like? 

#### 1. Data Ingestion

Data ingestion is the process of gathering data from various sources into a centralized system for processing or storage. It involves different techniques, such as batch and real-time ingestion, depending on the specific requirements and time-sensitivity of the data. Batch processing handles large volumes at scheduled times, while real-time ingestion processes data instantly as it becomes available.

Effective data ingestion is fundamental to any data engineering workflow, as it ensures diverse data sources are accurately captured and fed into subsequent steps. Automated tools enhance this process, reducing manual effort and improving data reliability. Challenges in data ingestion include dealing with disparate data formats and ensuring minimal data loss during transfer.

#### 2. Data Storage

Data storage refers to the secure and efficient saving of collected data in repositories such as databases, data warehouses, or data lakes. The choice of storage depends on the data's nature, access needs, and scalability requirements. While databases offer structured data storage, data lakes store unstructured data, providing organizations with flexibility in analysis and processing.

Key considerations for data storage include data security, access control, and backup strategies to prevent data loss. Scalability is also vital, as data volumes grow over time, requiring adaptable storage solutions. Efficient data storage enables smooth data retrieval and operations, supporting the broader objectives of data analysis and utilization.

#### 3. Data Integration

Data integration combines data from different sources, providing a unified view for analysis. It involves synthesizing information from various datasets, ensuring consistency and coherence across the organization. This process can be complex, especially when dealing with heterogeneous data formats and systems.

Effective data integration enhances decision-making by offering a holistic picture of operations and performance. Tools and techniques like data APIs and integration platforms facilitate seamless data merging, improving collaboration and data accessibility. Overcoming challenges such as data silos and interoperability between systems is crucial for successful integration efforts, ensuring streamlined data flow.

#### 4. Data Transformation

Data transformation involves converting raw data into a structured format suitable for analysis. This process includes cleaning, normalizing, and enriching data to ensure its accuracy and relevance. Techniques such as ETL (Extract, Transform, Load) are commonly used to streamline this process, making transformed data readily available for business intelligence applications.

Efficient data transformation is crucial for deriving actionable insights from diverse data sources. It addresses challenges like data inconsistency and redundancy, providing a unified dataset that supports analysis. This step also involves maintaining data lineage, ensuring transparency and traceability throughout the workflow, which is essential for data governance.

#### 5. Data Quality and Governance

Data quality and governance ensure that the data is accurate, consistent, and secure across its lifecycle. This includes implementing practices and policies to maintain high data standards, addressing issues such as data integrity, validity, and completeness. Governance frameworks help define roles and responsibilities for data management, promoting accountability and transparency. Data stewards are often appointed within organizational units to oversee data quality and utility.

Maintaining robust data quality and governance processes is vital for building trust in data-driven decision-making. It involves regular audits, monitoring, and validation checks to identify and rectify data anomalies promptly. By enforcing clear data governance rules, organizations can mitigate risks associated with data breaches and ensure compliance with regulatory standards.

#### 6. Data Presentation

Data presentation involves delivering processed data in a comprehensible format for end-users. This step typically uses visualization tools and dashboards to summarize key insights and trends, aiding swift decision-making. Effective data presentation turns complex datasets into intuitive representations, enabling stakeholders to understand and act on the information.

The choice of presentation tools can significantly impact how data insights are perceived and interpreted. It is crucial to select visualization methods that align with the audience’s needs and the data context. By focusing on clarity and relevance, data presentation ensures that insights derived from data analysis drive strategic decisions and business growth.

***Related content:*** [***Read our guide to data engineering tools***](https://dagster.io/guides/data-engineering-tools-types-features-and-10-essential-tools-xsde6)

### Common Challenges in Data Engineering Workflows 

Here are some of the challenges organizations commonly experience when building and maintaining data engineering workflows.

#### Data Silos

Data silos occur when information is isolated in separate systems or departments, preventing seamless data access and integration. This fragmentation can lead to inconsistencies, redundancies, and gaps in data, making it difficult for teams to collaborate effectively. In a data engineering workflow, breaking down silos is critical for ensuring that data flows freely across the organization, enabling comprehensive analysis and informed decision-making.

Overcoming data silos requires implementing data integration solutions that connect disparate systems, such as data pipelines and APIs. It also involves fostering a culture of collaboration and promoting data-sharing practices within the organization.

#### Scalability

Scalability is the ability of a data engineering workflow to handle growing volumes of data without compromising performance. As organizations collect increasing amounts of data, storage, processing, and integration systems must be able to scale efficiently. Scalability challenges often arise due to limited infrastructure, inadequate tools, or inefficient processes.

To manage scalability, organizations typically adopt cloud-based solutions that offer flexible storage and processing power. Implementing distributed computing frameworks like Apache Hadoop or Spark also helps to process large datasets in parallel, improving performance. 

#### Error Handling

Error handling is a critical aspect of data engineering workflows, as it ensures the resilience and reliability of data processes. Errors can occur during ingestion, transformation, or integration due to issues such as data corruption, network failures, or incorrect formats. If not managed properly, these errors can lead to inaccurate analysis or system failures.

To mitigate errors, workflows should include robust monitoring and alerting mechanisms that detect and respond to issues in real time. Retry logic, logging, and exception handling techniques help resolve errors without interrupting the overall data pipeline.

#### Data Quality

Data quality ensures that the data used in the workflow is accurate, consistent, and reliable. Poor data quality can lead to flawed analysis, incorrect insights, and misguided business decisions. Common data quality issues include missing values, duplicates, and inconsistent formatting.

To address these challenges, organizations implement data validation checks and cleaning processes at various stages of the workflow. Automated tools can identify and correct data quality issues, while governance frameworks ensure that standards are consistently applied.

### 6 Ways to Optimize Data Engineering Workflows 

#### 1. Break Workflows into Modular Components

Breaking workflows into modular components involves dividing processes into smaller, manageable tasks. This approach simplifies development and maintenance, making it easier to identify bottlenecks and implement updates. Modular design allows teams to independently optimize segments, enhancing overall efficiency and adaptability to changes.

Adopting a modular approach supports agile development practices, fostering collaboration and innovation within teams. It facilitates integration with new tools or technologies, enabling swift response to industry changes or business needs. By isolating workflow components, organizations can test and refine each module, ensuring robust and error-free operations across the data pipeline.

#### 2. Utilize Version Control Systems

Utilizing version control systems allows teams to manage and track changes to data engineering workflows effectively. These systems facilitate collaboration by enabling multiple team members to work on the same project without conflicts. Version control ensures a record of changes, helping to revert to previous states if needed and improving accountability.

Implementing version control streamlines workflow updates and audits, as it logs who made changes and why. It supports parallel development, where teams can experiment with new workflows while maintaining stable versions in production. This approach enhances reliability and enables quicker recovery from issues, bolstering the robustness of data engineering operations.

#### 3. Implement Error Handling and Logging Mechanisms

Implementing error handling and logging mechanisms is critical in managing and identifying issues within data engineering workflows. Effective error handling ensures workflows continue to run smoothly, even when encountering unexpected data or system errors, by implementing retries or fallback procedures.

Logging mechanisms provide a detailed record of operations and any issues encountered, facilitating quick diagnosis and resolution. These logs help identify patterns or recurring errors, guiding continuous improvements to the workflow. Such practices are essential for maintaining reliability in data processing, minimizing downtime, and ensuring data accuracy.

#### 4. Implement Automated Testing

Automated testing validates the functionality of data engineering workflows. This process involves systematically running tests to check for errors or performance issues in workflows, ensuring they function as intended across updates or changes. Automated testing reduces manual oversight, allowing teams to focus on resolving complex issues rather than routine checks.

Automated testing enables continuous integration and delivery practices, providing immediate feedback on workflow changes. It helps maintain high data quality by preventing flawed workflows from advancing to production. Implementing comprehensive automated tests aids in detecting potential issues early, supporting a robust and efficient data engineering environment.

#### 5. Design Workflows to Scale with Increasing Data Volumes

Designing workflows to scale with increasing data volumes ensures that systems can handle growing data demands without performance degradation. Scalability involves optimizing processes and choosing architectures that accommodate data growth, like distributed processing and cloud-based infrastructures that automatically adjust resources based on data loads.

By focusing on scalability, organizations can efficiently manage fluctuating data volumes, reducing bottlenecks and latency. This involves implementing parallel processing and data partitioning techniques to maximize throughput and minimize storage constraints. Scalable design ensures continuous performance optimization, maintaining workflow efficiency as data volume and complexity increase.

#### 6. Track KPIs and Success Metrics

Tracking KPIs and success metrics involves measuring the performance and effectiveness of data engineering workflows. This process examines various indicators, such as data processing time, error rates, and resource utilization, to ensure alignment with organizational goals. Regular monitoring helps identify areas for improvement, driving efficiency and productivity.

By focusing on relevant KPIs, teams can make informed decisions about optimizing workflows and resource allocation. Monitoring success metrics provides insights into how data initiatives impact business outcomes, guiding strategic adjustments as needed. This approach supports continuous improvement and fosters a data-driven culture by aligning workflows with broader business objectives.

### How Data Orchestration and Data Platforms Can Improve Your Data Engineering Workflow 

#### Simplifying Management of Data Engineering Workflows

Data orchestration and data platforms simplify the management of data engineering workflows by automating the coordination of data processes across various tools, systems, and environments. Orchestration platforms enable seamless scheduling, monitoring, and execution of tasks, reducing the need for manual intervention. This ensures that data ingestion, transformation, and integration steps happen in a defined order, with minimal human oversight.

By managing dependencies and resource allocation, orchestration tools help maintain consistency across workflows, even as they scale in complexity. These platforms provide a central interface to monitor progress, troubleshoot issues, and optimize performance, reducing operational overhead and improving workflow reliability.

#### Automating Workflow Optimization

Data orchestration platforms can automatically optimize workflows based on predefined rules and real-time conditions. They automatically allocate resources, scale processes, and reroute tasks to ensure the most efficient execution. This eliminates bottlenecks and reduces the risk of human error, ensuring that workflows adjust dynamically to handle variations in data loads or system performance.

With automated optimization, data engineers can focus on higher-level tasks such as improving data quality or refining analytical models, while the platform handles routine maintenance. Additionally, orchestration tools can detect inefficiencies, making adjustments that improve throughput and reduce processing time without manual intervention.

#### Providing Visibility and Control over KPIs

Orchestration platforms offer comprehensive visibility into the performance of data engineering workflows by tracking key performance indicators (KPIs) such as data processing times, error rates, and resource utilization. These platforms provide dashboards and reports that help data teams monitor workflow health, identify inefficiencies, and make data-driven decisions to enhance performance.

This real-time insight allows for more proactive management of workflows, enabling teams to swiftly respond to issues and maintain alignment with business goals. With clear visibility into KPIs, organizations can better understand how their data operations impact broader strategic objectives, ensuring continuous workflow improvement.

### How Dagster Data Orchestration Can Improve Your Data Engineering Workflow

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle.

Thanks to its modular design and rich feature set, Dagster serves as the unified control plane for data processes across the organization, with native support for data lineage and observability, an asset catalog, data validation checks, and best-in-class testability.

With composable integrations with popular data tools, native support for executing non-Python workflows in remote environments, declarative automation, and first-class support for partitioning, Dagster lets you seamlessly centralize and scale your data processes.

‍[**Learn more about the Dagster platform here**](https://dagster.io/platform-overview)**.**

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## Data Catalog: Components, Challenges & 5 Critical Best Practices

> A data catalog is a centralized repository that provides an organized inventory of data assets within an organization.

Source: https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices

### What Is a Data Catalog?

A [data catalog](https://dagster.io/platform-overview/data-catalog) is a centralized repository that provides an organized inventory of data assets within an organization. It includes metadata, which provides details about these assets, making it easier for users to find and utilize data. The catalog acts like a library, where data sets are the books, and metadata is the information helping users discover and understand the resources available.

Data catalogs enhance data accessibility by offering a user-friendly interface for searching and managing data. They facilitate data governance by ensuring that data assets are compliant, secure, and ready for analysis. By offering insights into the origin, usage, and interrelation of data sets, data catalogs improve data management processes and foster collaboration.

This is part of a series of articles about data mesh.

### Key Components of a Data Catalog

#### Metadata Types

##### Technical Metadata

Technical metadata comprises details like file type, data structure, storage location, and access permissions. It helps IT staff manage data assets, ensuring that data conforms to required formats and security standards. By understanding the technical specifications, organizations can optimize data integration and processing workflows.

This type of metadata also enhances data utilization by specifying the technical assets available. It supports data lineage, which traces the movement and transformation of data across different systems. Accurate technical metadata helps maintain consistency and accuracy, reducing errors in data-driven processes.

##### Business Metadata

Business metadata provides context by describing the data's meaning, usage, and relevance to business processes. It includes attributes like data owner, steward, definition, and business rules. By connecting data to business terms, organizations enhance understanding and foster alignment between business and IT teams.

This metadata ensures that users comprehend data's relevance to their work. It improves decision-making by offering insights into how data impacts business strategies and objectives. When aligned with business goals, metadata guides data utilization, ensuring information supports desired outcomes.

##### Operational Metadata

Operational metadata tracks data usage statistics, processing details, and operational logs. It provides insights into how a dataset was created, how often it’s accessed, by whom, and for what purpose. This information is critical for optimizing resource allocation and improving data management processes.

By monitoring data interactions, operational metadata helps identify trends or issues, such as security breaches or inefficient processes. It supports compliance efforts by ensuring data is accessed according to organizational policies. Operational insights aid in refining workflows and enhancing overall data governance.

#### Data Lineage and Provenance

Data lineage visually represents the flow of data through its lifecycle. It outlines how data is manipulated, transformed, and transmitted across various systems. A clear lineage provides transparency, ensuring users understand the origins and transformations of the data they analyze.

Data provenance complements lineage by detailing the historical context of data, capturing its source, custody, and modifications over time. Provenance helps organizations establish data trustworthiness, crucial for regulatory compliance and maintaining data integrity. Together, lineage and provenance ensure data reliability and quality.

#### Data Classification and Tagging

Data classification involves categorizing data assets based on predefined criteria, such as sensitivity, value, or relevance to business units. Classification enhances data security by identifying sensitive information, ensuring it receives appropriate protection levels. It aids in enforcing privacy policies and controls.

Tagging complements classification by associating data sets with keywords or labels, improving searchability and discoverability. Tags enable users to locate relevant data quickly, enhancing efficiency and data utilization. Effective tagging enhances the overall usability of a data catalog, fostering seamless data interaction.

### How Data Catalogs Work

Here are some of the key mechanisms involved in a data catalog system.

#### Data Discovery and Ingestion

Data discovery involves identifying and understanding what data is available and how it can be used. Data catalogs automate this process by scanning data sources, indexing available data, and creating a searchable database. They enable users to quickly identify pertinent data sets for their needs.

Ingestion is the process of importing data into a data catalog. During ingestion, data is enriched with metadata, making it easier to locate and manage. Automation of discovery and ingestion reduces manual effort, increases speed, and ensures data is current, relevant, and easily accessible.

#### Metadata Harvesting and Management

Metadata harvesting involves extracting metadata from various sources to populate the data catalog. Automated tools can scan databases, files, and external systems to collect metadata, ensuring the catalog remains up-to-date. Metadata enriches data with context and improves findability.

Managing metadata involves ensuring its accuracy, relevance, and consistency over time. Data catalogs require regular updates and validations to maintain integrity. Well-managed metadata forms the backbone of a high-functioning data catalog, facilitating better data governance and utilization.

#### Search and Query Functionality

A robust search and query functionality is vital for effective data utilization. Data catalogs provide advanced search capabilities, allowing users to perform keyword searches, browse data hierarchies, and execute structured queries. This functionality enables users to discover data efficiently.

By offering filters and refinements, catalogs improve search precision, enhancing data retrieval. These capabilities reduce time spent on data hunting and improve productivity. Advanced search tools help users focus on analysis and decision-making rather than data management logistics.

#### Integration with Data Governance Tools

Integration with data governance tools ensures adherence to organizational policies and standards. Data catalogs connect with governance tools to enforce rules on data access, usage, and retention. They provide a centralized view of compliance, facilitating audit trails and regulatory reporting.

Such integration promotes consistency and reduces risks of data breaches. By aligning catalog features with governance initiatives, organizations can protect sensitive information, manage data sprawl, and ensure all data is used responsibly and compliantly.

### Benefits of Implementing a Data Catalog

#### Enhanced Data Discovery and Accessibility

By providing a centralized repository, data catalogs enhance data discovery and accessibility. They enable users to access, locate, and understand data assets quickly. This improved access fosters creative data use and accelerates business insights by providing a comprehensive view of available resources.

#### Improved Data Governance and Compliance

Data catalogs support improved data governance by providing visibility into data assets and their usage. They help enforce policies and ensure data adheres to compliance regulations such as GDPR or HIPAA. By tracking data lineages and metadata, catalogs allow for thorough auditing, ensuring accountability.

#### Increased Data Quality and Consistency

The systematic arrangement of data within catalogs improves [data quality](https://dagster.io/platform-overview/data-quality) and consistency. By reducing redundancy, identifying errors, and providing metadata, catalogs help ensure that data is clean, relevant, and reliable. They support data validation processes and improve decision-making accuracy.

#### Improved Collaboration Among Data Teams

Data catalogs serve as a hub for collaboration, breaking down silos between teams. By providing a clear, shared view of data assets and metadata, catalogs enable teams to work together more effectively. They foster a culture of data sharing and transparency within organizations.

### Data Catalog vs. Data Dictionary vs. Data Inventory

A data catalog, data dictionary, and data inventory all play important roles in managing data assets. A comprehensive data catalog will typically contain both a data inventory and data dictionary.

- **A data inventory** is a simpler listing of all available data assets within an organization. It identifies data sources, types, and locations but does not usually provide the extensive metadata or search capabilities found in a data catalog. The primary purpose of a data inventory is to give organizations an overview of the data they own, often for compliance or audit purposes.
- **A data dictionary** focuses primarily on defining the structure and attributes of specific data sets. It details fields, types, and relationships within a database or system. The data dictionary serves more technical audiences such as database administrators and developers, providing detailed descriptions that support database management and schema design.
- **A data catalog** is a system that provides a searchable repository of data assets, enriched with metadata to make data discoverable and accessible. It’s designed for a broad range of users, from data analysts to business teams, and typically includes metadata that supports data governance, lineage, and collaboration.

### Common Challenges When Implementing Data Catalogs

#### Keeping Metadata Up-to-Date

Maintaining current metadata is vital yet challenging due to constantly changing data environments. Automated processes can help by regularly harvesting metadata, but they require robust error checking and validation to ensure accuracy and relevance of captured information.

Organizations need to incorporate continuous feedback mechanisms where users can report discrepancies and updates. Effective catalog management includes adapting to changes in data sources and business needs, ensuring metadata remains useful for decision-making and compliance.

#### Scaling with Growing Data Volumes

As data volumes grow, scaling data catalogs becomes challenging. Efficient indexing, advanced search capabilities, and resource optimization are essential for maintaining performance and utility as data expands. Performance tuning and architecture adjustments are necessary to accommodate increasing data loads.

Moreover, integrating new data sources seamlessly requires flexible and scalable frameworks. Ensuring data catalogs can handle the influx of data from diverse sources without performance degradation is a key implementation challenge that needs strategic planning and technology selection.

#### Ensuring User Adoption and Training

User adoption is critical for a successful data catalog implementation. Training and support systems must be in place to ensure users understand how to use the catalog. Familiarizing users with its functionalities and embedding it in workflows enhances the adoption process.

Organizations should promote a culture of data literacy, emphasizing the value of data catalogs for efficient data management. User-friendly interfaces, coupled with ongoing education, can drive higher adoption rates, ensuring a solid return on investment through broad usage.

### The Role of Data Orchestration in Maintaining a Data Catalog

Data orchestration refers to the automated coordination and management of data workflows, ensuring data moves seamlessly between systems while maintaining its integrity. In the context of a data catalog, orchestration plays a critical role in keeping the catalog up-to-date and aligned with data governance policies:

- By **automating data ingestion, metadata harvesting, and synchronization** between various data sources, orchestration tools ensure that the catalog reflects real-time data changes. These tools can detect new data assets, update existing metadata, and integrate additional context or lineage information as data flows through different platforms.
- Orchestration also helps **enforce data governance** by applying predefined rules and workflows during data movement. This ensures compliance with regulations and organizational policies, such as data masking for sensitive information or data retention schedules.
- Integrating data orchestration with a data catalog **enhances the catalog's reliability**, ensuring that users always have access to the most accurate, current, and compliant data.
- Orchestration can be used to **optimize performance** by scheduling tasks like indexing or metadata refreshes during low-traffic periods, ensuring that the catalog remains efficient even as data volumes grow.

### Best Practices for Effective Data Catalogs

#### 1. Automate Metadata Collection Processes

Automation of metadata collection minimizes the time and effort required to keep a data catalog updated. Automated tools can scan data repositories to pull and refresh metadata, ensuring accuracy and efficiency. These processes support timely data availability and reduce manual errors.

Organizations can leverage AI-driven tools to enhance automation. Such tools can improve the precision of metadata extraction, adjust to dynamic data landscapes, and ensure comprehensive coverage of data assets, thereby optimizing catalog performance and reliability.

#### 2. Foster a Data-Driven Culture

Creating a data-driven culture involves promoting and embedding data usage across all levels of an organization. Encourage employees to utilize data catalogs for decision-making and innovation. Demonstrating the value of data-driven insights can drive catalog adoption and utilization.

Leadership should advocate for data literacy training programs, reinforcing the importance of data in achieving strategic goals. A supportive culture will ensure that the data catalog is an integral part of daily operations, maximizing its value across the business.

#### 3. Provide User-Friendly Interfaces

Designing user-friendly interfaces promotes efficient use and adoption of data catalogs. Interfaces should be intuitive, allowing users of all skill levels to navigate and leverage the catalog’s features. Ease of use enhances the user experience, driving engagement and satisfaction.

Incorporating features like clear navigation paths, customizable views, and context-sensitive help can improve user satisfaction. Ensuring that the design aligns with user needs will facilitate better data exploration and usage outcomes, leading to more informed decision-making.

#### 4. Regularly Update and Maintain the Catalog

Regular maintenance of a data catalog is crucial for sustaining its value. This includes updating metadata, refining search capabilities, and incorporating feedback. Routine reviews ensure data accuracy and relevance, enabling efficient and informed decision-making based on current data sets.

Engage with users to identify improvement areas and potential feature enhancements. Regular updates can address evolving business needs, maintaining the catalog's efficacy. Keeping the catalog aligned with organizational goals and data strategies ensures ongoing relevance and utility.

#### 5. Integrate with Existing Data Tools and Workflows

Seamless integration with existing tools and workflows maximizes a data catalog's utility. Interconnectivity ensures that the catalog complements other data management resources, such as BI tools or data lakes, providing a cohesive data ecosystem that enhances usability and value.

Integrations should allow for smooth data exchanges and interoperability, reducing friction in data handling tasks. By aligning the catalog with established workflows, organizations can achieve streamlined operations, enhancing productivity and data-driven decision-making across the board.

### Unify Data Orchestration and Cataloging with Dagster

Dagster helps you automate and streamline the process of indexing your data by unifying a data catalog with the data orchestrator.

As the central control plane for all data operations, Dagster already knows about all your data assets, their definitions, the tooling they depend on, and the lineage between different assets.

This allows Dagster to automatically and seamlessly extract and enrich valuable metadata about each data asset, without needing to rely on a complex toolchain of dependencies, heuristics and configurations like other tools in this space.

By presenting an easy-to-use catalog interface atop software defined data assets, it ensures that business and technical stakeholders share the same context, enhancing collaboration across the organization.

To learn more about how Dagster serves as the single source of truth for your data assets, see this [deep dive into the Dagster+ Catalog](https://dagster.io/blog/data-catalog).

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## Data Engineering Tools: Types, Features, and 10 Essential Tools

> Data engineering tools are software applications and platforms that assist in building, managing, and optimizing data pipelines.

Source: https://dagster.io/guides/data-engineering-tools-types-features-and-10-essential-tools-xsde6

### What Are Data Engineering Tools?

Data engineering tools are software applications and platforms that assist in building, managing, and optimizing data pipelines. These tools cover various stages of the data lifecycle, including data ingestion, processing, transformation, storage, orchestration, and monitoring. They ensure that data flows seamlessly through an organization, from raw input to refined outputs ready for analysis, reporting, or machine learning applications.

Different types of data engineering tools work together to address the complexity of managing large-scale data environments. For example, ingestion tools collect data, processing tools clean and transform it, and orchestration tools automate the workflow. By integrating tools for storage, governance, and monitoring, organizations can maintain efficient, secure, and scalable data pipelines that serve multiple business needs.

### Types of Data Engineering Tools

#### Ingestion Tools

Ingestion tools are responsible for collecting data from various sources, such as databases, APIs, or streaming platforms, and bringing it into a centralized system. They handle both batch and real-time data ingestion, ensuring that data flows smoothly into data lakes, warehouses, or processing systems.

These tools are useful for ensuring that raw data from different sources is collected efficiently and made available for further processing and analysis. They also handle the challenges of managing multiple data formats and ensuring that data is ingested in a reliable and scalable manner.

#### Processing Tools

Processing tools are used to manipulate raw data to prepare it for analysis or storage. These tools execute operations like filtering, sorting, and aggregating data, often across distributed systems. They enable large-scale data processing in both batch and real-time environments, allowing organizations to handle vast amounts of data efficiently.

Processing tools help convert raw or semi-structured data into more refined forms, making it easier to derive insights, apply machine learning models, or conduct detailed reporting.

#### Transformation Tools

Transformation tools convert raw data into a structured and standardized format. This process often involves data cleaning, normalization, deduplication, and other quality improvements to ensure that data is consistent and usable.

Transformation tools ensure that data meets specific format requirements for downstream applications, such as analytics or reporting platforms. By automating and standardizing transformations, these tools help maintain data accuracy and reliability across systems.

#### Orchestration Tools

Orchestration tools manage the execution and coordination of data workflows, ensuring that different tasks—such as data ingestion, processing, and transformation—are performed in the correct order. These tools automate the scheduling, monitoring, and optimization of complex data pipelines, handling dependencies between tasks.

Orchestration tools ensure that workflows run smoothly and efficiently, even as data pipelines scale in size and complexity. They also provide error handling and recovery mechanisms, making data pipelines more resilient to failures.

#### Storage and Integration Tools

Storage and integration tools provide the infrastructure for storing large volumes of data and ensuring that data is accessible across different systems. Storage tools are designed to hold raw, processed, and structured data securely and efficiently, while integration tools ensure that data can flow between systems and applications.

Together, they form the backbone of data infrastructure, enabling organizations to store, access, and share data. These tools ensure that data is always available for analysis and operational use, supporting business continuity and scalability.

#### Governance and Security Tools

Governance and security tools ensure that data is managed, accessed, and protected according to organizational policies and regulatory requirements. Governance tools provide oversight of data quality, data lineage, and metadata management, allowing organizations to maintain control over their data assets.

Security tools protect data from unauthorized access and breaches through encryption, access control, and monitoring. Together, governance and security tools help ensure compliance with data privacy laws and internal standards, while also protecting sensitive information.

#### Monitoring and Logging Tools

Monitoring and logging tools track the health and performance of data pipelines and infrastructure in real-time. Monitoring tools provide visibility into key metrics, such as system performance and data flow rates, while logging tools capture detailed records of system activities.

These tools help identify issues early, allowing engineers to troubleshoot problems and optimize pipeline efficiency. Monitoring and logging are critical for maintaining the stability and reliability of data systems, ensuring that operations run smoothly.

#### Data Visualization and Presentation

Data visualization and presentation tools allow users to create charts, graphs, dashboards, and reports that illustrate trends, patterns, and correlations within data. By providing visual representations, they make complex datasets more accessible and easier to understand, enabling quicker decision-making.

Data visualization tools help users explore and interact with data in real time. Many tools offer drag-and-drop interfaces, allowing non-technical users to create visualizations without the need for extensive coding. These tools also support various data sources, enabling the integration of data from databases, spreadsheets, and cloud storage.

### Notable Data Engineering Tools

#### 1. Dagster

![](https://dagster.io/images/guides/689a0b12bba4875a2edfa2ec-65142c12.png)

**Category:** Ingestion, processing, transformation, and orchestration tools  

**Primary function:** Data orchestration, data quality, monitoring, and logging  

**License:** Apache-2.0

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle. It features a declarative programming model, integrated lineage and observability, data validation checks, and best-in-class testability.

**Key features of Dagster:**

- ‍**Data asset-centric:** Focuses on representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for building data pipelines.  **‍**
- **Observability and monitoring:** Aims to be the “single pane of glass” for your data team, with a robust logging system, built-in data catalog, asset lineage, and quality checks.  **‍**
- **Cloud-native and cloud-ready:** Provides a managed offering with robust, managed infrastructure, elegant CI/CD capability, and support for custom infrastructure.  **‍**
- **Integrations:** Extensive library of integrations with the most popular data tools, including the leading cloud providers (AWS, GCP, Azure), ETL tools (Fivetran, Airbyte, dlt, Sling), and BI tools (Tableau, Power BI, Looker, and Sigma).  **‍**
- **Flexible:** Supports any Python workflow and lets you execute arbitrary code in other programming languages and on external environments using Dagster Pipes.  **‍**
- **Declarative Automation:** Lets you go beyond cron-based scheduling and intelligently orchestrate your pipelines using event-driven conditions that consider the overall state of your pipeline and upstream data assets.

![](https://dagster.io/images/guides/689a0b8f60ab6de42c6cb6a5-0b83675d.png)

#### 2. dbt (Data Build Tool)

![](https://dagster.io/images/guides/689a0bac884de518dfd77207-9ab2b9e7.png)

**Category**: Transformation tools  

**Main function**: Data transformation, data quality, and enrichment  

**License**: Apache 2.0

dbt (Data Build Tool) is a SQL-first platform for transforming and orchestrating data pipelines using software engineering best practices. It allows teams to collaboratively develop, test, and deploy data transformations while ensuring data quality and governance. It automates dependency management and supports CI/CD workflows, making it possible to build reliable, modular data pipelines.

**Key Features of dbt:**

- ‍**SQL-first data transformations**: Allows users to write transformations in SQL or Python, with dbt managing dependencies and executing models in the correct order.  **‍**
- **Automated testing and documentation**: Automatically tests data models before production and generates documentation for better data transparency and collaboration.  **‍**
- **CI/CD and version control**: Supports Git-enabled version control and CI/CD pipelines to deploy data models safely across development, staging, and production environments.  **‍**
- **Modular, reusable code**: Offers macros and templates to create modular data transformations that are reusable and adaptable to different datasets.  **‍**
- **Cross-team collaboration**: Enables collaboration between data science and analytics teams by supporting both SQL and Python, enabling a unified workspace for data modeling.

![](https://dagster.io/images/guides/689a0c3a4dd435d768485ffc-bfe02f19.png)

#### 3. Snowflake

![](https://dagster.io/images/guides/689a0c520587fc371d7d84ec-c73b1ace.png)

**Category**: Processing, transformation, storage and integration tools  

**Main function**: Data storage and integration, data governance and security  

**License**: Commercial

Snowflake is a platform for simplifying data engineering tasks, enabling streaming and batch data pipelines on a single system. Its platform is optimized for performance and cost-efficiency, allowing users to process data using declarative pipelines in SQL or Python without needing to manage separate infrastructure.

**Key Features of Snowflake:**

- ‍**Unified streaming and batch pipelines**: Handles both streaming and batch data ingestion, offering sub-10-second latency with Snowpipe Streaming.  **‍**
- **Dynamic tables**: Automatically manage dependencies and materialize results based on freshness targets for more efficient processing.  **‍**
- **Multi-language support**: Supports Python, SQL, Java, and more, enabling code execution in a single optimized engine.  **‍**
- **Data sharing**: Direct access to live data via Snowflake Marketplace without the need for additional pipelines.  **‍**
- **Built-in DevOps features**: Integrates with CI/CD pipelines and version control systems, simplifying deployment and management of resources programmatically.

![](https://dagster.io/images/guides/689a0c809281fced788d1fff-e1cdf2c6.png)

#### 4. BigQuery

![](https://dagster.io/images/guides/689a0c9ba8dc9e4ea782acc8-237d0061.png)

**Category**: Processing, transformation, storage and integration tools  

**Main function**: Data storage, ingestion, batch and real-time processing  

**License**: Commercial

BigQuery is a fully managed, serverless data warehouse for efficient data analysis and transformation. With its scalable, distributed architecture, BigQuery allows data engineers to process both structured and unstructured data at high speed, using SQL and Python for querying and transformation.

**Key Features of BigQuery:**

- **Serverless data warehouse**: Automatically scales resources for querying terabytes of data in seconds and petabytes in minutes, without requiring manual provisioning.  **‍**
- **Separation of compute and storage**: Enables independent scaling of storage and compute resources, improving performance and reliability while handling large-scale data workloads.  **‍**
- **Support for streaming and batch data**: Continuously ingests data with real-time streaming, or batch-load data from sources like cloud storage, databases, or files.  **‍**
- **Advanced analytics and machine learning**: Offers built-in machine learning with BigQuery ML, geospatial analytics, and support for ANSI SQL.  **‍**
- **Integration**: Integrates with popular tools such as Looker Studio and Tableau, along with external data sources like Google Sheets, Bigtable, and Cloud Storage through federated queries.

![](https://dagster.io/images/guides/689a0cb6720225efd77bb797-b032a4c3.png)

#### 5. Apache Airflow

![](https://dagster.io/images/guides/689a0cc76ec8236a98b0a975-91e488bc.png)

**Category**: Orchestration tools  

**Main function**: Data orchestration and workflow scheduling  

**License**: Apache 2.0

Apache Airflow is an open-source platform for orchestrating complex data workflows. By allowing data engineers to author, schedule, and monitor workflows programmatically, Airflow simplifies the management of data pipelines in modern data environments.

**Key Features of Apache Airflow:**

- ‍**Python-based workflow creation**: Define workflows using Python, taking advantage of standard features like loops and datetime formats to dynamically generate tasks.  **‍**
- **Scalable architecture**: Leverage Airflow’s modular design and message queue system to scale workflows from a few tasks to a large number of workers.  **‍**
- **Dynamic pipeline generation**: Airflow allows dynamic creation of pipelines, providing flexibility to adapt to changing data environments.  **‍**
- **Web interface**: Monitor and manage workflows through a modern web UI that provides real-time insights into task statuses, logs, and scheduling.  **‍**
- **Integrations**: Airflow includes pre-built operators for integration with major cloud platforms like Google Cloud, AWS, and Azure, as well as other third-party tools.

![](https://dagster.io/images/guides/689a0cd80035af5ade422bfd-e882b91c.png)

#### 6. Apache Spark

![](https://dagster.io/images/guides/689a0cf2ff6cd26e09c06125-e729e9f3.png)

**Category**: Processing tools  

**Main function**: Batch and real time data processing, data transformation  

**License**: Apache 2.0

Apache Spark is an open-source, unified engine for large-scale data analytics. It supports batch and streaming data processing, SQL analytics, data science, and machine learning on single-node machines and distributed clusters.

**Key Features of Apache Spark:**

- ‍**Unified batch and streaming processing**: Handles batch and real-time data streams using a single engine.  **‍**
- **SQL analytics**: Can execute fast, distributed ANSI SQL queries for large datasets, delivering performance often faster than traditional data warehouses.  **‍**
- **Scalable data science**: Performs exploratory data analysis (EDA) and machine learning on petabyte-scale datasets without needing to downsample.  **‍**
- **Machine learning support**: Trains machine learning models on small datasets, then scales them to clusters of thousands of machines for production use.  **‍**
- **Multi-language support**: Allows users to develop data processing tasks in Python, SQL, Scala, Java, or R.

![](https://dagster.io/images/guides/689a0d15e98dec4de5a9dd73-8a5de31a.png)

#### 7. Apache Kafka

![](https://dagster.io/images/guides/689a0d36f3dd66ce2b2cb040-6962257b.png)

**Category**: Processing tools  

**Main function**: Real-time data processing and data ingestion  

**License**: Apache 2.0

Apache Kafka is a distributed event streaming platform for building real-time data pipelines and applications. Used by many large organizations, Kafka enables data engineers to handle massive volumes of data with high throughput, fault tolerance, and low-latency performance.

**Key Features of Apache Kafka:**

- ‍**High throughput and low latency**: Delivers messages with network-limited throughput, achieving latencies as low as 2ms in large, distributed environments.  **‍**
- **Scalability**: Scales clusters to thousands of brokers and process trillions of messages per day, while elastically expanding storage and processing to meet growing data demands.  **‍**
- **Durable storage**: Kafka’s distributed, fault-tolerant architecture ensures permanent, reliable storage of data streams across clusters.  **‍**
- **Built-in stream processing**: Performs real-time data transformations, joins, aggregations, and filtering with exactly-once processing to ensure accurate, event-time analytics.  **‍**
- **Integration**: Connects to hundreds of external systems like databases, cloud storage, and more through its Connect API.

#### 8. Airbyte

![](https://dagster.io/images/guides/689a0d47e2236b23d914b8f9-295c8d8f.png)

**Category**: Ingestion tools  

**Main function**: Data ingestion and support for multiple data sources  

**License**: MIT

Airbyte is an open source ELT platform for simplifying and scaling data integration for data engineers. It provides an extensive collection of connectors that support data extraction from various sources, enabling organizations to move and load data into warehouses, lakes, and databases.

**Key Features of Airbyte:**

- ‍**Extensive data extraction**: Extracts data from over 500 API, database, and file sources, including large datasets from CDC databases and multiple systems simultaneously.  **‍**
- ‍**Incremental data loading**: Loads data into over 50 destinations, including warehouses, lakes, and databases, with support for incremental loading to keep data up-to-date.  **‍**
- **Connector builder**: Users can build custom connectors using a no-code or low-code approach, enabling self-serve data movement for any HTTP API without the need for local development environments.  **‍**
- **Unified ELT platform**: Helps manage schema changes, with users setting sync frequencies between 5 minutes to 24 hours, and handles upstream breaking changes.  **‍**
- **Tool integration**: Integrates with Dagster, dbt, and Airflow for data orchestration.

![](https://dagster.io/images/guides/689a0d5b8a6c0cd329426062-2a93a79e.png)

#### 9. Tableau

![](https://dagster.io/images/guides/689a0d777331361daf54c3fb-b18d68b4.png)

**Category**: Data visualization and presentation  

**Main function**: Data presentation and visualization  

**License**: Commercial

Tableau is a self-service analytics platform that enables data engineers to transform raw data into actionable insights. With an AI-powered interface, Tableau simplifies data visualization and analytics, offering drag-and-drop functionality and integration with AI technologies like Salesforce Einstein.

**Key Features of Tableau:**

- ‍**Intuitive data exploration**: Its drag-and-drop interface can be used with built-in AI from Salesforce Einstein to quickly explore and analyze data, enriched with business context for deeper insights.  **‍**
- **Automated data narratives**: Users can add automated, easy-to-understand narratives to dashboards, making data analysis faster and more accessible for business users.  **‍**
- **Data governance and security**: Helps ensure secure, trusted data across environments with integration for single sign-on (SSO), compliance monitoring, and industry-standard certifications like SOCII and ISO.  **‍**
- **Scalable architecture**: Scales data infrastructure without manual server configurations or hardware management.  **‍**
- **Collaboration and sharing**: Supports teamwork with features that allow users to discover, share, and collaborate on data across devices, from desktops to mobile.

![](https://dagster.io/images/guides/689a0d9088c8bdb3722eb0d8-a313de8a.png)

#### 10. Apache Hadoop

![](https://dagster.io/images/guides/689a0d9f1074ce8f59aa9dc3-21a1e19c.png)

**Category**: Processing, transformation, storage and integration tools  

**Main function**: Batch data processing and data storage  

**License**: Apache 2.0

Apache Hadoop is an open-source framework for processing and storing large amounts of data across distributed computing clusters. It enables data engineers to handle large-scale data processing by breaking tasks into smaller workloads that can be executed in parallel.

**Key Features of Apache Hadoop:**

- ‍**Distributed data storage (HDFS)**: Stores data across multiple nodes in a cluster, ensuring fault tolerance by replicating data across nodes, protecting against hardware or software failures.  **‍**
- **Parallel data processing (MapReduce)**: Supports high-performance processing by splitting large datasets into smaller tasks that are processed in parallel, reducing computation time.  **‍**
- **Scalability**: Scales from a few nodes to thousands, enabling the processing of petabytes of data on commodity hardware.  **‍**
- **Flexibility in data formats**: Can handle structured, semi-structured, and unstructured data, providing versatility in how data is stored and processed.  **‍**
- **Ecosystem integration**: Works with other big data tools such as Apache HBase, Hive, and Spark to extend its functionality for analytics and real-time data processing.

![](https://dagster.io/images/guides/689a0db1b494bc7296e414f2-cb3a5970.png)

### Dagster: The Single Pane of Glass for Your Data Platform

Dagster is the unified control plane for your data platform. It not only orchestrates your data processes but also gives you the right abstractions to effectively manage and centralize complex data processes managed by diverse data teams and running on heterogeneous compute, tooling, and infrastructure.

It features a best-in-class developer experience, a modular architecture with first-class multi-tenancy, and built-in support for complex event-driven triggers, data quality checks, and a rich data catalog.

Most importantly, it doesn’t try to supplant your existing tooling; instead, it helps you manage the fragmented data ecosystem by providing native integrations with the most popular data tooling and cloud platforms, allowing data teams to run any tool on any infrastructure at scale, while serving as the single pane of glass to orchestrate and monitor these processes.

---

## Data Engineering with Python: 4 Libraries + 5 Code Examples

> Data engineering is the practice of designing, building, and maintaining the infrastructure necessary for collecting, storing, and processing large-scale data.

Source: https://dagster.io/guides/data-engineering-with-python-4-libraries-5-code-examples

### What Is Data Engineering?

Data engineering is the practice of designing, building, and maintaining the infrastructure necessary for collecting, storing, and processing large-scale data. It focuses on creating data pipelines that integrate raw data from various sources, transform and aggregate it, and make it available for business intelligence, AI & ML, and other use cases in a reliable, available, and usable format..

Data engineers work closely with databases, cloud platforms, and large-scale distributed systems to ensure the smooth operation of data-driven applications. Their work forms the backbone of data analytics, machine learning, and other advanced analytics tasks by ensuring that data is readily accessible, clean, and well-structured.

### Technologies Used by Data Engineers and How Python Fits In

Here’s a look into some of the technologies that support data engineering.

#### Tools and Their Role in the Data Engineering Process

Data engineers rely on a variety of tools to manage the complexity of data pipelines. These tools are responsible for different stages of the data lifecycle, from ingestion and storage to transformation and delivery:

- **Data ingestion tools**: Tools like Apache Kafka and Flume are commonly used for ingesting data from multiple sources, including real-time data streams. They provide scalable, fault-tolerant systems for handling large volumes of data.
- **Data storage**: Data is stored in databases or distributed storage systems like Amazon S3, Hadoop Distributed File System (HDFS), or relational databases such as PostgreSQL. These storage solutions are designed to handle vast quantities of structured and unstructured data.
- **Data transformation**: Tools like Apache Spark and Apache Flink are used to process and transform data. They enable complex operations such as aggregations, joins, and data cleansing at scale.
- **Orchestration tools**: Workflow orchestration platforms like Dagster help automate the scheduling and management of data pipelines, ensuring that each task runs in the correct sequence and handles dependencies between different processes.

#### Programming Languages

Programming languages are essential for building data engineering infrastructure. Data engineers use various programming languages that support interaction with databases,  data transformation, and process automation:

- **Python**: Python is popular for scripting ETL pipelines, automating tasks, and managing workflow orchestration. Its rich ecosystem of libraries makes it an ideal choice for handling a wide range of data engineering tasks.
- **SQL**: Structured Query Language (SQL) is the foundational language for interacting with databases. It’s used for querying, updating, and managing relational databases, making it crucial for data extraction and transformation tasks.
- **Scala**: Often used in combination with Apache Spark, Scala is a strong language for processing large datasets in distributed computing environments. It offers high performance and scalability, making it a preferred choice for real-time data processing.

#### Why Use Python for Data Engineering?

Python serves as the basis of many data engineering tasks due to its versatility and vast ecosystem of libraries. It is used across various stages of the data pipeline, from data extraction and processing to automating workflows and even training and deploying machine learning models.

Data engineers favor Python because of its simplicity, which enables faster development and easier maintenance of complex data systems. It is especially useful for tasks like data extraction from APIs or extract-transform-load (ETL) processes. Its wide adoption in data science and engineering allows collaboration between teams, making Python a bridge between engineering and analytical tasks.Python offers several advantages for data engineering tasks:

- **Simplicity and readability:** Python's clean syntax reduces cognitive load while coding, allowing engineers to write more maintainable and less error-prone code. This ease of use accelerates the development process, enabling data engineers to quickly prototype and iterate on data solutions.
- **Support for data science and machine learning:** Python provides extensive support for data science and machine learning with libraries like Pandas, NumPy, Scikit-learn, and TensorFlow. These libraries offer tools for performing data analysis, constructing machine learning models, and integrating analytics into data engineering pipelines. Data engineers can leverage Python's capabilities to prepare data for analysis and modeling.
- **Cross-platform compatibility:** Python's cross-platform compatibility allows it to run on various operating systems like Windows, macOS, and Linux without modification. This adaptability ensures that Python can fit within diverse IT environments and work with different systems. It is useful for data engineers who need to develop solutions that are portable and can be deployed across various platforms.

**Automation and workflow orchestration:** Automation in Python is straightforward, with a variety of open source libraries available to define complex workflows. Data engineers can set up tasks to automate data processing, improving efficiency and reducing human intervention. These tools provide solutions for task scheduling, dependency management, and error handling. Python-based orchestration frameworks handle task dependencies, visualize workflows, track progress, and handle failures.

### Open Source Python Libraries for Data Engineering

Here are some of the most popular open source Python libraries used by data engineers.

#### 1. Pandas

![Pandas](https://dagster.io/images/guides/681399f654933b29e12fbe6c-db702ea8.png)

- **License:** BSD-3-Clause
- **Repo:** <https://github.com/pandas-dev/pandas>
- **GitHub stars:** 40K+
- **Contributors:** 3K+

Pandas is a library that offers data analysis and manipulation tools built on top of NumPy. It provides data structures like DataFrames and Series, which simplify handling structured data and provide functionality for cleaning and processing data. Pandas aids in transforming raw data into formats suitable for analysis, offering methods to read and write data across different formats such as CSV, Excel, or SQL databases.

The library handles data wrangling and analysis workflows, making it suitable for exploratory data analysis (EDA) tasks. Its API allows for concise expressions of complex operations, enabling quick data manipulation tasks.

![](https://dagster.io/images/guides/681399f654933b29e12fbe70-2aa953d1.png)

   Source: [PyPi](https://pypi.org/project/dataframe-image/)

#### 2. PySpark

![PySpark](https://dagster.io/images/guides/681399f654933b29e12fbe6e-7819b9cd.png)

- **License**: Apache-2.0
- **Repo**: <https://github.com/apache/spark/tree/master/python/pyspark>
- **GitHub stars**: 40K+
- **Contributors**: 2K+

PySpark is the Python API for Apache Spark, providing tools for distributed, large-scale data processing and analysis. It supports Apache Spark's core features, including Spark SQL, DataFrames, Structured Streaming, MLlib for machine learning, and Spark Core, making it suitable for both batch and real-time data workflows.

PySpark allows Python developers to process massive datasets interactively through its shell or programmatically in a distributed environment. Spark SQL and DataFrames enable the use of SQL alongside Python code for efficient data transformations and queries. The Pandas API on Spark provides a familiar interface for scaling pandas workflows to big data.

![](https://dagster.io/images/guides/681399f654933b29e12fbe72-32386b67.png)

   Source: [PySpark](https://spark.apache.org/docs/latest/api/python/_images/pyspark-remote-debug1.png)

#### 3. dbt

![dbt](https://dagster.io/images/guides/681399f654933b29e12fbe71-c36dd39e.png)

- **License**: Apache-2.0
- **Repo**: <https://github.com/dbt-labs/dbt-core>
- **GitHub stars**: 10K+
- **Contributors**: 300+

dbt (Data Build Tool) is an open-source command-line tool to simplify data transformation within data warehouses. It focuses on the "Transform" step in Extract, Load, Transform (ELT) workflows, enabling analytics engineers to create reusable and maintainable data models using SQL select statements and Python data frames.

With dbt, users can define transformations in a modular way, using version control and testing practices common in software engineering. dbt compiles SQL queries into executable workflows, which can be scheduled and executed in the data warehouse, producing views or tables. YAML files are used to define metadata, properties, and configurations.

![](https://dagster.io/images/guides/681399f654933b29e12fbe6f-6bbf1a31.png)

   Source: [dbt](https://docs.getdbt.com/img/docs/building-a-dbt-project/testing-and-documentation/f2221dc-Screen_Shot_2018-08-14_at_6.29.55_PM.png)

#### 4. DuckDB

![DuckDB](https://dagster.io/images/guides/681399f654933b29e12fbe6b-680834ca.png)

- **License**: MIT
- **Repo**: <https://github.com/duckdb/duckdb>
- **GitHub stars**: 24K+
- **Contributors**: 300+

DuckDB is an in-process analytical database system for high-performance query processing. It runs directly within the host application, requiring no external dependencies. DuckDB supports a feature-rich SQL dialect, enabling users to query, transform, and analyze data. Its portability allows it to run on Linux, macOS, and Windows across major hardware architectures.

The system supports analytical queries, leveraging a columnar engine for parallel execution. This design enables processing of datasets larger than the available memory, achieving high performance for complex aggregations and transformations. It provides native APIs for popular programming languages, including Python, R, Java, and Node.js.

![](https://dagster.io/images/guides/681399f654933b29e12fbe6d-d3158d20.png)

   Source: [DuckDB](https://duckdb.org/images/blog/odbc/duckdb_DSN_setup.png)

### Code Examples: Using Python in Data Engineering

Here are a few simple code examples that illustrate the use of Python for common data engineering tasks.

#### 1. Data Collection

Python simplifies data collection using libraries like BeautifulSoup, Scrapy, and Requests. These libraries enable engineers to scrape web pages or fetch data from APIs, gathering structured or unstructured data from various sources.

**Example: Scraping Financial Data from a Website**

```python
import requests
from bs4 import BeautifulSoup‍

url \= 'https://example.com/financial-data'
response \= requests.get(url)
soup \= BeautifulSoup(response.text, 'html.parser')‍

\# Extracting stock prices from a table
table \= soup.find('table', {'id': 'stock-prices'})
rows \= table.find\_all('tr')‍

for row in rows\[1:\]:  \# Skipping header
  cols \= row.find\_all('td')    
  print(f"Stock: {cols\[0\].text}, Price: {cols\[1\].text}")
```

This example demonstrates web scraping using BeautifulSoup to retrieve stock prices from a table on a webpage.

#### 2. Data Transformation

Data engineers often clean, reshape, or aggregate data as part of the ETL (Extract, Transform, Load) process. Python libraries like Pandas and Dask are essential for these tasks, even when handling large datasets that can't fit into memory.

**Example: Cleaning and Aggregating Sensor Data**

```python
import pandas as pd

\# Load the dataset
data \= pd.read\_csv('sensor\_data.csv')

\# Fill missing values and convert timestamp to datetime
data\['temperature'\] \= data\['temperature'\].fillna(data\['temperature'\].mean())
data\['timestamp'\] \= pd.to\_datetime(data\['timestamp'\])

\# Resample to calculate the daily average temperature
daily\_avg\_temp \= data.set\_index('timestamp').resample('D')\['temperature'\].mean()

print(daily\_avg\_temp.head())
```

In this example, Pandas is used to fill missing values, convert timestamps, and calculate the daily average temperature from a dataset of sensor readings.

#### 3. Data Storage

Python can store data in SQL and NoSQL databases. Libraries like psycopg2 allow interaction with PostgreSQL, while PyMongo supports NoSQL databases like MongoDB. Python also integrates with distributed storage solutions such as Hadoop and Parquet.

Before proceeding, please install the pymongo library. You can install it using the following command:

```python
pip3 install pymongo
```

**Example: Storing Customer Data in MongoDB**

```python
from pymongo import MongoClient

client \= MongoClient('localhost', 27017\)
db \= client\['company\_db'\]
collection \= db\['customers'\]

customer \= {
  'name': 'John Doe',
  'email': 'john@example.com',
  'purchases': \[{'item': 'Laptop', 'price': 1200}\]
}

\# Insert the document into MongoDB
collection.insert\_one(customer)

\# Query the database
result \= collection.find\_one({'name': 'John Doe'})
print(result)
```

#### 4. Data Streaming

Python can handle real-time data streams through libraries like PySpark Streaming and Kafka-Python, enabling engineers to process large amounts of incoming data continuously. These tools are valuable in real-time data pipelines where immediate processing is required.

**Example: Processing Log Data from Kafka**

```python
from kafka import KafkaConsumer

consumer \= KafkaConsumer(
  'logs\_topic',
  bootstrap\_servers=\['localhost:9092'\],
  auto\_offset\_reset='earliest',
  enable\_auto\_commit=True,
  group\_id='log-consumers'
)

for message in consumer:
  log\_entry \= message.value.decode('utf-8')
  if 'ERROR' in log\_entry:        
    print(f"Error found: {log\_entry}")
```

Here, Kafka-Python is used to consume log data from a Kafka topic in real-time. The script filters and prints log entries containing errors.

#### 5. Data Integration

Python is useful for combining data from multiple sources. Libraries like Pandas allow engineers to merge, concatenate, and reshape datasets from diverse file types such as CSV, Excel, or databases, making it easy to integrate data for analysis.

**Example: Merging Sales Data from CSV and Database**

```python
import pandas as pd
import sqlite3

\# Load CSV data
csv\_data \= pd.read\_csv('sales\_data.csv')

\# Load data from a SQLite database
conn \= sqlite3.connect('sales\_db.sqlite')
db\_data \= pd.read\_sql\_query('SELECT \* FROM sales\_records', conn)

\# Merge the datasets on the "order\_id" column
merged\_data \= pd.merge(csv\_data, db\_data, on='order\_id')

print(merged\_data.head())
```

In this example, Pandas is used to merge sales data from a CSV file with data from a SQLite database. The merged dataset is ready for further analysis or reporting.

### Orchestrating Python Data Engineering Workflows with Dagster

Dagster is a Python-based data orchestrator which seamlessly integrates with Python’s rich data engineering ecosystem, including its powerful machine learning and AI libraries. All of these are easily accessible from your data pipelines.

[Dagster Pipes](https://dagster.io/blog/dagster-pipes), meanwhile, lets you execute your existing workflows in other programming languages on external environments. This gives you the best of both worlds: the readability of Python and the flexibility to use other languages as needed, with full support for Dagster’s best-in-class data orchestration, observability, lineage, cataloging, and debugging capabilities.

Dagster also integrates with many of the most popular Python data engineering tools and libraries, such as [dbt](https://docs.dagster.io/integrations/libraries/dbt), [DuckDB](https://docs.dagster.io/integrations/libraries/motherduck/dagster-duckdb), [Pandas](https://docs.dagster.io/integrations/libraries/pandas/dagster-pandas), [Ray](https://github.com/danielgafni/dagster-ray), and more.

To learn more about how you can use Dagster in your data stack, please [see this tutorial](https://dagster.io/blog/data-engineering-in-python) on setting up a simple Dagster project as a well-structured Python package.

‍

---

## Data Lineage for Compliance: GDPR, HIPAA, and What Auditors Want

> GDPR and HIPAA auditors don't want dashboards. Learn how column-level lineage built into your pipeline architecture produces audit-ready evidence on demand.

Source: https://dagster.io/guides/data-lineage-for-compliance-gdpr-hipaa-what-auditors-want

Auditors don't evaluate dashboards. They follow a specific row of data from its raw source, through every transformation, to the number they're questioning. Compliance breaks down in the gap between what data teams typically show and what regulators require. Lineage graphs, catalog entries, and screenshots don't constitute evidence. Closing that gap means building evidence into your pipeline architecture at development time, not assembling it the week before a review.

### TL;DR

- Regulators ask for provenance of a specific field, not confirmation that a lineage tool exists.
- GDPR field-level compliance requires column-level lineage rather than table-level flow maps.
- HIPAA audits focus on access trails and minimum necessary exposure at each transformation hop.
- Manual documentation decays; teams that have moved to automated lineage report 57 percent faster audit prep.
- Teams that can't produce field-level provenance on demand are betting on how thorough the next auditor happens to be.

### What regulators actually ask data teams for

The question an auditor asks is never “do you have a lineage tool?” It’s “show me where this number came from and who touched it.” This distinction matters. Many teams treat lineage as an internal documentation exercise rather than evidence for regulatory reviews.

### GDPR specifics: provenance, retention, and right to be forgotten

GDPR asks three questions about personal data: where was it collected, what systems processed it, and can it be deleted?

#### Provenance

Proving provenance under GDPR means tracing a specific field, such as a customer email, a device ID, or a behavioral record, back to the collection point. Table-level lineage won’t satisfy this. An auditor who asks “where did this email address come from?” doesn’t want to hear that the customers table came from raw\_crm. Column-level lineage answers that customers.email originated in raw\_crm.contact\_email, was normalized to lowercase in a staging model, and passed unchanged into the reporting layer. That chain is the evidence.

Column-level lineage is required for GDPR field-level compliance. Lineage shows how personal data is collected, processed, and stored, which is the auditable traceability GDPR requires.

#### Retention and right to be forgotten

Right-to-be-forgotten requests expose incomplete lineage immediately. Personal data persists when a deletion touches only the source system because downstream assets aren’t mapped. The compliance team signs off on a deletion that isn’t complete.

Column-level lineage addresses this by showing every downstream location that holds a copy of the field in question. An incomplete lineage map produces an incomplete deletion. The audit doesn’t catch the gap. The next data breach does.

### HIPAA specifics: PHI flow, access trails, and minimum necessary

HIPAA audits are structured differently from GDPR reviews. GDPR auditors trace provenance and verify deletion. HIPAA auditors verify access: who saw protected health information (PHI), through which system, at what time, and whether that access was consistent with a documented need.

#### PHI flow

The minimum necessary standard in HIPAA requires that PHI is disclosed only to the extent needed for a specific purpose. For data pipelines, that means each transformation hop should expose only the fields required downstream. Lineage must capture not just where PHI went, but what was visible at each stage. A model that passes 40 PHI fields to an analytics layer when only 3 are needed represents a compliance gap, and table-level lineage won’t surface it.

#### Access trails

Dagster’s asset-centric approach captures the data produced at each step rather than merely logging which task ran. For HIPAA access trail requirements, this difference is significant. When an auditor asks which assets a user’s query touched, the answer should come from the orchestration system, not from reconstructed server logs.

Access logs tell auditors who accessed data and when. Lineage tells them what that data contained. A data visibility framework combines both: the lineage graph shows the transformation path, and the access log shows who traversed it.

### Why manual evidence falls apart under audit

Manual lineage documentation starts decaying the moment a pipeline changes. For example, a wiki updated in Q1 doesn’t reflect the transformation logic deployed in Q3, or a Slack thread where an engineer explained a join condition six months ago isn’t audit evidence.

A compliance officer can’t certify a transformation they can’t reproduce. When field calculation logic lives only in memory, teams reconstruct the history manually during an audit. That reconstruction slows reviews and introduces errors.

Automating lineage reduces manual data processing time, speeds up audit preparation, and eliminates the reconstruction work that slows reviews and introduces errors.

A data catalog addresses this problem for teams with documented schemas that actually hold. Pipelines that are slow-moving and dbt models that are well-commented can survive a light-touch review with a catalog updated quarterly. The problem is that most regulated pipelines aren’t stable, and the audit that matters tends to arrive when the stack is mid-migration. A catalog captures metadata at a point in time. It does not capture what actually ran during the run that produced the record being questioned.

### Column-level lineage as audit evidence

Table-level lineage is a starting point. Column-level lineage is the audit artifact.

#### What the granularity difference means

Table-level lineage tells an auditor that the customers table came from raw\_crm. Column-level lineage tells them that email came from raw\_crm.contact\_email, was lowercased in stg\_customers, and was masked to email\_hash in the reporting layer. That answer tells auditors whether PII handling was correct at each step. SOX Section 404 demands the same granularity: an auditable record of data provenance, transformation logic, and processing controls.

#### The performance trade-off

Deeper lineage capture carries a real cost, and different approaches trade visualization richness against runtime performance. An architecture where lineage is native to asset definitions sidesteps this trade-off because the dependency graph is declared at build time rather than inferred from runtime logs.

### How Dagster closes the gap

In Dagster, lineage is captured automatically when you define an asset that depends on another. The dependency graph exists before the first run. Unlike task-based orchestrators that require external integrations to reconstruct lineage after execution, Dagster captures this metadata as part of the asset definition. Post-execution reconstruction creates what amounts to an archaeology problem, where teams must parse logs to determine what actually ran.

Dagster’s column-level lineage extends this to the field level. Engineers can specify column dependencies manually for Dagster-native assets or use automated dbt integrations. The result is a field-level provenance record that lives in the orchestration system, reflects what actually ran, and doesn’t depend on a catalog entry updated on a separate schedule.

### Audit logs, RBAC, and the rest of the compliance stack

Lineage answers what happened to the data. Audit logs answer who accessed it and when. Role-based access control answers who should have been allowed to. A compliance stack that has one without the other two leaves auditors with partial answers.

#### What each layer covers

Lineage traces the transformation path: which assets produced which fields, which models consumed them, and what logic ran. Audit logs provide the access record: timestamps, user identities, query patterns. RBAC establishes the authorization boundary: which roles have permission to read or write which assets.

These three layers produce the full answer to a HIPAA or GDPR audit request. An auditor asking “did this user have authorization to access this PHI?” looks to RBAC. The follow-up question about whether they actually accessed it is answered by logs. The lineage graph tells them what that PHI contained and where it came from.

#### Regulated industries in practice

[BenchSc](https://dagster.io/customers/benchsci-leap-forward-with-dagster)i, a pharmaceutical AI company, used Dagster to coordinate disparate data sources and analytics tools across their drug discovery platform. The team reduced computational costs and data errors by materializing assets only when necessary. Every materialization is intentional and logged, which keeps the lineage graph accurate.

[Group 1001](https://dagster.io/customers/group-1001-case-study), a large insurance organization, moved hundreds of SQL Server tables and tens of millions of records in 4 months with only 2 developers. They built observability and lineage into the migration pipelines throughout. The 10x productivity gain came from pipeline state being visible and auditable throughout the migration, achieved without sacrificing observability for speed.

In both cases, the compliance evidence wasn’t assembled for the audit. It was already in the system.

### Built for regulated industries: explore Dagster+ Pro

Dagster+ Pro implements this compliance stack in a single platform: column-level provenance, asset-centric lineage, audit logs, and RBAC. Column-level lineage in the Dagster+ UI lets engineers trace how a field is created and used across the entire data platform. External Assets extend that coverage to pipelines orchestrated outside Dagster, so ingestion syncs or jobs from other orchestrators don’t create a gap in the lineage graph.

The compliance pressure is expanding beyond banking. AI pipelines face the same provenance question as GDPR-regulated data: where did this field come from, what transformed it, and who had access?

A team that builds lineage into assets at development time isn’t preparing for audits. They’re already done. The evidence is in the system, and the audit becomes a query rather than a scramble. Teams that can’t produce column-level provenance for a field on demand are betting on how thorough the next auditor is.

### FAQs about data lineage for compliance

#### Which granularity do GDPR and HIPAA auditors actually require?

Auditors require column-level lineage to verify the provenance and handling of specific fields like customer emails or protected health information. Table-level maps fail to prove that a specific sensitive field was correctly masked or deleted downstream. Automated, granular lineage results in faster audit preparation by providing this field-level evidence on demand.

#### How does lineage handle the right to be forgotten requests?

Lineage identifies every downstream asset that holds a copy or transformation of a specific user’s data, ensuring deletion requests are complete. Without column-level mapping, personal data often persists in downstream reporting tables even after the source record is removed. In Dagster, the TableColumnLineage object is used to declare these dependencies, creating an auditable trail for GDPR field-level compliance.

#### Can lineage coverage extend to assets orchestrated outside Dagster?

Dagster uses External Assets to incorporate metadata from tools like Fivetran into a single lineage graph. This prevents blind spots in the audit trail when data moves through ingestion syncs or legacy schedulers. That cross-cutting visibility matters most when data moves across systems with different orchestrators is precisely the scenario where audit trails tend to break down.

#### What is the ROI of switching from manual to automated lineage?

Automating lineage reduces manual data processing time by 85 percent and can lead to 45 percent cost savings within a year, according to Opus Technologies. Manual documentation decays immediately as pipelines change, forcing teams into data archaeology during audits. Automated systems capture lineage at development time, turning audit preparation into a query rather than a weeks-long reconstruction project.

#### How do dbt lineage and orchestrator lineage work together?

dbt provides transformation lineage within the data warehouse, while the orchestrator captures the end-to-end journey from ingestion to the final BI layer. Dagster integrates with dbt to pull these column-level dependencies into the primary UI, creating a unified evidence layer. This combined record satisfies SOX Section 404 requirements for an auditable record of data provenance and processing controls.

---

## Data Observability in 2025: Pillars, Pros/Cons & Best Practices

> Data observability refers to the ability to fully understand the health and state of data in an organization.

Source: https://dagster.io/guides/data-observability-in-2025-pillars-pros-cons-best-practices

### What Is Data Observability?

Data observability refers to the ability to fully understand the health and state of data in an organization. While data monitoring provides surface-level metrics, data observability dives deeper, enabling insights into data lineage, quality, and real-time issues. It equips teams with visibility into data workflows, identifying where bottlenecks and errors occur, ensuring continuous and reliable data delivery.

Data observability comprises techniques and tools that track the state of data as it flows through diverse systems. It involves collecting and analyzing data metrics to create an accurate view of data operations. This extends beyond simple error detection, fostering an ecosystem where data issues can be predicted and preemptively resolved. The focus is on empowering organizations to not only react to data problems post-occurrence but to proactively prevent them.

This is part of a series of articles about data quality.

### Importance of Observability in Modern Data Environments

In [modern data environments](https://dagster.io/solutions/data-modernization), data observability plays a critical role in maintaining the integrity and reliability of data. With businesses heavily relying on data-driven insights, any disruption or inaccuracy can lead to substantial financial and operational setbacks. Data observability helps ensure data pipelines function smoothly, detecting anomalies and inefficiencies before they impact the end-users, thus preserving the trust of stakeholders and customers.

As data systems evolve, they often integrate numerous platforms and technologies, each with distinct functionalities and vulnerabilities. Data observability helps consistently manage these disparate systems, offering a consolidated view that enhances decision-making. This approach addresses challenges related to data complexity, assisting organizations in mitigating risks, enhancing performance, and harnessing the full potential of their data ecosystems.

### The Five Pillars of Data Observability

#### 1. Freshness

Data freshness ensures that the information available is current and up-to-date, which is crucial for decision-making processes. Lack of data freshness can lead to relying on obsolete insights, affecting business outcomes. By monitoring timestamps and source updates, organizations ensure that data pipelines deliver the latest data available, providing users with real-time accuracy. This pillar of data observability helps minimize the risk of lag-induced errors that may have significant operational consequences.

Management of data freshness also involves recognizing patterns of data latency, allowing teams to adjust data intake processes without interrupting workflows. Understanding these patterns enables organizations to optimize resources and avoid unnecessary processing delays.

#### 2. Quality

[Data quality](https://dagster.io/platform-overview/data-quality) examines the properties of data to ensure it is accurate, complete, and reliable for downstream use. This pillar focuses on characteristics like null values, duplicate records, and consistency within expected ranges. High data quality ensures confidence in business insights, avoiding errors that arise from flawed datasets.

By continuously monitoring these attributes, teams can quickly detect and resolve issues like unexpected null values or mismatched formats, which might otherwise propagate errors throughout the system. Maintaining data quality also involves establishing thresholds for acceptable data characteristics. For example, if a given field must always contain unique values, a violation would trigger alerts, enabling timely remediation.

#### 3. Volume

Another important dimension is the quantity of data processed, flagging abnormal increases or decreases which might indicate issues such as data leaks or surges. Monitoring data volume provides insights into the operational thresholds of data systems and can preemptively indicate infrastructure stress points or data loss incidents. It is essential for capacity planning, ensuring systems are equipped to handle expected data loads without negatively impacting performance.

By setting volume benchmarks, organizations can track deviations from normal patterns, which helps in diagnosing root causes promptly. This discipline aids in maintaining an optimal balance between data inflow and processing capabilities, mitigating bottlenecks, and reducing the risk of system outages.

#### 4. Schema

Schema monitoring inspects the structure of data, ensuring compatibility across interconnected systems. When schemas evolve, they can impact data usability if not managed properly. Observability into schema changes allows teams to align transformations with existing processes, preventing compatibility issues and system failures. A focus on schema observability ensures seamless data flow and maintains the integrity of data consumers' analyses.

Maintaining schema observability also involves tracking schema changes over time, which serves as a historical record to inform future adjustments. This aids in understanding how data models must evolve to support new business requirements.

#### 5. Lineage

Data lineage provides a detailed map of the data journey from source to consumption, revealing dependencies and transformations along the way. This transparency is crucial for backtracking issues, understanding the impact of changes, and ensuring that data is reliable and traceable. It empowers stakeholders to confidently enact data-driven decisions, knowing the data’s history and transformations have been scrutinized.

Understanding data lineage is especially beneficial when troubleshooting, as it provides insights into where data has diverged from expected paths. This capability streamlines error detection and resolution, reducing time spent on manual investigations.

### Data Observability vs. Other Data Management Concepts

#### Data Observability vs. Data Quality

Data observability and data quality serve distinct but complementary roles in managing data. Data quality focuses on the correctness, consistency, and completeness of data, ensuring all information is accurate and usable. In contrast, data observability provides the infrastructure and insights needed to monitor these quality attributes continuously. Observability offers a proactive framework to troubleshoot and enhance data quality before issues escalate.

While data quality emphasizes the validation and cleaning of data, observability tools integrate within systems to provide a deeper understanding of data flow and structure. Observability enables organizations to track and rectify data quality issues in real-time, preventing degradation in data value.

#### Data Observability vs. Data Monitoring

Data monitoring involves keeping track of data flow through systems with basic metrics and alerts on performance breaches, while data observability delves deeper, offering a comprehensive view of data processes. Observability provides the context necessary for understanding the root causes of issues detected through monitoring. It incorporates anomaly detection, root-cause analysis, and predictive insights, making it a more holistic approach than mere monitoring.

While monitoring might flag an issue, observability allows teams to understand why the issue occurred, facilitating quicker and more effective resolutions. The added granularity of observability transforms data oversight from reactive to proactive, enabling faster response times and more thorough insights.

#### Data Observability vs. Data Governance

Data governance defines the policies and processes that ensure data management aligns with organizational goals, focusing on compliance, privacy, and quality, while data observability focuses on understanding and improving the state of data systems through real-time insights and monitoring. Governance sets the standards for data, while observability ensures those standards are met consistently across data lifecycles.

The synergy between data observability and governance lies in their shared goal of preserving data integrity. Observability tools enable governance frameworks by providing the metrics and insights needed to enforce policies effectively.

#### Data Observability vs. Data Testing

Data testing involves evaluating specific data sets against predefined criteria to ensure they meet certain standards before use. In contrast, data observability provides continuous insights into the entire data ecosystem, facilitating early detection of issues across data pipelines. While testing checks for particular anomalies, observability continuously monitors all data flow, enabling adaptations to changes or disruptions in real-time.

While data testing validates data at given points, observability ensures data reliability through its lifecycle with ongoing monitoring. Data observability equips organizations with comprehensive tools that maintain data integrity, providing a real-time overview that complements the more static nature of data testing.

#### Data Observability vs. Data Visibility

Data visibility refers to the ability to access and view data across various systems, focusing primarily on ensuring that data can be located, retrieved, and understood by users within an organization. It’s about making data accessible and transparent across departments, empowering stakeholders with insights into what data exists and where it resides.

Data observability, however, goes beyond visibility by enabling a deeper understanding of data health, lineage, and operational status. While visibility is concerned with the accessibility of data, observability provides a continuous, detailed look at the behavior and performance of data throughout its lifecycle. Observability tools help track data anomalies, latency, and integrity issues in real-time, providing diagnostic insights that visibility alone does not offer.

### Benefits of Implementing Data Observability

#### Improved Data Reliability and Trust

Implementing data observability leads to enhanced reliability and trust in data ecosystems. By providing comprehensive insights into data behavior, observability uncovers hidden issues, promoting consistent data delivery. This transparency assures stakeholders of data validity, fostering an environment where data-driven decisions can be safely made.

#### Faster Issue Detection and Resolution

Data observability tools significantly decrease the time required for detecting and resolving data-related issues. Continuous monitoring allows for instant identification of anomalies, streamlining troubleshooting procedures. This acts to minimize downtime for data-reliant systems, enabling uninterrupted data flow and processing, avoiding cascading issues that impact operations.

#### Enhanced Collaboration Among Data Teams

Data observability fosters improved collaboration by providing shared visibility into data pipelines for all stakeholders. It aligns teams around common data goals and improves communication channels by using a unified view of data health. This shared understanding leads to synchronized efforts in addressing data issues, enhancing overall productivity. It also supports cross-departmental initiatives by ensuring everyone has access to the same insights.

#### Increased Operational Efficiency

Integrating data observability boosts operational efficiency by automating the monitoring and resolution of data concerns. This automation reduces manual intervention, allowing teams to allocate resources to strategic initiatives. By identifying and addressing inefficiencies, organizations can streamline data processing workflows, leading to faster decision-making and execution.

### Challenges in Achieving Data Observability

#### Complex Data Pipelines and Environments

Complex data pipelines, composed of numerous interconnected systems, present significant challenges for achieving data observability. These multi-layered structures often obscure critical data flows, making it difficult to track data movement and identify issues promptly. Each element within the pipeline may have unique metrics and behaviors, complicating comprehensive visibility. Addressing this complexity requires observability tools capable of integrating disparate systems into a unified view.

To manage complex environments, organizations must implement scalable observability solutions that map intricate data interactions across platforms and technologies. Simplifying data flows through standardization and integration can mitigate some challenges, though it may not fully resolve them.

#### Data Silos Across Organizations

Data silos inherently limit observability efforts, as they partition valuable data, hindering comprehensive analysis. These isolated data sets restrict visibility, spawning inconsistency and reducing the effectiveness of observability frameworks. Removing silos requires data integration and centralized data governance strategies to ensure all data can be observed uniformly, enabling efficient monitoring and collaborative decision-making.

Breaking down silos involves fostering a culture of transparency that encourages data sharing across departments. Technological solutions, such as data lakes or warehouses, can aggregate data from various sources, yet necessitate ongoing management to retain cohesion.

#### Scaling Observability for Big Data

Scaling observability to accommodate big data environments poses a notable challenge. As data volume and complexity increase, maintaining consistent observability metrics becomes arduous, particularly when utilizing traditional monitoring tools. To scale observability, organizations need advanced tooling designed to handle high data throughput and complexity while maintaining insight accuracy and timeliness.

Implementing scalable observability solutions requires adaptable technology frameworks capable of growing with organizational data needs. This includes investing in capable infrastructure, leveraging machine learning for intelligent anomaly detection, and employing distributed monitoring solutions.

#### Integration with Existing Tools and Systems

Integrating observability solutions with existing enterprise tools and systems is another formidable challenge. Legacy systems may lack compatibility with modern observability platforms, complicating efforts to create seamless data flow across the organization. Additionally, existing tools may already provide partial observability features, creating overlap and redundancy, resulting in inefficiencies.

Strategic integration focuses on creating cohesive platforms that leverage existing investments while filling gaps. This may involve custom development or next-generation middleware solutions that bridge disparate systems. The goal is to ensure observability tools enhance and extend the capabilities of current systems, providing seamless interoperability.

### Key Features of Data Observability Tools

Data observability tools are essential for ensuring the health and reliability of modern data systems. These tools provide deep insights into data pipelines, allowing organizations to monitor, detect, and resolve issues in near real time.

Here are the key features provided by modern data observability tools:

- **Automated data monitoring**: Data observability tools offer automated, continuous monitoring of data pipelines and workflows. This feature ensures that any deviation from expected performance—such as data anomalies, schema changes, or volume fluctuations—is detected in real-time. Automated monitoring helps teams stay on top of data health without requiring manual oversight, reducing the risk of undetected issues that could disrupt operations.
- **Root-cause analysis**: A critical feature of data observability tools is their ability to conduct root-cause analysis. When issues arise, these tools trace the problem back through the data lineage to identify the source, allowing teams to quickly understand the cause and impact of the problem. This capability helps minimize downtime and prevents the recurrence of similar issues by addressing their underlying causes.
- **Data lineage tracking**: Data lineage tracking provides a comprehensive view of how data flows through various systems, detailing each transformation, handoff, and source. This feature is crucial for troubleshooting data quality issues, auditing data flows, and ensuring regulatory compliance. By visualizing the data journey, teams can pinpoint where breakdowns occur and understand the broader impact of any changes or errors.
- **Anomaly detection and alerting**: Advanced anomaly detection capabilities enable observability tools to identify unusual patterns in data before they escalate into larger issues. These tools use predefined thresholds, machine learning models, or statistical analysis to detect deviations from normal behavior. When anomalies are detected, customizable alerts are sent to relevant teams.
- **Visualization dashboards**: Data observability platforms typically include robust dashboards that offer visual representations of key metrics, trends, and health indicators across the data pipeline. These dashboards provide teams with a high-level overview of data performance and allow for drill-downs into specific areas of interest. Visualization helps in understanding complex data behaviors at a glance and aids in communicating insights to non-technical stakeholders.
- **Customizable metrics and KPIs**: To ensure observability meets organizational needs, these tools allow users to define and track custom metrics and key performance indicators (KPIs). This flexibility ensures that teams can monitor the specific aspects of data health that are most relevant to their operations, whether it's data freshness, volume thresholds, or schema changes.

### The Role of Data Platforms and Orchestration in Data Observability

Data platforms and orchestration tools play an essential role in achieving effective data observability, as they centralize, manage, and streamline data processes across complex environments. Data platforms provide a unified architecture where data is ingested, stored, transformed, and made accessible for analysis. In modern platforms, observability is embedded within this architecture, allowing teams to monitor the health and integrity of data as it moves through various systems.

[Orchestration tools](https://dagster.io/platform-overview/data-orchestration) play a dual role in enhancing data observability. First, these tools coordinate complex data pipelines, ensuring smooth execution of processes such as ingestion, transformation, and delivery. By embedding observability into these orchestrated workflows, teams gain real-time insights into the health and performance of their data systems.

Second, orchestration tools can automatically react to issues detected by observability systems. For example, if an anomaly such as data freshness delays or schema mismatches is identified, orchestration tools can trigger predefined remediation workflows, such as reprocessing data, sending alerts, or rolling back changes. This capability reduces manual intervention, ensures timely resolutions, and minimizes the impact of disruptions.

***Learn more in our detailed guide to*** [***data orchestrator***](https://dagster.io/blog/decade-of-data)

### Best Practices for Effective Data Observability

#### 1. Foster a Culture of Data Transparency

Promoting a culture of data transparency is fundamental to effective data observability. This involves creating an environment where data insights are readily accessible and communicated across teams. Transparent data practices ensure that all stakeholders understand data operations, enhancing collaboration and trust within the organization.

To instill transparency, organizations should establish clear protocols for data sharing and communication. This includes consistent documentation, regular updates, and open access to data health metrics. Encouraging data transparency empowers teams to make informed decisions, designing strategies that drive efficiency and innovation.

#### 2. Establish Clear Data Governance Policies

Implementing clear data governance policies is vital for successful data observability. Governance policies define the rules and responsibilities for managing data, ensuring consistency, integrity, and compliance across systems. These policies provide the framework for how observability efforts align with broader organizational goals, enhancing the quality and reliability of data assets.

Effective governance aligns with observability by clearly delineating the standards and practices for data management. Establishing detailed policies fosters accountability and compliance, minimizing risks associated with data manipulation or unauthorized access.

#### 3. Implement Automated Data Monitoring

Automating data monitoring is crucial for maintaining timely insights and enhancing operational efficiency. Automated monitoring offers continuous oversight and immediate alerts, minimizing manual labor while ensuring consistent data health assessments. Incorporating automation allows organizations to quickly respond to issues, freeing up human resources for strategic planning and development.

Automation in monitoring processes enables precise data tracking, flagging inconsistencies as they arise. This ensures that anomalies are rapidly identified and addressed before impacting larger systems. Automated tools also enable scalability, accommodating increasing data loads without requiring proportional resource increases.

#### 4. Regularly Audit and Validate Data Pipelines

Regular audits and validations of data pipelines are essential best practices for effective data observability. This involves continuously verifying data flows and integrity to ensure adherence to quality standards and operational expectations. Frequent audits help uncover anomalies and inefficiencies, allowing for corrective action before issues exacerbate. These practices ensure data systems operate effectively and reliably.

Validating data pipelines requires a comprehensive understanding of data flow dynamics, enabling teams to pinpoint areas requiring attention. Regular audits foster a proactive approach to observability, mitigating risks associated with data inaccuracies and inconsistencies.

#### 5. Invest in Team Training and Skill Development

Investing in team training and skill development is a critical best practice for enhancing data observability. As technology and processes evolve, equipping teams with relevant skills ensures they can effectively leverage observability tools and practices. Ongoing education and skill enhancement maintain operational efficiency and adaptability in handling data issues and optimizing observability frameworks.

Training initiatives should cover both technical and strategic aspects of observability, empowering teams with a comprehensive understanding. Skill development programs should focus on both current technologies and emerging trends, preparing teams for future advancements.

### Enabling Data Observability with Dagster

Dagster is the unified control plane for your data platform. Its data asset-aware orchestration model naturally enables data observability. The built-in asset graph and data lineage allow you to quickly perform root cause analyses, while Dagster+ Insights enable teams to identify anomalies and easily monitor the health of the data platform.

Dagster also integrates with your existing tooling and wider data stack, allowing you to quickly onboard onto the platform and leverage its robust orchestration, logging, monitoring, and metadata management capabilities to enable observability for the often opaque and fragmented modern data stack.

To learn more about data observability in Dagster, see this [deep dive into our vision for the future of data orchestration and cataloging](https://dagster.io/blog/data-catalog).

‍

---

## Data Orchestration Simplified: Process, Capabilities, and Strategies

> Data orchestration refers to the automated coordination and management of data movement and data processing across different systems and environments

Source: https://dagster.io/guides/data-orchestration-simplified-process-capabilities-and-strategies

### What Is Data Orchestration?

Data orchestration refers to the automated coordination and management of data movement and data processing across different systems and environments. It involves the integration of data from diverse sources and formats, ensuring that it flows through analytics, reporting, and AI & ML pipelines. This process is crucial for delivering accurate, timely, and contextual information, contributing to better data-driven decision-making.

[Data orchestration](https://dagster.io/platform-overview/data-orchestration) helps manage large volumes of data across various platforms. By automating these processes, organizations can reduce manual intervention, minimize errors, and optimize resource allocation. This ensures consistency, enhances data quality, and provides a unified view of information across the enterprise landscape.

This is part of a series of articles about data platforms.

### 3 Steps of Data Orchestration

Data orchestration typically involves the following stages:

#### 1. Organize Data From Different Sources

Data orchestration begins with the aggregation and organization of data from diverse sources, whether internal databases, external APIs, or flat files. This step involves ensuring data consistency and unifying disparate data formats to enable processing. Efficient data organization reduces noise and facilitates better insight extraction, which is vital for turning raw data into actionable information.

In addition, structured data organization supports scalability as data volumes grow. It provides a foundation that can adapt to increased data loads without compromising performance or accuracy.

#### 2. Transform Data for Better Analysis

Data transformation involves converting raw data into a format suitable for analysis, optimizing it for various business intelligence tools. This step can entail cleaning, enriching, and normalizing data, ensuring accuracy and relevancy. Transformation is crucial as it prepares data for efficient processing, making it ready for consumption in analytical models.

The transformation process can also include applying business logic to data sets to provide context and relevance. Organizations can gain deeper insights and predictions by aligning data with business objectives and strategic goals. Transformations should be kept agile, allowing for adjustments as business requirements evolve.

#### 3. Activate Your Data

Activating your data means leveraging processed data to generate actionable insights and deliver them to the systems, applications, or users who need them. This step ensures that data is not just collected and transformed but is also effectively used to drive decision-making and operational improvements.

In modern organizations, a primary way to activate the data is by utilizing it to train [AI and machine learning pipelines](https://dagster.io/solutions/ai). These pipelines use datasets to train predictive models and automate decision-making processes. By feeding high-quality, processed data into these systems, organizations can unlock advanced capabilities such as real-time recommendations, anomaly detection, or demand forecasting.

Data activation also involves distributing insights to relevant business units or integrating processed data into operational workflows. This might include populating dashboards, automating marketing campaigns, or optimizing supply chain operations.

### Benefits of Data Orchestration

#### Lower Costs

Data orchestration [cuts costs](https://dagster.io/platform-overview/cost-insights) by automating processes that otherwise require manual labor. Businesses can decrease headcount and redirect resources to more strategic initiatives. This efficiency comes from eliminating redundant tasks and ensuring that data resources are allocated optimally. Automation also reduces the likelihood of human error, which can lead to costly mistakes and time-consuming rework.

Additionally, data orchestration lowers operational expenses related to data storage and management. It facilitates efficient data processing and reduces waste by ensuring data flows through only the necessary channels.

#### No Data Bottlenecks

Orchestration removes data bottlenecks by ensuring timely data flow across systems. These bottlenecks often arise from manual intervention points and disconnected systems, which bog down processes and reduce overall efficiency. By automating data movement and transformation, orchestration ensures that information is available where and when it's needed, without delay or human dependency.

Furthermore, data orchestration provides a clear overview of data pathways, making it easier to identify blockages or inefficiencies. Real-time monitoring and management tools enable organizations to react swiftly to issues, thus maintaining smooth operations and consistency in output.

#### Better Data Governance

Data orchestration instills strong governance by enforcing compliance and security policies throughout the data lifecycle. It automates the application of rules and standards, ensuring that data handling complies with organizational and regulatory requirements. Governance is critical in industries like healthcare and finance, where data sensitivity is paramount.

Moreover, orchestration tools help maintain data integrity and accuracy. By automating data validation and cleaning processes, they ensure that only high-quality, reliable data is used for analysis. This enhances trust in data-driven insights and decisions.

### What Are the Use Cases of Data Orchestration?

#### Business Intelligence

Data orchestration enhances business intelligence (BI) by automating the flow of data from various sources to BI tools, ensuring accurate, real-time data availability. Without orchestration, building a pipeline would require manual intervention to aggregate, transform, and load data into analytical systems. This increases the risk of delays and errors in reporting, making decision-making slower and less reliable.

With orchestration, data is seamlessly gathered, cleaned, and delivered to BI platforms, ensuring consistency and timeliness. The automated nature of orchestration allows data teams to focus on deriving insights instead of spending time maintaining pipelines, leading to faster and more informed business decisions.

#### Marketing and Customer Analytics

In marketing and customer analytics, data orchestration streamlines the integration of customer data from multiple touchpoints—web, email, social media, and CRM systems. A regular pipeline without orchestration would require significant manual effort to reconcile data from these sources, resulting in fragmented or outdated customer profiles.

Orchestration automates this process, ensuring that data is continuously updated and unified. This enables marketers to create accurate, dynamic customer segments and run real-time, personalized campaigns. Automated orchestration also supports A/B testing and performance tracking by ensuring that data is always current, improving the ability to optimize customer engagement strategies based on the latest insights.

#### Fraud Detection and Risk Management

For fraud detection and risk management, data orchestration automates the real-time aggregation and processing of data from multiple financial systems, transactions, and external sources. Without orchestration, manual processes can introduce delays, making it difficult to detect anomalies or respond to fraud in real time.

With automated orchestration, data from various sources is constantly monitored and analyzed, enabling quicker identification of suspicious patterns or risks. The system can trigger alerts and update risk models immediately as new data arrives, minimizing response times and enhancing overall security. This automation significantly reduces the risk of human error and ensures comprehensive monitoring across systems.

#### Predictive Maintenance

In predictive maintenance, data orchestration automates the collection and processing of sensor data from machinery or equipment in real-time. Without orchestration, managing this data manually could result in delayed insights, leading to unscheduled downtimes and inefficient maintenance schedules.

With orchestration, data from sensors, logs, and other sources is continuously processed and fed into predictive models. This ensures that maintenance teams receive timely alerts about potential failures before they occur. Automated orchestration optimizes maintenance cycles, reduces downtime, and extends equipment lifespan by ensuring that decisions are based on accurate, up-to-date data.

### Common Data Challenges Orchestration Can Help Resolve

#### Complex Data Processes

Organizations often handle a range of data sources and formats, leading to labor-intensive processes. These complex workflows often involve multiple handoffs, data transformations, and compliance checkpoints, increasing the likelihood of delays and errors. Without orchestration, teams must manually ensure that each step of the data pipeline functions properly.

Data orchestration simplifies these processes by automating the coordination between different tools and systems. It enables smooth data movement through each stage, ensuring that tasks are executed in the correct sequence and that dependencies are met. This automation reduces human error and accelerates data processing, resulting in faster, more reliable insights.

#### Data Silos

Data silos occur when different departments or systems within an organization store and manage data independently, leading to fragmented information that is difficult to access and analyze holistically. Siloed data prevents organizations from gaining a unified view of operations, often resulting in redundant or inconsistent datasets that hinder decision-making.

Data orchestration breaks down these silos by integrating disparate data sources into a cohesive pipeline. It ensures that data from different systems is aggregated, standardized, and made accessible across the organization. By centralizing data flow, orchestration provides a single source of truth, supporting better collaboration and more informed decision-making.

#### Data Quality Issues

Poor data quality can severely limit the effectiveness of analytics and data-driven decision-making. Inconsistent, incomplete, or inaccurate data can lead to flawed insights, adversely affecting business outcomes. Without orchestration, maintaining high data quality often requires manual, time-consuming interventions such as error correction, validation, and cleaning.

Data orchestration addresses these challenges by automating data validation and cleaning processes as data moves through the pipeline. Orchestration tools apply predefined rules to ensure that only clean, accurate, and reliable data is used for analysis. This improves data quality and ensures consistency and trust in the insights generated from the data.

### Key Features of Data Orchestration Tools

#### Workflow Scheduling

Workflow scheduling in data orchestration tools automates task execution, ensuring timely data processing and availability. Effective scheduling reduces manual oversight, minimizes errors, and guarantees data readiness for downstream applications. Scheduled workflows can adapt to varying loads and priorities, improving operational agility and resource utilization.

Advanced scheduling capabilities allow for trigger-based or conditional task execution, optimizing efficiency. By adjusting workflows in response to specific events or conditions, organizations can maintain smooth, continuous operations.

#### Task Dependency Management

Task dependency management is crucial for harmonious data orchestration, ensuring tasks execute in the correct sequence, without conflicts or bottlenecks. Tools that manage dependencies streamline complex workflows, enabling seamless task transitions and reducing the likelihood of system disruptions. This coordination is vital for maintaining end-to-end data pipeline integrity.

Furthermore, efficient management of dependencies allows organizations to optimize resource allocation and execution parallelism. By understanding task relationships, organizations can enhance orchestration performance and reduce processing times.

#### Logging and Monitoring

Logging and monitoring capabilities provide critical insights into the orchestration processes, helping detect and address issues in real time. By tracking task execution and data flows, these features enable proactive management and troubleshooting, improving system reliability and performance. Comprehensive monitoring ensures orchestration processes run smoothly and efficiently.

In addition, detailed logs facilitate root cause analysis and allow for continuous process improvement. By identifying patterns or recurring issues, organizations can refine orchestration workflows, enhancing efficiency over time.

#### Integration With Diverse Data Sources

Integration capabilities with diverse data sources are essential for comprehensive data orchestration. This functionality ensures that data from different environments is collected, processed, and harmonized effectively. Broad integration allows organizations to access and use all relevant data, enhancing analytics and decision-making capabilities.

Moreover, integration supports data consistency and completeness across platforms, minimizing discrepancies and data silos. By facilitating unified access to data, orchestration tools improve collaboration and information flow across the enterprise.

### Strategies for Effective Data Orchestration

#### 1. Centralized Orchestration Framework

A centralized orchestration framework allows organizations to manage and control data processes across the enterprise from a single point. This promotes consistency, reduces complexity, and enhances coordination. Centralization supports comprehensive environment visibility, ensuring better resource management and process optimization.

Additionally, a centralized framework simplifies the integration and adaptation of new technologies and processes. By offering a unified platform, organizations can accelerate innovation and respond swiftly to changes in business needs.

#### 2. Modular and Reusable Pipelines

Modular and reusable pipelines in data orchestration enable efficient deployment and management of data workflows. By creating standard modules, organizations can simplify complex processes and reduce duplication of effort. This modularity promotes agility, allowing for fast adaptation to changing requirements.

Furthermore, reusable pipelines enhance consistency and reliability in data processing. They ensure that workflows maintain integrity across various contexts and minimize errors.

#### 3. Data Lineage and Metadata Management

Data lineage and metadata management provide visibility into data movement and transformation processes. Understanding how data flows and changes throughout its lifecycle is crucial for maintaining transparency and trust. These practices help track data origins, transformations, and destinations, ensuring data reliability and compliance.

In addition, effective metadata management supports efficient data discovery and utilization. Metadata provides context, helping users find, comprehend, and leverage data resources easily.

#### 4. Scalability and Parallelism

Scalability and parallelism in data orchestration enable organizations to handle growing data volumes efficiently. Scalable frameworks allow orchestration processes to adapt without performance degradation as demand increases. Parallelism facilitates concurrent task execution, optimizing resource utilization and reducing processing time.

Moreover, leveraging parallelism improves orchestration speed and effectiveness, crucial for real-time data application needs. It also supports high throughput, essential for industries with volumetric data workloads.

#### 5. Fault Tolerance and Retry Mechanisms

Implementing fault tolerance and retry mechanisms ensures robust data orchestration that can recover from interruptions and errors. These features provide resilience, maintaining process continuity and minimizing data loss. By automatically retrying failed tasks, orchestration tools maintain data flow integrity.

Furthermore, fault tolerance allows orchestration systems to identify and isolate issues quickly, preventing widespread disruption. This enhances the reliability and predictability of data operations, fostering a stable data environment.

### Dagster: The Data Orchestrator That Enables These Strategies

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle.

It boasts an intuitive programming model, representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for defining complex data processes and their dependencies.

Thanks to its modular design and rich feature set, Dagster serves as the unified control plane for data processes across the organization, with native support for lineage and observability, an asset catalog, data validation checks, and best-in-class testability.

With intuitive, composable integrations with popular data tools, native support for executing non-Python workflows in remote environments, declarative automation, and intuitive partitioning, Dagster lets you seamlessly centralize and scale your data processes.

[**Learn more about the Dagster platform here**](https://dagster.io/platform-overview)**.**

‍

---

## Data Orchestration Tools: 10 Key Features & 10 Platforms to Know

> Data orchestration tools manage data workflows, automating the movement and transformation of data across different systems.

Source: https://dagster.io/guides/data-orchestration-tools-10-key-features-10-platforms-to-know

### What Are Data Orchestration Tools?

Data orchestration tools manage data workflows, automating the movement and transformation of data across different systems. These tools simplify processes, ensuring data flows in an organized manner. They enable synchronization between various data processes, offering mechanisms to schedule, manage, and monitor data workflows. This ensures that data is available at the right time and place, supporting real-time analytics and business operations.

Organizations rely on data orchestration tools to handle intricate data pipelines, reduce manual intervention, and increase operational efficiency. They minimize errors by defining clear dependencies between tasks. These tools provide an overarching view of data processes, allowing systems to adapt to changes and scale operations without compromising data integrity.

This is part of a series of articles about data platforms.

### Key Features of Data Orchestrators

#### 1. Workflow Scheduling

Workflow scheduling enables the automation of data pipelines to run at specified times or in response to particular events. This ensures that workflows can be executed without manual intervention, reducing operational overhead.

Orchestration tools offer various scheduling options, from simple time-based scheduling (such as daily or hourly triggers) to more complex event-driven triggers. Advanced tools may support dynamic scheduling, adjusting based on workload or data availability, ensuring efficient resource usage and timely data processing across the organization.

#### 2. Task Dependency Management

Dependencies between tasks can be defined using data orchestrators, ensuring that each task begins only when its predecessor has completed successfully. This avoids situations where tasks execute prematurely, which can lead to incomplete or inaccurate data processing.

Additionally, orchestration tools often allow for conditional task execution, where a task might only run if previous tasks meet certain criteria, further enhancing the flexibility and reliability of data pipelines.

#### 3. Data Lineage

Data lineage provides a detailed record of the data's journey through various workflows, tracking where the data originates, how it transforms, and where it ends up. This capability is crucial for auditing, troubleshooting, and ensuring the accuracy of analytical results. By visualizing the flow of data through pipelines, organizations can trace specific data points, understand dependencies between tasks, and quickly identify the source of errors or anomalies.

Advanced data orchestration tools allow users to inspect and audit data transformations at each step of the pipeline, ensuring compliance with data governance standards. This visibility helps organizations maintain trust in their data by providing a clear path of how data was processed and what changes were made along the way, simplifying root-cause analysis in the event of data issues.

#### 4. Metadata Management

Metadata management in data orchestration involves organizing, storing, and maintaining information about the data being processed. Effective metadata management enables users to track key details about datasets—such as schema definitions, file formats, and version history—ensuring consistency and discoverability across various workflows.

Data orchestration tools often integrate metadata management features to improve the discoverability and traceability of datasets. These tools can automatically capture metadata during the execution of data workflows, facilitating easier debugging, optimizing resource allocation, and supporting regulatory compliance by keeping a record of how data is used, accessed, and modified throughout its lifecycle.

#### 5. Error Handling and Retry Mechanisms

Data pipelines are prone to errors, whether due to system failures, network issues, or incorrect configurations. Data orchestration tools are equipped with sophisticated error handling and retry mechanisms to mitigate these challenges.

When a task fails, the orchestrator can automatically retry it based on customizable retry logic, such as a set number of attempts or waiting intervals between retries. If the retries fail, the tool typically raises alerts, logs the error, and can trigger alternative workflows or compensating actions to handle failures gracefully, ensuring minimal disruption to business operations.

#### 6. Monitoring and Logging

Orchestration tools offer real-time tracking of task execution, allowing users to monitor the status of workflows, identify performance bottlenecks, and detect failed tasks quickly. Logs provide detailed information about each task's execution, including success and failure messages, duration, and resource consumption.

This visibility helps in diagnosing problems and in optimizing performance and resource allocation, leading to more efficient data processing and faster problem resolution.

#### 7. Integrations with Other Data Sources

Data orchestration tools can connect to databases, cloud storage systems, SaaS applications, data lakes, and big data processing platforms like Apache Spark, Hadoop, or ETL frameworks. This allows them to seamlessly move and transform data across disparate environments, support complex data workflows that may span on-premises and cloud infrastructures.

By supporting native integrations with popular services, data orchestration tools can serve as the unified control plane for heterogeneous and disparate workflows across the data platform.

#### 8. Security and Access Control

Security is a top concern for any data-related operation. These tools often support role-based access control, ensuring that only authorized personnel can access or modify workflows and sensitive data.

Many tools also integrate with existing authentication systems such as LDAP or OAuth, allowing for single sign-on (SSO) and enforcing strict access policies. Additionally, data orchestrators may include encryption for data at rest and in transit, ensuring that sensitive information is handled securely throughout the workflow.

#### 9. Visualization and UI

Data orchestration tools provide intuitive user interfaces and visualization capabilities that simplify the management of complex workflows. Many offer drag-and-drop interfaces for designing workflows visually, making it easier for users to build, configure, and manage pipelines without needing extensive coding skills.

The visualization features also extend to monitoring, where users can observe the flow of data in real time, quickly identifying bottlenecks or issues. This allows both technical and non-technical users to interact with data pipelines, fostering collaboration and reducing the learning curve associated with more technical data management tools.

#### 10. Data Quality Checks

Ensuring data quality is critical to maintaining accurate analytics and operations, and data orchestration tools support this through automated data quality checks. These checks can be implemented at various stages in the workflow to validate data for correctness, completeness, and consistency.

For example, tools can flag missing values, detect outliers, or verify that data meets predefined formatting rules. When issues are detected, the workflow can either halt, alert users, or trigger remediation processes, ensuring that only high-quality, reliable data is processed and used for downstream tasks like analytics or machine learning models.

### Notable Data Orchestration Tools

#### 1. Dagster

![Dagster](https://dagster.io/images/guides/681399f654933b29e12fbe74-c614eacf.png)

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle. It features a declarative programming model, integrated lineage and observability, data validation checks, and best-in-class testability.

- **License:** Apache-2.0
- **Repo:** <https://github.com/dagster-io/dagster/>
- **GitHub stars:** 11K+
- **Contributors:** 400+

**Key features of Dagster:**

- **Data asset-centric:** Focuses on representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for building data pipelines.
- **Observability and monitoring:** Aims to be the “single pane of glass” for your data team, with a robust logging system, built-in data catalog, asset lineage, and quality checks.
- **Cloud-native and cloud-ready:** Provides a managed offering with robust, managed infrastructure, elegant CI/CD capability, and support for custom infrastructure.
- **Integrations:** Extensive library of integrations with the most popular data tools, including the leading cloud providers (AWS, GCP, Azure), ETL tools (Fivetran, Airbyte, dlt, Sling), and BI tools (Tableau, Power BI, Looker, and Sigma).
- **Flexible:** Supports any Python workflow and lets you execute arbitrary code in other programming languages and on external environments using Dagster Pipes.
- **Declarative Automation:** Lets you go beyond cron-based scheduling and intelligently orchestrate your pipelines using event-driven conditions that consider the overall state of your pipeline and upstream data assets.

![](https://dagster.io/images/guides/681399f654933b29e12fbe9b-df4a5318.png)

#### 2. Apache Airflow

![Apache Airflow](https://dagster.io/images/guides/681399f654933b29e12fbe95-a4df6bbe.png)

Apache Airflow is an open-source platform to develop, schedule, and monitor batch-oriented workflows. It allows users to build and manage complex workflows using Python code. With Airflow’s web interface, users can track the state of their workflows and adjust as necessary.

- **License**: Apache 2.0
- **Repo**: <https://github.com/apache/airflow>
- **GitHub stars**: 36k+
- **Contributors**: 3k+

**Features of Apache Airflow:**

- **Workflows as code**: Lets users define workflows entirely using Python, enabling dynamic, customizable pipeline creation.
- **Extensible framework**: Integrates with different technologies and lets users extend functionalities as needed.
- **User interface**: Provides visibility into pipeline status, logs, and task management.
- **Scheduling and execution**: Offers scheduling capabilities with rich semantics for defining workflows that run at regular intervals.
- **Version control and collaboration**: Workflows can be stored in version control, allowing rollbacks and multi-developer collaboration.

![](https://dagster.io/images/guides/681399f654933b29e12fbe78-941cc26a.png)

   Source: Apache Airflow

#### 3. Prefect

![Prefect](https://dagster.io/images/guides/681399f654933b29e12fbe93-d9053348.png)

Prefect is a data orchestration tool that enables users to build, manage, and monitor data workflows with enhanced security, scalability, and observability. It allows users to automate workflows across different infrastructure while maintaining control and collaboration features.

- **License**: Apache 2.0
- **Repo**: <https://github.com/PrefectHQ/prefect>
- **GitHub stars**: 17k+
- **Contributors**: 300+

**Features of Prefect:**

- **Dynamic workflows**: Supports the orchestration of any Python-based workflow.
- **Enterprise-grade scalability**: Scales orchestration with features like scheduling, retries, concurrency management, and automation.
- **Fine-grained access control**: Enables teams to collaborate securely with role-based access, sharing, and permissions management.
- **Centralized monitoring and observability**: Offers a unified dashboard for tracking, deploying, and managing workflows, ensuring visibility into system metrics and logs.
- **Resilient pipelines**: Automates workflows with built-in retries and transactional semantics to ensure reliability in the face of failures.

![](https://dagster.io/images/guides/681399f654933b29e12fbe78-941cc26a.png)

   Source: [Prefect](https://orion-docs.prefect.io/latest/img/ui/cloud-dashboard.png)

#### 4. Databricks Workflows

![Databricks](https://dagster.io/images/guides/681399f654933b29e12fbe94-ded7b246.png)

Databricks Workflows is a fully managed orchestration service integrated with the Databricks Data Intelligence Platform. It allows users to create, manage, and monitor multitask workflows for ETL, analytics, and machine learning pipelines.

- **License:** Commercial

**Features of Databricks Workflows:**

- **Platform integration**: Fully integrated with Databricks, allowing automation of notebooks, Delta Live Tables, and SQL queries without external orchestration tools.
- **Reliability**: Offers guaranteed 99.95% uptime.
- **Monitoring and observability**: Provides insights into every workflow run, with notifications for failures via email, Slack, PagerDuty, or custom webhooks.
- **Batch and streaming orchestration**: Supports both scheduled batch jobs and real-time pipelines with triggers based on data availability.
- **Compute management**: Optimizes resource usage with automated job clusters that only run when scheduled, ensuring cost-effective performance for production workloads.

![](https://dagster.io/images/guides/681399f654933b29e12fbe98-27116af2.png)

   Source: [Databricks](https://www.databricks.com/sites/default/files/2023-11/db-813-blog-img-og.png?v=1700207102)

#### 5. Google Cloud Composer

![Google Cloud Composer](https://dagster.io/images/guides/681399f654933b29e12fbe92-a152be5a.png)

Google Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow and is used to automate and manage complex data workflows across cloud and on-premise environments. Cloud Composer simplifies the creation, scheduling, and monitoring of workflows using Airflow’s capabilities, without the burden of infrastructure management.

- **License:** Commercial

**Features of Google Cloud Composer:**

- **Managed Airflow environment**: Fully managed Airflow service, eliminating the need to handle infrastructure setup or maintenance.
- **Cross-cloud orchestration**: Orchestrates workflows that span multiple cloud platforms and on-premises systems.
- **DAG-based workflows**: Offers Python-based DAGs to define and schedule complex data pipelines with clear task dependencies.
- **Google Cloud integration**: Provides built-in connectors for Google Cloud services like BigQuery, Cloud Storage, and Pub/Sub, simplifying data movement and processing.
- **Scalable Kubernetes architecture**: Supports Airflow components, such as schedulers and workers, which run on Google Kubernetes Engine for scalable execution.

![](https://dagster.io/images/guides/681399f654933b29e12fbe97-fb72728d.png)

   Source: [Google Cloud](https://cloud.google.com/static/composer/docs/images/terraform-monitoring-health.png)

#### 6. Azure Data Factory

![Azure Data Factory](https://dagster.io/images/guides/681399f654933b29e12fbe8d-9040491b.png)

Azure Data Factory (ADF) is a fully managed cloud service for data orchestration and integration across hybrid environments. It enables the creation, scheduling, and monitoring of data-driven workflows, allowing organizations to transform raw data from various sources.

- **License:** Commercial

**Features of Azure Data Factory:**

- **Data connectivity**: Connects to a range of data sources, including relational, non-relational, and on-premises systems.
- **ETL and ELT pipelines**: Supports data workflows for transforming and integrating data using mapping data flows or compute services like Azure Databricks and Azure HDInsight.
- **Custom triggers**: Automates pipeline execution with event-driven triggers, enabling workflows to run based on events such as file uploads or schedule-based triggers.
- **Scalable data transformation**: Leverages Spark clusters for scaling data transformation tasks, ensuring that large datasets are processed efficiently without managing infrastructure.
- **Security and monitoring**: Features role-based access control (RBAC), integrated security with Entra ID, and monitoring through Azure Monitor to ensure data protection and pipeline visibility.

![](https://dagster.io/images/guides/681399f654933b29e12fbe86-e670fa9f.png)

   Source: [Microsoft](https://cdn-dynmedia-1.microsoft.com/is/image/microsoftcorp/val-prop-1?resMode=sharp2\&op_usm=1.5,0.65,15,0\&wid=734\&hei=414\&qlt=100\&fmt=png-alpha\&fit=constrain)

#### 7. Metaflow

![Metaflow](https://dagster.io/images/guides/681399f654933b29e12fbe88-d98601f9.png)

Metaflow is an open-source data orchestration framework to simplify the development and management of machine learning, AI, and data science workflows. Built originally by Netflix, Metaflow provides an environment to develop, scale, and deploy data-driven projects, while automatically handling aspects such as versioning, orchestration, and scaling to the cloud.

- **License**: Apache 2.0
- **Repo**: <https://github.com/Netflix/metaflow>
- **GitHub stars**: 8k+
- **Contributors**: \\\~100

**Features of Metaflow:**

- **Python-based workflows**: Allows users to write and manage workflows in plain Python, making it easy to orchestrate complex data and ML processes.
- **Scalable compute**: Leverages cloud resources, including GPUs and multi-core instances, to scale workflows as needed, enabling parallel processing and large-scale computations.
- **Automatic versioning**: Tracks and stores variables and data versions throughout the workflow, providing a complete history for easy experiment tracking and debugging.
- **Cloud integration**: Scales from local development to cloud deployments on AWS, Azure, Google Cloud, or Kubernetes without users modifying the codebase.
- **Simplified deployment**: Deploys workflows to production with a single command, integrating with existing systems and automatically handling updates in data.

![](https://dagster.io/images/guides/681399f654933b29e12fbe96-98069113.png)

   Source: [Netflix](https://miro.medium.com/v2/resize\:fit:1400/0*64YZvf3KWf7C8MXE)

#### 8. Keboola

![Keboola](https://dagster.io/images/guides/681399f654933b29e12fbe99-d06787f6.png)

Keboola is a data orchestration platform to simplify data integration, automation, and governance. By offering self-service capabilities, Keboola allows users to build, deploy, and manage data solutions without relying on IT departments.

- **License:** Commercial

**Features of Keboola:**

- **Self-service data integration**: Simplifies the process of integrating data from multiple sources, empowering users to manage their own data workflows without IT involvement.
- **Automation and orchestration**: Automates data workflows and applications, enabling faster execution of business processes with minimal manual intervention.
- **Pre-built solutions**: Offers a library of pre-built components and templates, allowing users to deploy data workflows, AI solutions, and automations with a single click.
- **Data governance and security**: Built with security and governance in mind, ensuring compliance with data regulations and providing control over data access and usage.
- **AI integration**: Provides an easy onramp to AI, enabling companies to augment datasets and build AI-powered applications and automations without complex infrastructure.

![](https://dagster.io/images/guides/681399f654933b29e12fbe9c-75091b6e.png)

   Source: [Keboola](https://dagster.io/images/guides/6593e5694166c7d653d37515-be47a46a.jpg)

#### 9. Mage

![Mage](https://dagster.io/images/guides/681399f654933b29e12fbe91-6ee69168.png)

Mage is an open-source data orchestration framework to simplify and improve the process of building, transforming, and managing data pipelines. Combining the flexibility of notebooks with modular, testable code, Mage enables developers to extract, transform, and load data.

- **License**: Apache 2.0
- **Repo**:<https://github.com/mage-ai/mage-ai>
- **GitHub stars**: 7k+
- **Contributors**: 100+

**Features of Mage:**

- **Hybrid data orchestration**: Users can schedule and manage data pipelines with interactive notebooks and reusable, modular code.
- **Multi-language support**: Users can write pipelines in Python, SQL, or R.
- **Built-in observability**: Monitors and tracks pipelines with UI tools for observability, including real-time alerts and logs.
- **Data integration**: Synchronizes data from third-party sources to internal destinations with pre-built connectors.
- **Interactive development**: Offers a notebook editor to run code interactively and preview results instantly, improving the developer experience.

![](https://dagster.io/images/guides/681399f654933b29e12fbe9d-460a2c7e.png)

   Source: Mage

#### 10. Luigi

![Luigi](https://dagster.io/images/guides/681399f654933b29e12fbe73-81aae981.png)

Luigi is a Python-based data orchestration tool for building and managing complex pipelines of batch jobs. It handles tasks such as dependency resolution, workflow management, and failure handling, allowing users to automate and monitor long-running processes.

- **License**: Apache 2.0
- **Repo**: <https://github.com/spotify/luigi>
- **GitHub stars**: 17k+
- **Contributors**: 500+

**Features of Luigi:**

- **Dependency resolution**: Automatically manages dependencies between tasks, ensuring workflows execute in the correct order.
- **Workflow management**: Handles the orchestration of thousands of long-running tasks, making it suitable for complex batch jobs.
- **Extensible integration**: Supports a variety of tools and frameworks like Hadoop, Spark, and Hive, while also allowing custom Python tasks.
- **Atomic file operations**: Guarantees that file system operations (e.g., in HDFS or local files) are atomic, preventing pipeline crashes due to partial data.
- **Visualization**: Provides a web interface with a visualizer that displays the dependency graph, helping users track task completion and identify bottlenecks.

![](https://dagster.io/images/guides/681399f654933b29e12fbe9a-8697778b.png)

   Source: [Luigi](https://raw.githubusercontent.com/spotify/luigi/master/doc/user_recs.png)

### Key Considerations for Choosing a Data Orchestration Tool

Here are some of the factors that organizations should consider when evaluating data orchestration solutions.

#### Ease of Use and Developer Productivity

Tools with intuitive user interfaces, low-code or no-code options, and clear documentation allow developers to quickly build, manage, and troubleshoot data workflows. Visual pipeline editors, drag-and-drop components, and predefined templates further enhance usability by simplifying workflow creation without requiring extensive coding knowledge.

In addition, tools that support popular programming languages like Python, SQL, or R can significantly improve productivity by allowing developers to work with familiar environments. The availability of debugging tools, real-time logging, and version control features also enables teams to iterate faster, manage changes effectively, and maintain reliable workflows.

#### Scalability and Performance

Data orchestration tools must be able to scale as the volume, variety, and velocity of data increase. This includes handling more tasks in parallel and orchestrating workflows across distributed environments like cloud platforms or on-premises systems. The chosen tool should support horizontal scaling, dynamic workload distribution, and efficient resource utilization.

Performance optimization features such as job prioritization, resource throttling, and adaptive scheduling help maintain high throughput and responsiveness, even under heavy workloads. Tools that leverage cloud-native architectures or support distributed computing frameworks like Kubernetes often provide greater scalability for large-scale data operations.

#### Error Handling and Reliability

Unexpected failures in workflows can disrupt operations. A suitable tool should provide comprehensive error management features, including automatic retries, failure notifications, and fallback mechanisms. These features ensure that the workflow can recover from failures with minimal downtime or manual intervention.

Tools that allow users to define custom retry logic, such as setting retry intervals or the number of attempts, improve flexibility in managing errors. Built-in support for checkpointing and transactional tasks ensures that workflows can resume from the last successful state, improving overall reliability and reducing the risk of data loss or corruption during failures.

#### Integration Capabilities

Integration capabilities determine how well the tool connects to existing systems and data sources. A wide range of connectors ensures seamless data flow across different platforms, enhancing data accessibility and processing capabilities.

The tool should integrate smoothly with existing data pipelines, databases, and analytical tools, supporting diverse data processing environments. Integration flexibility reduces the need for extensive custom solutions and ensures quick deployment into existing workflows.

#### Observability and Monitoring

The data orchestration tool should provide real-time insights into the status of workflows, helping teams identify and resolve issues quickly. Monitoring capabilities such as health checks, performance metrics, and detailed logs enable proactive detection of bottlenecks, delays, or failures.

Comprehensive alerting features, including integration with notification systems like Slack, PagerDuty, or email, allow teams to respond to issues promptly. The tool should also offer dashboards or visualizations that display key metrics, such as task durations, resource utilization, and success/failure rates.

#### Modularity and Reusability

A modular architecture enables the reuse of components across different workflows. Modularity allows users to break down workflows into smaller, reusable tasks or components, promoting efficiency and consistency. Reusable components reduce the time needed to build new workflows, as developers can easily leverage existing modules without rewriting code or logic.

In addition, tools that support the creation of templates, reusable functions, or workflow libraries help maintain standardization across different projects. Modular designs accelerate development and simplify maintenance, as individual components can be updated or replaced without affecting the entire pipeline.

#### Security and Compliance

The data orchestration tool must support encryption for data at rest and in transit to protect sensitive information. Role-based access control (RBAC), audit logging, and multi-factor authentication (MFA) are key features that ensure only authorized personnel can access or modify workflows.

Compliance with standards such as GDPR, HIPAA, or SOC 2 is another critical factor to consider. Tools that offer built-in compliance features, such as data masking, logging of sensitive operations, and automated audits, help organizations meet regulatory requirements more easily.

#### Extensibility and Customization

A tool that offers APIs, SDKs, or plugin support allows developers to extend its functionality by integrating custom logic, third-party services, or bespoke components. This flexibility is crucial for organizations with unique requirements that may not be fully addressed by out-of-the-box features.

Customization options, such as user-defined workflows, custom operators, or specialized connectors, also allow teams to adapt the tool to their specific infrastructure or business processes. Tools with open-source components or active developer communities often offer better support for extensibility, as users can contribute or use community-built plugins.

#### Community Support

An active community and support system are Access to a vibrant community fosters collaboration and problem resolution, providing broader insights into effective tool use. Additionally, professional support and documentation are useful for training users and resolving complex problems.

### Why Choose Dagster?

Unlike other orchestrators, Dagster aims to be the unified control plane for your data platform. It not only orchestrates your data processes but also provides a flexible, programmable platform allowing multifunctional data teams to run any tool on any infrastructure at scale, while serving as the single pane of glass to orchestrate and monitor these processes.

It features native integrations with the most popular data tooling and cloud platforms, a best-in-class developer experience, and built-in support for complex event-driven triggers, data quality checks, and a rich data catalog.

[**Learn more about the Dagster platform**](https://dagster.io/platform-overview)

‍

---

## Data Pipeline Architecture: 5 Design Patterns with Examples

> Data pipelines architecture automates the collection, processing, and transfer of data from various sources to destinations for analysis or storage.

Source: https://dagster.io/guides/data-pipeline-architecture-5-design-patterns-with-examples

### What Is a Data Pipeline Architecture?

Data pipeline architecture is the process of designing how data is collected, processed, and transferred from different sources to destinations for analysis or storage. It encapsulates the end-to-end flow of data, ensuring data is efficiently managed and made valuable for business use. An effective architecture eliminates manual interventions, reducing errors and enhancing the speed of data handling processes.

A data pipeline architecture determines the stages included in the pipeline, including data ingestion, transformation, and storage. These stages are interconnected through a series of processes that allow data flow. The architecture ensures that raw data is converted into a clean, structured format that can be used for analytical purposes. An effective architecture should also integrate mechanisms to address issues such as latency and scalability to optimize data processing.

### The Main Components of a Data Pipeline Architecture

#### Sources

In a data pipeline architecture, sources are the origin points from which data is ingested. These can include databases, APIs, application logs, IoT devices, or any other systems generating data. The selection of sources depends on the business objectives and the nature of the data required for analysis. Properly defining and managing data sources is crucial to ensuring data accuracy and relevance as it enters the pipeline.

The architecture must support a variety of data sources, including both structured and unstructured data. This support ensures that a broad spectrum of data is captured without losing integrity or context. Integration with multiple sources requires mechanisms to extract data efficiently while minimizing the impact on the performance of source systems.

#### Data Ingestion

Data ingestion is the process of collecting and Effective data ingestion requires handling diverse data formats, ensuring fault tolerance, and managing data consistency across ingestion pipelines. Organizations often use tools like Apache Kafka or AWS Kinesis to support high-throughput, low-latency ingestion. Ensuring scalability and resilience at this stage prevents bottlenecks and enables smooth downstream processing.

#### Destination / Sinks

Destinations, or sinks, are where processed data is stored or used after passing through the pipeline. They can be data warehouses, data lakes, data marts, or cloud storage.. The design of these destinations is crucial as it determines how quickly and flexibly data can be accessed and used. Choosing the right destination depends on factors like data volume, type, and intended use.

A well-designed data pipeline ensures that data moving to these destinations is easily accessible for data scientists and analysts. It also considers performance optimization to handle large datasets swiftly. Data governance policies are typically enforced at this stage to ensure compliance with regulatory requirements and to promote data quality and security across all stored datasets.

#### Data Processing and Transformation

Transformations involve converting raw data into a format that can be readily used for analysis. This stage may include cleaning, filtering, aggregating, or enriching data to align with business rules and logic. The objective is to enhance the value of data by making it relevant and insightful for business decisions. Transformation processes also safeguard the integrity and accuracy of converted data.

Besides basic conversions, data transformations often involve applying algorithms to derive deeper insights. This stage might employ machine learning models, statistical analyses, or other computational methods to generate actionable intelligence. Business logic is also applied here to ensure that the transformed data aligns closely with organizational goals.

#### Data Governance, Observability, and Quality

Data governance ensures that data is managed according to policies and regulations, maintaining integrity, security, and compliance. This includes defining roles and responsibilities for data management, enforcing access controls, and implementing audit trails. Governance frameworks help prevent unauthorized data modifications and ensure that sensitive data adheres to regulatory requirements like GDPR or HIPAA.

Observability and data quality monitoring enable proactive detection of anomalies, inconsistencies, and errors in the pipeline. Implementing automated data validation checks, lineage tracking, and anomaly detection ensures data accuracy and reliability. Data observability tools help monitor the health of data pipelines, helping to proactively identify and resolve issues in the data platform.

#### Data Orchestration

Data orchestration coordinates the movement and transformation of data across different pipeline stages, ensuring that processes execute efficiently and in the correct order. Orchestration tools manage dependencies, trigger workflows, and provide failure recovery mechanisms, reducing manual intervention. Solutions like Dagster help automate and optimize these workflows.

A well-designed orchestration strategy enables parallel processing, load balancing, and dynamic scaling to accommodate varying workloads. By integrating monitoring and alerting capabilities, orchestrators enhance pipeline reliability and performance. Efficient orchestration ensures seamless data flow, minimizing delays and operational disruptions.

***Related content: Read our guide to data pipeline framework (coming soon).***

### Common Data Pipeline Architecture Design Patterns with Examples

#### 1. Batch Processing

Batch processing is one of the most traditional data pipeline designs, where data is processed in large, discrete sets (or "batches") instead of in real-time. This pattern is suitable for scenarios where immediate data processing is not necessary, and data can be gathered and handled in bulk.

**How it works:** In batch processing, data is collected over a specified period—daily, hourly, or weekly—and then processed in one large operation. This includes tasks like data cleaning, transformation, and aggregation. The processed data is then stored in data warehouses or databases for analysis.

**Example:** Banks often use batch processing to handle transactions at the end of the day. During off-peak hours, these transactions are processed in bulk to update account balances and generate reports.

#### 2. Stream Processing

Stream processing focuses on real-time data processing, allowing for near-instantaneous insights and reactions to incoming data. This pattern is vital for use cases where even slight delays in data analysis could impact decision-making or operations.

**How it works:** Data is continuously ingested and processed as soon as it is generated. Small chunks of data, known as records or events, are processed incrementally. Stream processing requires handling out-of-order data and managing time-sensitive operations like windowing, where data is grouped by time intervals.

**Example:** In online fraud detection, stream processing analyzes each transaction in real-time to identify suspicious activity. This enables immediate actions, such as blocking fraudulent transactions before they are completed.

#### 3. Lambda Architecture

The Lambda architecture is a hybrid pattern combining both batch and stream processing techniques. It provides real-time analytics while also handling massive historical datasets for in-depth analysis.

**How it works:** This architecture consists of three layers:

- **The batch layer** processes historical data in bulk.
- **The** **serving layer** indexes and stores the batch-processed data for querying.
- **The** **speed layer** processes real-time data to provide quick insights that the batch layer has not yet handled.

**Example:** eCommerce platforms often use Lambda Architecture to generate real-time product recommendations. The batch layer handles comprehensive user behavior data, while the speed layer updates the model with recent actions like clicks or searches.

#### 4. Microservices-Based

This pattern breaks down the data pipeline into a series of independent microservices, each responsible for a specific task. This allows for more flexible and scalable data management.

**How it works:** Each microservice in the pipeline handles a distinct part of the data processing task. Services communicate through lightweight protocols like HTTP REST or messaging systems such as RabbitMQ. This modular structure allows individual components to be updated or scaled independently.

**Example:** A streaming service like Netflix may use a microservices-based pipeline for tasks such as user authentication, video delivery, and usage analytics. Each service operates independently, which enables rapid scaling and updates without affecting the entire system.

#### 5. Event-Driven Architecture

An event-driven architecture is designed around real-time events, where system components react to events as they occur. This is particularly useful for dynamic, real-time systems that need to respond instantly to changes.

**How it works:** Components are loosely coupled, communicating asynchronously through event producers and consumers. When an event occurs, it is processed by an event handler, which triggers actions such as updating data, sending notifications, or invoking a new process.

**Example:** In an online retail platform, when a customer places an order, an event is generated that triggers inventory updates, order confirmation, and shipment processes independently.

### Challenges in Data Pipeline Architecture

Here are some of the challenges organizations face when designing their data pipeline architecture.

#### Ensuring Data Quality

This involves validating and cleaning data to maintain its accuracy, consistency, and relevance. Ensuring data quality requires implementing processes that measure, monitor, and manage data quality across all stages of the pipeline. Poor data quality can lead to incorrect analytics, misguided decisions, and inefficiencies.

Data quality metrics, such as timeliness, completeness, and uniqueness, must be established and continuously monitored. Implementing automated data cleaning and transformation mechanisms helps in minimizing human-induced errors. Quality control frameworks enhance the reliability of insights drawn from the data.

#### Reducing Integration Complexity

Integrating diverse data sources and platforms is inherently complex. As data solutions involve more sophisticated ecosystems, harmonizing interfaces and protocols becomes a major challenge. Organizations need to manage different data formats, APIs, and database connections, which can introduce integration bottlenecks without a clear strategy.

Addressing integration complexity involves the adoption of middleware and standardized protocols. Harnessing platforms that offer native integrations or employ standardized connectors simplifies the process. Efficiently orchestrating data flow among various systems reduces overhead.

#### Addressing Scalability Constraints

Scalability involves effectively handling growth in data volume, variety, and velocity. As data sources expand and user requests increase, pipelines must adapt without degrading performance. Scalability challenges necessitate designing pipelines that can grow dynamically with organizational needs.

Using cloud-based solutions and distributed architectures aids in overcoming scalability issues. Technologies that support horizontal scaling allow resources to be added seamlessly as demand rises. Ensuring pipelines are scalable helps maintain performance levels and responsiveness.

#### Maintaining Data Security and Privacy

Securing data involves protecting it against unauthorized access and ensuring compliance with privacy regulations. As data traverses across different stages, maintaining its confidentiality, integrity, and availability is critical. Breaches can lead to significant financial and reputational damage, making security a top concern.

Implementing strong encryption practices and access controls across the pipeline is essential. Regular audits and compliance checks ensure adherence to legal standards, such as GDPR. Securing sensitive information helps build trust with customers and stakeholders.

### The Role of Data Platforms and Orchestrators in Data Pipeline Architecture

#### Data Platforms: Managing and Processing Data in the Organization

Data platforms serve as the backbone of data pipeline architecture by providing the infrastructure and tools needed to store, manage, and process data at scale. These platforms centralize data from multiple sources, offering a unified environment for data storage, retrieval, and analysis. A key role of a data platform is to handle diverse data types, including structured, semi-structured, and unstructured data, allowing organizations to extract valuable insights across various formats.

Data platforms typically integrate with data warehouses, data lakes, and cloud-based storage systems to ensure scalable and efficient data management. They also offer support for analytics, such as machine learning and artificial intelligence, enabling businesses to leverage large datasets for predictive modeling and decision-making. Popular data platforms, like Apache Spark and Snowflake, are built to process both batch and real-time data, ensuring flexibility in handling different pipeline patterns.

In addition, [modern data platforms](https://dagster.io/solutions/data-modernization) provide features for data governance, ensuring compliance with regulatory requirements and maintaining data quality. They often include tools for monitoring, auditing, and controlling access to sensitive data, making sure that data security and privacy standards are upheld throughout the pipeline.

#### Data Orchestrators: Executing and Managing Data Pipelines

Data orchestrators play a crucial role in automating, scheduling, and managing the flow of data through the pipeline. They coordinate various tasks, from data ingestion and transformation to storage and validation, ensuring that processes are executed in the correct sequence and at the right time. These tools act as conductors, managing dependencies between different stages of the pipeline and handling failures to maintain system reliability.

Data orchestration tools like Dagster allow data engineers to define complex workflows, which enable easy visualization and management of pipeline activities, and make it easier to track and debug processes. Orchestrators can also handle dynamic workloads, automatically scaling to meet the demands of the pipeline.

Data orchestrators integrate with various platforms and services, including cloud-based storage, databases, and analytics tools, to streamline the end-to-end data pipeline. They also support multi-step data workflows, ensuring that data transformations, validation checks, and loading operations are executed seamlessly. Through automation and efficient resource allocation, orchestrators help optimize pipeline performance and reduce the risk of bottlenecks or delays.

### Data Pipeline Architecture Best Practices

#### 1. Map and Understand the Dependencies of Your Data Pipeline

Mapping dependencies involves identifying all interconnected components within a data pipeline. Understanding how these elements interact is crucial for effective monitoring and troubleshooting. This practice helps maintain data integrity and smooth data flow, ensuring that any changes in one part do not adversely affect the entire pipeline.

Creating an updated map of dependencies allows teams to visualize relationships and assess the impact of modifications. Proper documentation helps handover processes and incident response, enhancing collaboration and reducing downtime. By mapping dependencies, organizations ensure their data pipelines are resilient and adaptable to changes.

#### 2. Design a Modular and Automated Pipeline

A modular and automated pipeline facilitates flexibility and efficiency. Modular design enables individual components to be developed, tested, and deployed independently, improving maintainability and scalability. Automation reduces manual intervention, lowering the risk of errors and expediting data handling processes.

Modular architectures support agile practices, allowing for easy updates and integration with emerging technologies. Automation through scripts and tools like data orchestration frameworks ensures consistent, reliable data operations. Adopting modular and automated designs reduces complexity and enhances overall pipeline performance.

#### 3. Create Data Pipeline SLAs

Service Level Agreements (SLAs) set expectations for data pipeline performance and reliability. They define metrics such as uptime, latency, and throughput, ensuring alignment with business goals. Establishing SLAs helps monitor performance, risk management, and drives accountability across teams.

SLAs provide benchmarks for evaluating the effectiveness of data pipelines and impact decision-making processes. Crafting clear performance metrics fosters transparency and helps prioritize efforts for pipeline optimization. SLAs are crucial in maintaining high service quality and meeting stakeholder expectations consistently.

#### 4. Create Data Products

Developing a data product involves transforming raw data into a useful, consumable asset for end-users. It requires defining user needs and designing data offerings that address those needs. Successful data products enhance decision-making and add business value, turning data into actionable insights.

The creation process includes identifying target audiences, designing product features, and ensuring usability. Employing feedback mechanisms allows iterative improvements tailored to user requirements. A well-designed data product promotes data democratization, empowering users across the organization with valuable insights.

#### 5. Make Pipelines Idempotent

Idempotency ensures that processing the same input multiple times results in the same output without side effects. This property improves data pipeline robustness, allowing safe reprocessing in case of failures or data reruns. Adopting idempotent operations prevents duplication and inconsistencies across data handling stages.

Implementing idempotency involves designing operations to be stateless or using unique transaction identifiers. Techniques such as upserts (update or insert) can enhance idempotency. Ensuring pipelines are idempotent helps maintain data integrity and supports reliable, fault-tolerant operations.

#### 6. Continuously Review and Optimize Costs

Continuous cost review helps balance performance and spending in data pipeline management. Analyzing usage, storage requirements, and resource allocation identify areas for potential savings. Optimizing costs involves fine-tuning processes to maximize efficiency without compromising data quality or access.

Leveraging cloud services with pay-as-you-go models allows for scalable cost management. Periodic audits and utilization assessments can reveal inefficiencies, enabling corrective actions. Maintaining a cost-effective pipeline ensures sustainable operations, aligning with budget constraints while fulfilling business intelligence needs.

Using Dagster to Orchestrate Your Data PipelinesDagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle.

It features a declarative programming model, representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for defining complex data architectures.

With a modular design and rich feature set, Dagster serves as the unified control plane for data processes across the organization, with native support for lineage and observability, an asset catalog, data validation checks, and best-in-class testability.

Thanks to intuitive, composable integrations with popular data tools, native support for executing non-Python workflows in remote environments, declarative automation, and intuitive partitioning, Dagster lets you seamlessly centralize and scale your data processes.

[**Learn more about the Dagster platform**](https://dagster.io/platform-overview).

‍

---

## Data Pipeline Frameworks: Key Features & 10 Tools to Know in 2025

> A data pipeline framework is a structured system that enables the movement and transformation of data within an organization.

Source: https://dagster.io/guides/data-pipeline-frameworks-key-features-10-tools-to-know-in-2025

### What Is a Data Pipeline Framework?

A data pipeline framework is a structured system that enables the movement and transformation of data within an organization. It makes it possible to automate, manage, and simplify the flow of data across various sources and systems.

These frameworks support extraction, transformation, and loading (ETL) processes, ensuring data is accessible and ready for analysis. Organizations can use them to handle large volumes of data systematically, minimizing errors and optimizing resource usage.

Data pipeline frameworks are crucial for [modern data management](https://dagster.io/solutions/data-modernization), enabling real-time data processing, batch processing, and hybrid models. They offer an architecture that simplifies data workflows, making it easier for data engineers to manage complex data platforms.

### Key Features of a Data Pipeline Framework

Data pipeline frameworks should include the following features.

#### Handling Large Datasets and Varying Data Loads

A data pipeline framework should be able to process large datasets with varying loads, ensuring consistent performance even as demand fluctuates. It can also handle different data sources and types, from structured to unstructured data. Effective frameworks use parallel processing and optimized data storage techniques, enabling scalability as data volumes grow.

Additionally, these frameworks employ data partitioning strategies, allowing them to efficiently manage and distribute data processing tasks across resources. By reducing data movement and optimizing storage access, the system enhances throughput and reduces latency. This capability is vital in environments with unpredictable workload patterns.

#### Fault Tolerance

Fault tolerance ensures that data processing continues smoothly even if components fail. This capability prevents data loss and mitigates the impact of failures on data accuracy and availability. Effective frameworks incorporate redundancy and error-handling mechanisms to automatically detect, isolate, and recover from failures.

They use techniques like checkpointing and data replication to safeguard against potential data losses. Fault-tolerant frameworks also use monitoring and alerting systems to identify issues swiftly, facilitating prompt resolution. These systems create a resilient architecture that adapts to hardware and software failures, maintaining data flow integrity.

#### Tracking Data Flow and Transformations

Tracking data flow and transformations in a data pipeline framework is crucial for maintaining data lineage and validating process accuracy. This involves the documentation of every data transformation, ensuring transparency and traceability throughout the data lifecycle.

Effective tracking systems capture metadata at each stage, allowing stakeholders to understand the origin, journey, and modifications of datasets. This transparency is useful for auditing, compliance, and debugging processes. Some frameworks provide data visualization tools that offer insights into data pathways, transforming raw data into easy-to-understand flows.

#### Monitoring and Logging

Monitoring and logging enable proactive detection of anomalies, ensuring smooth operations and facilitating timely interventions. Comprehensive logging captures detailed information about each process step, aiding in troubleshooting failures and improving system transparency and accountability.

Effective data pipeline frameworks incorporate monitoring tools that visualize system metrics, providing insights through intuitive dashboards. These tools can include alerts for unusual behaviors, enabling quick response to potential performance issues. They also support historical performance analysis, offering data for optimizing future processes.

***Related content: Read our guide to data pipeline architecture.***

### Popular Data Pipeline Frameworks

#### 1. Dagster

![Dagster](https://dagster.io/images/guides/681399f654933b29e12fbe74-c614eacf.png)

**License:** Apache-2.0**Repo:** <https://github.com/dagster-io/dagster/>**GitHub stars:** 11K+**Contributors:** 400+

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle. It features a declarative programming model for defining data pipelines, integrated lineage and observability, data validation checks, and best-in-class testability.

[**Learn more about the Dagster platform**](https://dagster.io/platform-overview)

![](https://dagster.io/images/guides/681399f654933b29e12fbe8b-9b7817a3.png)

#### 2. Apache Airflow

![Apache Airflow](https://dagster.io/images/guides/681399f654933b29e12fbe89-e50e352f.png)

**License:** Apache-2.0**Repo:** <https://github.com/apache/airflow>**GitHub stars:** 36k+**Contributors:** 3k+

Apache Airflow is an open-source workflow management platform for authoring, scheduling, and monitoring complex data workflows. Its directed acyclic graph (DAG) approach allows for flexible construction and management of task dependencies. Airflow’s scalable architecture makes it suitable for both small and enterprise-scale pipelines.

Airflow’s integration capabilities support third-party services and custom plugins. It is suitable for environments requiring dynamic pipeline structures, with flexible execution management features. Additionally, its large community ensures regular updates and improvements.

![](https://dagster.io/images/guides/681399f654933b29e12fbe78-941cc26a.png)

#### 3. Apache Beam

![Apache Beam](https://dagster.io/images/guides/681399f654933b29e12fbe8a-f4e5dafa.png)

**License:** Apache-2.0**Repo:** <https://github.com/apache/beam>**GitHub stars:** 7k+**Contributors:** 1k+

Apache Beam is a unified model for defining batch and streaming data processing pipelines. It allows users to deploy pipelines across execution engines such as Apache Flink, Spark, and Google Dataflow. Beam’s portability framework ensures that users can write once and run anywhere.

Apache Beam provides an abstraction for data processing, which supports windowing, sessionization, and complex event processing. It offers a rich SDK ecosystem, extending its capabilities to languages like Java and Python. It is extensible and interoperable, allowing users to scale without substantial code changes.

![](https://dagster.io/images/guides/681399f654933b29e12fbe7d-01eb48a5.png)

#### 4. Prefect

![Prefect](https://dagster.io/images/guides/681399f654933b29e12fbe76-8d978196.png)

**License:** Apache-2.0**Repo:** <https://github.com/PrefectHQ/prefect>**GitHub stars:** 15k+**Contributors:** 200+

Prefect is a data workflow automation tool for data flow monitoring and management. It emphasizes code-based process composition, offering a Pythonic approach to designing data workflows. Prefect handles dynamic workflows, providing features such as retries, caching, and failure handling.

Prefect’s real-time logging and centralized monitoring offer visibility into data workflow execution. The framework’s resilience in handling task failure and automated task retries improve overall data pipeline reliability.

![](https://dagster.io/images/guides/681399f654933b29e12fbe90-6beef032.png)

#### 5. Metaflow

![Metaflow](https://dagster.io/images/guides/681399f654933b29e12fbe88-d98601f9.png)

**License:** Apache-2.0**Repo:** <https://github.com/Netflix/metaflow>**GitHub stars:** 8k+**Contributors:** \\<100

Metaflow, developed by Netflix, provides a human-centric interface for managing and executing data workflows. It simplifies the transition from model prototypes to production systems, enhancing collaboration between data scientists and engineers. Metaflow supports local and cloud execution, offering versatility in deployment options.

Metaflow’s built-in data versioning and lineage tracking ensure that users maintain detailed records of data dependencies and transformations. Its integration with Python and R environments makes it suitable for data-centric applications, with built-in support for Python libraries and cloud-based storage.

![](https://dagster.io/images/guides/681399f654933b29e12fbe84-c730bebb.png)

#### 6. Luigi

![Metaflow](https://dagster.io/images/guides/681399f654933b29e12fbe81-e1a6ffb3.png)

**License:** Apache-2.0**Repo:** <https://github.com/spotify/luigi>**GitHub stars:** 17k+**Contributors:** 500+

Luigi is a Python-based data pipeline framework focusing on managing long-running batch processes. It is useful for building complex pipeline dependencies, making it suitable for recurring data tasks and batch processing. Luigi handles failures in the data pipeline, ensuring that tasks can resume from their last successful state.

It provides developer-friendly tooling for designing workflows efficiently. It can manage complex dependencies between disparate tasks. Luigi also supports a variety of data sources and targets. The framework’s checkpointing and resource management features ensure stable pipeline execution even in failure-prone environments.

![](https://dagster.io/images/guides/681399f654933b29e12fbe8c-d532e511.png)

#### 7. Kedro

![Kedro](https://dagster.io/images/guides/681399f654933b29e12fbe85-8fa35c54.png)

**License:** Apache-2.0**Repo:** <https://github.com/kedro-org/kedro>**GitHub stars:** 9k+**Contributors:** 200+

Kedro is an open-source Python framework for building reproducible and maintainable data pipelines. It focuses on modularity, allowing developers to create well-structured, reusable workflows while ensuring data quality and consistency. Kedro’s emphasis on code style and organization supports sustainable project development.

Kedro's integration with Jupyter notebooks enhances interactivity and experimentation during the development phase. It supports creating pipelines that are easy to version and audit. By enforcing structure and documentation, it promotes collaboration across data teams.

![](https://dagster.io/images/guides/681399f654933b29e12fbe83-2ab12c58.png)

#### 8. Mage

![Mage](https://dagster.io/images/guides/681399f654933b29e12fbe87-2a388fd3.png)

**License:** Apache-2.0**Repo:** <https://github.com/mage-ai/mage-ai>**GitHub stars:** 8K+**Contributors:** 100+

Mage is an open-source data pipeline tool that simplifies the creation and management of data workflows. It emphasizes ease of use with a no-code and low-code interface, allowing users with varying technical expertise to build and manage pipelines effectively. Mage supports both batch and streaming data processing.

Its modular design enables integration with various data sources and destinations, including databases, APIs, and cloud storage services. The framework offers pre-built templates and connectors that accelerate pipeline development. Key features include a real-time debugging tool that allows users to inspect data at each step of the pipeline.

![](https://dagster.io/images/guides/681399f654933b29e12fbe8f-79c60cec.png)

#### 9. Azure Data Factory

![Azure](https://dagster.io/images/guides/681399f654933b29e12fbe8d-9040491b.png)

**License:** Commercial

Azure Data Factory is a cloud-based data integration service for orchestrating and automating data movement and transformation. It offers a visual interface for designing workflows, simplifying complex data integration scenarios without extensive coding. Its scalable architecture supports data processing workloads of varying sizes with ease.

Key features include copy activities for massive data loads and data flow activities for complex data transformations directly in the cloud. Azure’s global network offers low-latency, fast data processing capabilities, ensuring high performance for data-intensive applications. It is mainly suited for enterprises using Microsoft’s cloud infrastructure.

![](https://dagster.io/images/guides/681399f654933b29e12fbe86-e670fa9f.png)

#### 10. Google Cloud Dataflow

![Google Cloud](https://dagster.io/images/guides/681399f654933b29e12fbe82-2ce7db93.png)

**License:** Commercial

Google Cloud Dataflow is a fully managed streaming analytics service that supports both batch and real-time data processing. It leverages Apache Beam as its core programming model, ensuring integration with existing Beam pipelines. Google Cloud Dataflow’s unified processing model and built-in data visualization tools make it well-suited for complex data workflows that require flexibility and scalability.

Besides its data handling capabilities, Google Cloud Dataflow provides a series of pre-built connectors to easily assimilate various data sources. With integrated monitoring and automated scaling, it adapts to changing workloads, providing consistent performance. It can handle complex event processing and session-based windows.

![](https://dagster.io/images/guides/681399f654933b29e12fbe8e-a05829ac.png)

### How to Choose a Data Pipeline Framework?

When selecting a framework for managing data pipelines, organizations should consider the following factors.

#### Organization-Specific Data Needs

Assessing the volume, variety, and velocity of data the organization manages is crucial for determining the necessary features and capabilities of the framework. Consider the types of data sources and destinations, such as SQL databases, cloud storage, or APIs, to ensure the framework supports integration with existing infrastructure.

Identifying the data processing requirements, such as real-time stream processing or batch processing, will help narrow down suitable options. Organizations must also consider data compliance and governance requirements, ensuring that the chosen framework facilitates data security and lineage tracking.

#### Compatibility with Existing Infrastructure

Evaluating how well a framework integrates with current data storage systems, cloud providers, and processing tools can prevent significant redevelopment costs and resource allocation. This compatibility ensures a seamless transition and maximizes the value of existing technologies.

Organizations should also consider the technical expertise required for integration and the ease of adoption by the current team. A framework with strong community support and extensive documentation can facilitate quicker onboarding and resolution of integration challenges.

#### Scalability and Performance

A framework that offers horizontal and vertical scaling will better accommodate increasing data loads and user demands. Key performance metrics to evaluate include data processing speed, throughput, and latency. These metrics help assess how the framework will perform under various conditions and workloads.

Additionally, assessing the framework’s ability to optimize resource usage through features like parallel processing and dynamic allocation can reduce operational costs and improve efficiency. Organizations should look for frameworks that offer benchmarking tools to simulate and measure performance in real-world scenarios.

#### Development and Maintenance Complexity

Frameworks that offer intuitive interfaces, comprehensive documentation, and extensive community support can reduce the learning curve and simplify development processes. This ease of use is essential for reducing time-to-market and minimizing the technical debt associated with maintaining complex data pipelines.

Organizations should assess the framework’s support for automated testing and deployment, which can streamline ongoing maintenance and updates. Additionally, frameworks that support modular design and reuse of components can significantly reduce maintenance overhead. Evaluating the total cost of ownership, including licensing fees, training costs, and potential staffing needs, will help organizations select a framework that aligns with budget constraints.

#### Flexibility and Extensibility

A flexible framework can adapt to new data sources and formats as organizational needs evolve, ensuring that it continues to provide value. Extensibility, through plugins or custom integrations, allows admins to expand the framework’s native capabilities and tailor solutions to organizational requirements.

Choosing a framework with a strong ecosystem of third-party tools and plugins can enhance functionality without significant additional development effort.

### Using Dagster to Orchestrate Your Data Pipelines

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle.

It features a declarative programming model, representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for defining complex data architectures.

With a modular design and rich feature set, Dagster serves as the unified control plane for data processes across the organization, with native support for lineage and observability, an asset catalog, data validation checks, and best-in-class testability.

Thanks to intuitive, composable integrations with popular data tools, native support for executing non-Python workflows in remote environments, declarative automation, and intuitive partitioning, Dagster lets you seamlessly centralize and scale your data processes.

[Learn more about the Dagster platform here](https://dagster.io/platform-overview).

‍

---

## Data Pipelines on AWS: 12 Services & How to Use Them Effectively

> AWS provides an ecosystem of services to support data pipelines at every stage—from data ingestion and transformation to storage, analysis, and monitoring.

Source: https://dagster.io/guides/data-pipelines-on-aws-12-services-how-to-use-them-effectively

### How Does AWS Support Data Pipelines? 

AWS provides an ecosystem of services to support [data pipelines](https://dagster.io/learn/data-pipeline) at every stage—from data ingestion and transformation to storage, analysis, and monitoring. These services cater to different data processing needs, including batch processing, streaming, workflow orchestration, and security.

For batch workloads, AWS services like AWS Batch and AWS Glue enable efficient ETL (extract, transform, load) operations, allowing organizations to process large datasets at scale. For real-time data processing, services such as AWS Glue Streaming, AWS Lambda, and Amazon Kinesis provide low-latency processing capabilities.

Data storage and warehousing solutions like Amazon S3 and Amazon Redshift ensure that processed data is stored efficiently and is readily accessible for analytics. Workflow orchestration tools like AWS Step Functions and Managed Workflows for Apache Airflow (MWAA) help coordinate complex data processing tasks, ensuring reliability and automation.

AWS also offers monitoring and security tools commonly used with data pipelines, including Amazon CloudWatch for performance tracking and AWS CloudTrail for auditing and compliance. 

### AWS Services for Building Data Pipelines 

#### Batch Data Processing

##### 1. AWS Batch

AWS Batch is a managed service that enables efficient execution of batch computing workloads without provisioning or managing servers. It dynamically provisions compute resources based on job requirements, optimizing cost and performance. Batch processing pipelines often handle large-scale data transformations, such as aggregating logs, running machine learning training jobs, or processing financial transactions in bulk. 

AWS Batch supports diverse workloads by allowing users to define job queues, specify dependencies, and leverage AWS Fargate or EC2 instances for execution. For example, a data pipeline could use AWS Batch to process large CSV files stored in Amazon S3, convert them to a structured format like Parquet, and load them into Amazon Redshift for analytics.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b7a-6a084912.png)

#### Data Integration

##### 2. AWS Glue

AWS Glue is a fully managed ETL service that automates data integration by discovering, cataloging, and transforming data across multiple sources. It provides a serverless environment for writing ETL scripts in Python or Scala and supports both batch and streaming workloads. Glue’s [Data Catalog](https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices) helps centralize metadata, making it easier to query and manage datasets across services like Amazon S3, Redshift, and Athena.

For example, a data pipeline might use Glue to extract customer transaction data from an RDS database, clean and transform it, and then load it into a Redshift data warehouse for business intelligence. Glue’s ability to generate schema recommendations and automate transformations reduces the need for manual intervention.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b77-ddfcf4b1.png)

##### 3. AWS AppFlow

AWS AppFlow simplifies data integration by enabling no-code/low-code data transfers between SaaS applications (such as Salesforce, ServiceNow, or Google Analytics) and AWS services. It supports scheduled or event-driven transfers, applying transformations like field mapping and data validation before delivering the data to its destination.

For example, an organization might use AppFlow to sync marketing campaign data from Salesforce into an S3-based data lake, where it is later analyzed using AWS Glue and Athena. By reducing the complexity of connecting third-party applications with AWS, AppFlow accelerates data ingestion while maintaining security and compliance.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b7d-8d4227e3.png)

#### Real-Time Data Processing

##### 4. AWS Glue Streaming

AWS Glue Streaming is an extension of AWS Glue, tailored for real-time data transformations. It connects to streaming sources like Amazon Kinesis Data Streams or Apache Kafka to ingest and process data as it arrives. 

For example, Glue Streaming can clean, deduplicate, and enrich streaming data in real time before delivering it to a data lake or downstream analytics tools. This is suitable for applications such as real-time analytics dashboards or fraud detection systems that require immediate insights.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b83-84e717fb.png)

##### 5. AWS Lambda

AWS Lambda is a serverless compute service that lets users run code in response to events without provisioning or managing servers. It supports real-time event-driven workflows by processing data as soon as it is ingested. 

For example, Lambda can parse JSON log files from Amazon S3, process IoT device telemetry data, or trigger data pipelines in response to new files being uploaded. Lambda's low-latency processing, coupled with automatic scaling, ensures that it can handle unpredictable data volumes.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b89-695c19f7.png)

#### Data Warehousing (AWS Redshift)

##### 6. Amazon Redshift

Amazon Redshift is a fully managed data warehouse that enables fast query performance on large datasets. It is optimized for analytical workloads, supporting columnar storage and massively parallel processing (MPP) to improve query efficiency. Redshift integrates with AWS services like Glue, S3, and Kinesis to enable seamless data ingestion. 

For example, a data pipeline might extract transactional data from an operational database, transform it using AWS Glue, and load it into Redshift for advanced analytics. Redshift Spectrum further extends its capabilities by allowing queries on structured and semi-structured data stored in S3 without moving the data.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b80-b8e9009c.png)

#### Workflow Orchestration

##### 7. AWS Step Functions

AWS Step Functions simplifies the orchestration of distributed workflows, ensuring reliable execution of each step in a pipeline. Using its state machine model, users can define tasks, specify their sequence, and include conditional branching, retries, and error-handling logic. 

For example, developers can build a pipeline that ingests data with AWS Glue, processes it with AWS Batch, and stores it in Amazon S3, all coordinated by Step Functions. Its integration with AWS services ensures that pipelines are tightly coupled with the underlying infrastructure, improving reliability and reducing the complexity of manual coordination.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b86-60234694.png)

##### 8. AWS MWAA

AWS Managed Workflows for Apache Airflow (MWAA) provides a managed service for Apache Airflow, a widely used open-source tool for orchestrating workflows. MWAA lets users define workflows as Directed Acyclic Graphs (DAGs), enabling developers to create and schedule complex pipelines that span multiple systems. 

For example, a developer could design a DAG to pull data from external APIs, transform it with Python scripts, and store the results in a database. MWAA integrates with services like Amazon Redshift, S3, and Lambda, allowing it to manage data movement across the AWS ecosystem while providing a familiar interface for developers who already use Airflow.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b8c-011b083f.png)

#### Data Storage and Transfer

##### 9. Amazon S3

Amazon S3 is a scalable object storage service that serves as the backbone for many data pipelines. It can store raw, processed, and archived data in multiple formats, including CSV, Parquet, and JSON. With features like S3 Select, users can query data directly within S3 without moving it to another system, saving time and resources. 

S3 also supports lifecycle policies and intelligent tiering, automatically optimizing storage costs by moving older data to lower-cost storage classes. Its integration with services like Glue, Redshift, and Athena makes it a key component of data pipelines for storage and processing.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b8f-b6b3d224.png)

#### 10. AWS DataSync

AWS DataSync can accelerate and simplify the transfer of data between on-premises systems and AWS or between different AWS storage services. It automates tasks such as file transfers, data synchronization, and migration, ensuring that the pipeline always has access to the latest data. 

DataSync includes built-in data validation, ensuring that transferred data is accurate and complete. For example, DataSync can be used to continuously sync on-premises database backups to Amazon S3, ensuring that the pipeline has up-to-date information for processing.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b92-1459071a.png)

#### Monitoring and Logging

##### 11. Amazon CloudWatch

Amazon CloudWatch provides real-time monitoring and logging for AWS resources and applications. It collects and tracks metrics, such as CPU utilization, memory usage, and data transfer rates, across the services used in the data pipeline. 

CloudWatch Logs enable detailed analysis of pipeline activities, such as debugging ETL jobs or identifying performance bottlenecks in AWS Glue or Lambda functions. Additionally, users can create alarms to notify the team of issues like job failures or unexpected data latencies, ensuring that the pipeline runs smoothly.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b96-4eaa0f15.png)

##### 12. AWS CloudTrail

AWS CloudTrail offers detailed tracking of API calls and user activity within the AWS environment. It provides logs that show who accessed what resource and when, making it an essential tool for auditing and compliance. For example, CloudTrail can help trace the origin of a pipeline failure by identifying the exact configuration changes or API calls that occurred before the issue.

![](https://dagster.io/images/guides/68dd4d1e3db260699f582b99-0254a4d1.png)

### 5 Best Practices for Building Data Pipelines on AWS 

Here are some useful practices to consider when creating a data pipeline in an AWS environment.

#### 1. Define a Clear Data Pipeline Architecture

Designing a well-structured data pipeline architecture is essential for ensuring scalability, efficiency, and maintainability. Begin by identifying the sources of the data and the required transformations before determining the best destination system for storage and analysis. Consider whether data should be stored in a data lake (Amazon S3) for flexibility or a data warehouse (Amazon Redshift) for analytical queries.

Clearly define the flow of data, specifying how each stage (ingestion, transformation, storage, and delivery) will be handled. Use tools like AWS Step Functions or MWAA to orchestrate workflows and manage dependencies between stages. Proper architecture ensures the pipeline is modular and can accommodate future changes, such as integrating new data sources.

#### 2. Implement Robust Security Measures

Protecting the data pipeline is critical to ensure confidentiality, integrity, and compliance. Leverage AWS Identity and Access Management (IAM) to enforce fine-grained permissions, ensuring that only authorized users and services can access pipeline components. For sensitive data, use encryption both in transit (e.g., with SSL/TLS) and at rest (e.g., using AWS Key Management Service with S3 or RDS).

Enable logging and monitoring using AWS services like CloudTrail and CloudWatch to detect and respond to suspicious activity. Additionally, implement network security by using Virtual Private Clouds (VPCs), security groups, and private endpoints to control access to resources. 

#### 3. Balance File Sizes to Improve Processing Efficiency

Efficient data processing in pipelines often depends on the size of the files being handled. Too many small files can lead to excessive overhead, while very large files can slow down processing. Optimize file sizes based on the tools and services being used. For example, when working with Amazon S3 and Amazon EMR, aim for file sizes between 128 MB and 1 GB to maximize parallel processing efficiency.

Organizations can use AWS Glue or custom scripts to combine smaller files into appropriately sized partitions before storage. Similarly, when writing output from a data pipeline, consider splitting large datasets into manageable chunks for easier downstream processing and analysis.

#### 4. Incorporate Data Validation and Error Handling Mechanisms

To ensure the quality and reliability of the pipeline, incorporate validation checks at every stage. Validate data during ingestion by verifying schema, data types, and completeness. Use tools like AWS Glue's DataBrew or Lambda functions to detect anomalies, such as missing values or outliers, and either log them for later inspection or correct them in real time.

Implement error-handling mechanisms to manage pipeline failures gracefully. For example, configure retries in AWS Step Functions or enable checkpointing in streaming tools like Kinesis Data Streams to prevent data loss. Detailed error logging via CloudWatch Logs can help quickly identify and resolve issues, minimizing downtime.

#### 5. Combine Multiple Processing Steps to Simplify Operations

Consolidating related processing tasks can reduce complexity and improve performance in the pipeline. For example, rather than executing separate jobs for data cleaning, aggregation, and enrichment, use AWS Glue or Spark jobs to handle these steps in a single operation. This minimizes intermediate data movement and reduces the overall runtime.

Use orchestration tools like Dagster and AWS Step Functions to group tasks logically, ensuring dependencies are maintained while avoiding unnecessary duplication of effort. By combining steps, teams can simplify pipeline management and optimize resource usage.

### Orchestrating Data Pipelines in the Cloud with Dagster

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle.

Thanks to its modular design and rich feature set, Dagster serves as the unified control plane for data processes across the organization, with built-in data lineage tracking and observability, an asset catalog, data validation checks, and best-in-class testability.

With native support for observing non-Python workflows and integrations with the most popular data tools and leading cloud providers like AWS, Dagster integrates seamlessly with your existing data stack. 

‍[**Learn more about the Dagster platform here**](https://dagster.io/platform-overview)**.**

---

## Data Pipelines with Python: 6 Frameworks & Quick Tutorial

> A data pipeline is a series of processes that move data from one system to another.

Source: https://dagster.io/guides/data-pipelines-with-python-6-frameworks-quick-tutorial

### What Is a Data Pipeline?

A data pipeline is a series of processes that collect, transform, and deliver data, typically starting from raw data sources and ending in a destination where it can be analyzed or stored. These pipelines are responsible for ensuring data is managed in a structured, reliable way, ensuring it is ready for downstream use.

The process typically involves multiple stages, such as data extraction, cleaning, transformation, and loading (ETL). At the extraction stage, raw data is gathered from various sources, including databases, APIs, or sensors. This data may be unstructured or incomplete, so the cleaning phase ensures that irrelevant or corrupted data is filtered out.

Once cleaned, the data is transformed into the desired format, which could include aggregating, summarizing, or enriching the data. The data is then loaded into a target system, such as a data warehouse, where it can be queried or analyzed.

### Why Build Data Pipelines in Python?

The Python language offers the following advantages for data pipelines:

- **Flexibility:** Python allows developers to design pipelines that can handle a broad range of data formats and sources. It supports a variety of operations, from simple data aggregation to machine learning tasks. The language's dynamic nature means that as data requirements evolve, pipelines can be easily adjusted without overhauling the system architecture. This adaptability extends to integrating new data tools or libraries.
- **Integration capabilities:** Python integrates systems and disparate data sources, enabling connectivity between databases, APIs, and file systems.
- **Libraries:** With libraries like SQLAlchemy for database interactions and frameworks such as Flask for API integration, creating interconnected data workflows is straightforward. Python’s language-agnostic interfaces mean that it can interact with other programming environments and systems, increasing its utility in multi-technology landscapes.
- **Data processing and machine learning:** Python boasts a rich ecosystem of tools for data processing, allowing pipelines to conduct tasks beyond basic ETL. Libraries such as Scikit-learn and TensorFlow facilitate the incorporation of machine learning models into data workflows, improving data transformation processes with predictive capabilities. There are specialized libraries for statistical analysis, data visualization, natural language processing, and more.

***Related content: Read our guide to data pipeline architecture.***

### Notable Python Data Pipeline Frameworks

#### 1. Dagster

![Dagster](https://dagster.io/images/guides/681399f654933b29e12fbe74-c614eacf.png)

Dagster is a Python-based open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle. It features a declarative programming model, integrated lineage and observability, data validation checks, and best-in-class testability.

- **License:** Apache-2.0
- **Repo:** <https://github.com/dagster-io/dagster/>
- **GitHub stars:** 11K+
- **Contributors:** 400+

**Key features of Dagster:**

- **Python and more:** Supports any Python workflow and lets you execute arbitrary code in other programming languages and on external environments using Dagster Pipes.
- **Data asset-centric:** Focuses on representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for building data pipelines.
- **Observability and monitoring:** The “single pane of glass” for your data team, with a robust logging system, built-in data catalog, asset lineage, and quality checks.
- **Cloud-native and cloud-ready:** Provides a managed offering with robust, managed infrastructure, elegant CI/CD capability, and support for custom infrastructure.
- **Integrations:** Extensive library of integrations with the most popular data tools, including the leading cloud providers (AWS, GCP, Azure), ETL tools and BI tools.
- **Declarative Automation:** Go beyond cron and intelligently orchestrate pipelines using event-driven conditions and the overall state of your pipeline and upstream data assets.

#### 2. Apache Beam

![Apache Beam](https://dagster.io/images/guides/681399f654933b29e12fbe7a-4318c377.png)

Apache Beam is an open-source, unified programming model that simplifies batch and streaming data processing, and provides a Python SDK. It allows developers to write pipelines once and execute them across various distributed processing backends, or "runners," such as Apache Flink, Google Cloud Dataflow, and Apache Spark.

- **License:** Apache-2.0
- **Repo:** <https://github.com/apache/beam>
- **GitHub stars:** 7K+
- **Contributors:** 1K+

‍

**Key features of Apache Beam:**

- **Unified model:** Provides a single programming model for both batch and streaming data, streamlining workflows for all data teams.
- **Python SDK:** Supports Python streaming pipelines, type safety, developing new I/O connectors in Python, and machine learning inferences with Python.
- **Extensible:** Supports extensions like TensorFlow Extended and Apache Hop, allowing for customization and the addition of advanced features.
- **Portable:** Enables pipeline execution across multiple environments, preventing vendor lock-in and offering flexibility in deployment.
- **Open source:** Developed by a vibrant community, ensuring continual updates and support tailored to diverse use cases.

![](https://dagster.io/images/guides/681399f654933b29e12fbe7d-01eb48a5.png)

#### 3. Apache Airflow

![Apache Airflow](https://dagster.io/images/guides/681399f654933b29e12fbe80-bd81dc24.png)

Apache Airflow is an open-source platform that allows users to programmatically author, schedule, and monitor workflows. It manages complex, scalable workflows through a modular architecture that can handle numerous workers.

- **License:** Apache-2.0
- **Repo:** <https://github.com/apache/airflow>
- **GitHub stars:** 36K+
- **Contributors:** 3K+

**Key features of Apache Airflow:**

- **Pure Python:** Lets users define workflows using standard Python syntax, making it easy to create and manage pipelines without relying on the command-line or XML.
- **Easy UI:** Provides a web interface to monitor, schedule, and manage workflows with full visibility into task statuses and logs.
- **Integrations:** Offers an array of plug-and-play operators that integrate with Google Cloud, AWS, Azure, and other third-party services.
- **Scalable:** Built on a modular architecture with message queues, allowing it to scale effortlessly to handle large and complex workflows.
- **Extensible:** Custom operators and extensions can be easily added, allowing pipelines to be adapted to specific environments and needs.

![](https://dagster.io/images/guides/681399f654933b29e12fbe78-941cc26a.png)

#### 4. Prefect

![Prefect](https://dagster.io/images/guides/681399f654933b29e12fbe76-8d978196.png)

Prefect is an open-source workflow orchestration platform to build, run, and monitor data pipelines. It allows users to write workflows as Python code, while abstracting away the complexities of managing execution. Prefect is known for its emphasis on handling failures and retries, ensuring that data flows are resilient and easy to monitor.

- **License:** Apache-2.0
- **Repo:** <https://github.com/PrefectHQ/prefect>
- **GitHub stars:** 16K+
- **Contributors:** 200+

**Key features of Prefect:**

- **Python-based:** Defines workflows using Python, enabling developers to build and manage pipelines with familiar syntax and tools.
- **Failure handling:** Provides automatic retries, error handling, and logging to ensure workflows are resilient and easy to debug.
- **Dynamic scheduling:** Offers flexible scheduling, allowing workflows to be executed on specific triggers, intervals, or ad-hoc.
- **Modular and scalable:** Designed to scale with workflows, handling large, complex pipelines by allowing modular task execution.
- **Cloud and on-premise support:** Integrates with cloud platforms and can be deployed on-premise, offering flexibility in execution environments.

![](https://dagster.io/images/guides/681399f654933b29e12fbe7e-fff715a5.png)

#### 5. Luigi

![Luigi](https://dagster.io/images/guides/681399f654933b29e12fbe73-81aae981.png)

Luigi is an open-source Python framework to create scalable and modular data pipelines. It helps manage workflows by breaking down complex data processes into smaller, reusable components. Luigi supports task scheduling and dependency management, ensuring that tasks are executed in the correct order and only when all dependencies are met.

- **License:** Apache-2.0
- **Repo:** <https://github.com/spotify/luigi>
- **GitHub stars:** 17K+
- **Contributors:** 500+

‍

**Key features of Luigi:**

- **Modular:** Allows decomposition of large applications into smaller, manageable modules that mirror backend structures.
- **Extensible:** Enables integration with external systems, allowing for secure and customizable extension of pipeline functionalities.
- **Scalable:** Supports distributed development, enabling multiple teams to collaborate and scale pipelines effectively.
- **Technology agnostic:** Avoids dependency on specific technologies, allowing for adaptability to future trends and preventing lock-in.

![](https://dagster.io/images/guides/681399f654933b29e12fbe79-fcc2174c.png)

#### 6. Dask

![Dask](https://dagster.io/images/guides/681399f654933b29e12fbe77-fd1cdee9.png)

Dask is an open-source parallel computing library that scales Python code across multiple cores or distributed systems. It allows users to parallelize their computations with familiar tools like pandas, NumPy, and Scikit-learn, enabling efficient handling of large datasets and complex computations.

- **License:** BSD-3-Clause
- **Repo:**[ https://github.com/dask/dask](https://github.com/dask/dask)
- **GitHub stars:** 12K+
- **Contributors:** 500+

‍

**Key features of Dask:**

- **Big pandas:** Dask DataFrames use pandas internally, enabling users to handle larger-than-memory datasets with minimal code changes.
- **Parallel for loops:** Helps parallelize operations like loops and data transformations, making complex workflows faster without requiring deep knowledge of distributed systems.
- **Big arrays:** NumPy arrays can be extended to operate on large datasets, enabling computations on large arrays that don't fit into memory.
- **Machine learning:** Integrates with machine learning libraries like Scikit-learn, allowing distributed training and model evaluation across large datasets.
- **Performance at scale:** Runs directly on inhouse hardware without virtualization or unnecessary overhead, delivering fast, efficient performance.

![](https://dagster.io/images/guides/681399f654933b29e12fbe7c-460ccaa5.png)

### Tutorial: Creating a Data Pipeline in Python

Creating a data pipeline in Python involves several key steps, including extracting data from a source, transforming it to meet your needs, and then loading it into a destination for further use. Below, we will go through a simple data pipeline process that covers these steps.

#### Step 1: Installing the Necessary Packages

To build the data pipeline, we need to install several Python libraries:

- **Pandas**: For data manipulation.
- **SQLAlchemy**: To connect and interact with databases.
- **Boto3**: For cloud storage integration (e.g., Amazon S3).

Install the libraries using the following command:

pip install pandas sqlalchemy boto3

#### Step 2: Extracting Data

In this step, data is extracted from sources such as databases, APIs, or files (CSV, JSON, etc.). Here, we will use SQLAlchemy to extract data from a database and load it into a Pandas DataFrame.

```python
import pandas as pd
from sqlalchemy import create\_engine

\# Database connection setup
db\_engine \= create\_engine('postgresql://user:password@localhost:5432/example\_db')

\# SQL query to extract data
query \= "SELECT \* FROM sales\_data"

\# Load data into a Pandas DataFrame
df \= pd.read\_sql(query, db\_engine)

\# Print result on the console
print(f"Output:\\n{df}")
```

![](https://dagster.io/images/guides/681399f654933b29e12fbe7f-f5d8b39a.jpg)

In this code, create\_engine is used to establish a connection to a PostgreSQL database, and pd.read\_sql() extracts data based on the SQL query. The extracted data is loaded into a DataFrame (df) for further manipulation.

#### Step 3: Transforming Data

Once the data is extracted, it often requires cleaning or transformation before it can be analyzed. For example, we might need to handle missing values, modify data types, or create new derived columns.

```python
\# Fill missing values and change data type
df\['sales\_amount'\] \= df\['sales\_amount'\].fillna(0).astype(float)

\# Add a new column categorizing sales into high, medium, and low
df\['sales\_category'\] \= pd.cut(df\['sales\_amount'\], bins=\[0, 100, 500, float('inf')\], labels=\['Low', 'Medium', 'High'\])

\# Print result on the console
print(f"After Transformation:\\n{df}")
```

![](https://dagster.io/images/guides/681399f654933b29e12fbe75-734daf55.jpg)

In this step, missing values in the sales\_amount column are filled with 0, and the data type is converted to float. Additionally, a new column sales\_category is created to categorize sales into 'Low', 'Medium', and 'High' based on predefined ranges.

#### Step 4: Loading Data

After transforming the data, the next step is to load it into a destination, such as a database or cloud storage. For this, we use SQLAlchemy to write the data back into a database.

```python
\# Load the transformed data into a new table in the PostgreSQL database
df.to\_sql('transformed\_sales\_data', db\_engine, if\_exists='replace', index=False)
```

\
`In this example, the df.to\_sql() function loads the transformed data into a new table named transformed\_sales\_data in the same PostgreSQL database. The if\_exists='replace' argument ensures that the table is replaced if it already exists.`

#### Step 5: Analyzing Data

With the data loaded, you can now analyze it using Pandas for aggregation or visualization. For instance, calculating the mean sales by category can provide insights into performance.

```javascript
\# Analyze data by calculating mean sales per category
sales\_summary \= df.groupby('sales\_category').agg({'sales\_amount': 'mean'})
print(sales\_summary)
```

‍

![](https://dagster.io/images/guides/681399f654933b29e12fbe7b-ef0388c0.jpg)

This code calculates the mean sales for each sales category, providing a summary that can be used for further decision-making.

### Orchestrating Complex Data Pipelines with Dagster

Unlike the simple example above, realistic data pipelines may involve several components across multiple steps. Even a small company’s data platform may include hundreds of such pipelines materializing thousands of data assets.

Data orchestrators like Dagster help you manage the complex chain of dependencies between those assets, enabling your team to manage a large number of data pipelines and ensure that they run in the right order and on time.

When those pipelines inevitably break, it also helps your team manage failures with automatic retries and robust logging and data lineage tracking to help you identify and debug issues.

Dagster is designed for scale, with the ability to handle large graphs containing thousands of assets and an execution model that supports both parallel and distributed computing. It also lets you orchestrate data pipelines written in other languages and executed on remote environments, while retaining Dagster’s strengths in observability and orchestration.

To learn more about how Dagster can help you manage your data pipelines, see this [overview of the philosophy behind Dagster and the features it enables](https://dagster.io/blog/what-is-dagster).

‍

---

## Data Quality Checks: How to Test 6 Data Quality Dimensions

> Data quality testing involves evaluating data to ensure it meets specific standards for accuracy, completeness, consistency, and more.

Source: https://dagster.io/guides/data-quality-checks-how-to-test-6-data-quality-dimensions

### What Is Data Quality Testing?

Data quality testing involves evaluating data to ensure it meets specific standards for accuracy, completeness, consistency, and more. These tests identify discrepancies and errors in data that can lead to flawed analysis or decision-making. Testing is a continuous process carried out at various stages of data handling, from collection to [processing](https://dagster.io/platform-overview/data-orchestration) and storage, providing a view of data health.

Organizations use data quality testing to maintain data integrity, reliability, and trustworthiness. This process helps ensure that data sources produce accurate results when used in analytics, reporting, or operational tasks. By implementing data quality testing, companies can minimize risks associated with inaccurate data and increase confidence in data-driven decisions.

### Key Dimensions of Data Quality and Relevant Data Checks

#### 1. Accuracy

Accuracy in data quality ensures that information correctly represents the real-world entities it models. It's essential for preventing errors in data-driven decisions and operations. Accurate data must be verified against trusted sources and refined regularly to maintain its precision. Errors and discrepancies can distort outcomes and lead to mistrust in data assets.

Relevant checks include:

- **Source verification:** Compare data against trusted reference sources to confirm correctness.
- **Threshold analysis:** Validate that values fall within expected thresholds or ranges.
- **Anomaly detection:** Identify outliers using statistical methods to flag potential inaccuracies.
- **Cross-field validation:** Ensure relationships between fields are logical (e.g., birth date \\< current date).
- **Historical comparison:** Assess data against historical records to detect deviations.

#### 2. Completeness

Completeness of data refers to the extent to which all required data is available. Missing data can lead to incorrect interpretations and faulty decision-making processes. To ensure completeness, businesses must identify essential data points and verify that all fields are filled appropriately.

Relevant checks include:

- **Null value checks:** Identify and report missing values in required fields.
- **Mandatory field validation:** Confirm all critical fields are populated.
- **Record-level completeness:** Check if datasets include all required records.
- **Cross-system completeness:** Ensure data transfers between systems do not omit critical elements.
- **Range and enumeration verification:** Validate that fields that allow a limited number of known values are populated by appropriate values.

#### 3. Consistency

Consistency in data quality means maintaining uniformity across datasets, regardless of the source or format. Inconsistent data can result from varied data formats, differing scales, or versioning errors. Upholding consistency requires coherent data handling policies and routine checks for anomalies.

Relevant checks include:

- **Format standardization:** Ensure consistent data formatting across datasets (e.g., date formats, numeric precision).
- **Field-level consistency:** Compare repeated values across datasets for uniformity (e.g., customer ID).
- **Version control validation:** Verify that the most recent data versions are used across systems.
- **Cross-system checks:** Ensure consistency between integrated datasets or systems.
- **Schema consistency:** Confirm that database schemas adhere to a unified standard.

#### 4. Timeliness

Timeliness refers to data being available when needed for decision-making. Delayed or outdated information reduces the effectiveness of insights. Ensuring timeliness requires efficient data collection processes and immediate data updates.

Relevant checks include:

- **Timestamp validation:** Confirm that timestamps on data entries align with processing requirements.
- **Latency monitoring:** Measure and monitor delays in data capture or transfer processes.
- **Scheduling adherence:** Validate that data updates or feeds occur as per the defined schedule.
- **Alert mechanisms:** Implement alerts for delayed data updates or stale records.

#### 5. Uniqueness

Uniqueness in data ensures that each entry is distinct, without duplications. Duplicate data can clutter datasets, skew analysis, and increase storage costs. It is particularly vital in identifying and managing resource allocations, customer records, and supply chains.

Relevant checks include:

- **Duplicate detection:** Use algorithms to identify duplicate entries in datasets.
- **Primary key validation:** Ensure primary key constraints are enforced in databases.
- **Merge check:** Confirm that merged datasets do not introduce duplicate records.
- **Entity resolution:** Perform entity matching to ensure distinct representation of individuals or objects.
- **Storage cleanup:** Periodically remove redundant or duplicate data.

#### 6. Validity

Validity checks that data values match defined formats and fall within the expected ranges. Invalid data can emerge from input errors or faulty data integrations. Thus, validity is crucial in ensuring data accuracy and usability.

Relevant checks include:

- **Format validation:** Check if data values comply with predefined formats (e.g., email, phone number).
- **Range validation:** Ensure numeric or date values fall within accepted ranges.
- **Domain constraints:** Verify values belong to a defined set of valid categories or codes.
- **Dependency validation:** Check that data values conform to interdependent rules (e.g., zip code matching city).
- **Regular expression matching:** Use regex patterns to validate textual fields (e.g., postal codes, identification numbers).

### Tools and Techniques for Data Quality Checks

#### Automated Tools

Automated tools streamline data quality checks by leveraging predefined rules and algorithms to identify and correct issues at scale. These tools often integrate with [ETL pipelines](https://dagster.io/solutions/etl-elt-pipelines), databases, and [analytics platforms](https://dagster.io/integrations), providing validation during data ingestion and processing.

Key features include duplicate detection, schema validation, and outlier identification. These tools offer user-friendly interfaces for creating workflows that enforce consistency, accuracy, and completeness. Platforms may also incorporate [machine learning](https://dagster.io/solutions/ai) to detect anomalies and suggest corrections based on historical patterns.

#### Manual Techniques

Manual techniques remain relevant in scenarios where domain expertise is essential for identifying subtle errors or interpreting data nuances that automated systems might overlook. Analysts may use tools like spreadsheets or SQL queries to perform ad hoc checks, verify calculations, or validate complex relationships between data points.

These methods are particularly useful for small-scale data audits, testing new data pipelines, or addressing one-off issues. While manual techniques are time-consuming, they provide a layer of quality assurance that automated tools alone cannot achieve.

Best practices for manual checks include creating detailed guidelines for reviewers, using peer reviews to minimize oversight, and documenting findings to inform future improvements in automated systems.

#### Scripting and Programming

Custom scripts and programs allow for tailored data quality checks that address unique business requirements or complex datasets. Common programming languages such as [Python](https://dagster.io/blog/data-engineering-in-python), R, or SQL are used to implement advanced validation logic, automate repetitive checks, and process large datasets efficiently.

For example, Python libraries like Pandas and NumPy enable analysts to write scripts for checking data consistency, handling missing values, and detecting anomalies. SQL queries are often used to validate database integrity, enforce referential constraints, and perform cross-table comparisons.

Scripting offers flexibility and scalability, enabling organizations to adapt their data quality processes to evolving needs. However, it requires skilled personnel to develop the scripts, and might create an ongoing maintenance effort.

### 5 Best Practices for Effective Data Quality Checks

#### 1. Automate Repetitive Checks Wherever Possible

Manual checks, while valuable in some scenarios, are prone to human error and become increasingly inefficient as data volumes grow. Automation helps streamline repetitive tasks, such as checking for missing fields, identifying duplicates, and validating data against predefined business rules. By leveraging automation tools and frameworks, organizations can ensure that checks are performed consistently and at scale.

Technologies like rule-based engines, machine learning models, and data validation scripts are particularly useful for monitoring data quality continuously and flagging anomalies in real time. This reduces the burden on data teams and frees them to focus on resolving issues rather than identifying them. Additionally, automated checks can be scheduled to run at specific intervals or triggered dynamically during critical data operations.

#### 2. Integrate Checks into ETL Pipelines

ETL pipelines are the backbone of data processing workflows, moving data from source systems to storage and analytics platforms. By embedding data quality checks directly into ETL pipelines, organizations can catch errors at the earliest stages of the data lifecycle. For example, schema validation can ensure that incoming data adheres to the expected format and structure, while transformation logic can include checks to prevent invalid or incomplete records from proceeding to the next step.

Integration within ETL also allows organizations to enforce consistency across datasets by applying normalization rules and reconciling discrepancies before loading data into target systems. Many ETL tools come equipped with data quality modules that facilitate this process.

#### 3. Perform Checks Regularly (Batch vs. Real-Time)

Regular checks are essential to ensure that datasets remain reliable and relevant over time. [Batch checks](https://dagster.io/glossary/batch-processing) are ideal for processing large volumes of data in scheduled intervals, such as daily, weekly, or monthly cycles. These are particularly useful for historical data, reporting, and archival systems, where real-time updates may not be critical. Real-time data quality checks are indispensable for dynamic environments, such as e-commerce platforms, IoT ecosystems, or financial trading systems.

Real-time checks validate data as it is generated or ingested, ensuring that immediate actions are based on accurate and up-to-date information. A hybrid approach, which combines batch processing for static datasets with real-time monitoring for live systems, offers the best of both worlds.

#### 4. Maintain an Audit Trail for Identified and Resolved Issues

An audit trail serves as a critical record-keeping mechanism for all data quality activities. It provides a transparent history of issues, their root causes, and the steps taken to resolve them. Such documentation is invaluable for compliance with regulatory standards like GDPR or HIPAA, which often require organizations to demonstrate accountability in their data management practices. Beyond compliance, audit trails enable organizations to perform root cause analysis, identifying recurring patterns and systemic issues that need to be addressed at their source.

A well-maintained audit trail includes detailed logs that capture timestamps, error descriptions, responsible parties, and resolution outcomes. These records can be stored in centralized systems accessible to all stakeholders, fostering collaboration and continuous improvement.

#### 5. Collaborate Across Teams to Define Expectations and Priorities

Effective data quality management requires input and cooperation from multiple stakeholders, including [data engineers](https://dagster.io/learn), analysts, business leaders, and compliance officers. Each group has unique perspectives and priorities, which must be aligned to ensure a unified approach. For instance, while business teams may focus on the accuracy and timeliness of data for decision-making, compliance teams may emphasize adherence to regulations.

To align these goals, organizations should hold cross-functional workshops and meetings to define quality standards, acceptable error thresholds, and processes for resolving disputes. Collaboration tools, such as shared dashboards and [data catalogs](https://dagster.io/platform-overview/data-catalog), can help teams maintain visibility into data quality metrics and issues. Clear communication channels and governance frameworks ensure that everyone understands their roles and responsibilities.

### Automating Data Quality with Dagster

[Data quality](https://dagster.io/platform-overview/data-quality) can be significantly improved by tightly coupling data quality checks with the orchestration of the processes that generate the data.

Dagster’s [Asset Checks](https://dagster.io/blog/dagster-asset-checks) feature also allows you to automate data quality checks and validate data assets as soon as they are materialized, proactively alerting you to any data quality issues and preventing malformed or erroneous data from flowing to downstream systems.

The [Dagster+ Catalog](https://dagster.io/blog/dagster-plus-calatog-a-new-built-in-asset-library), meanwhile, enables master data management and easier discovery of trusted data assets across your organization. Unlike traditional data catalogs, Dagster is also aware of the context and processes surrounding the materialization of these assets, offering your team built-in monitoring and observability capabilities that allow you to visualize the health of your data pipelines and perform root-cause analyses from within the same platform.

To learn more about how Dagster can serve as the single pane of glass for the health of your data pipelines, see this [deep dive into Asset Checks](https://dagster.io/blog/dagster-asset-checks).

‍

### Frequently Asked Questions

#### What is data quality testing?

Data quality testing involves evaluating data to ensure it meets specific standards for accuracy, completeness, consistency, and more. These tests identify discrepancies and errors in data that can lead to flawed analysis or decision-making.

#### What are the six dimensions of data quality covered in this guide?

This guide covers accuracy, completeness, consistency, timeliness, uniqueness, and validity, along with example checks you can apply for each dimension.

#### How do automated tools support data quality checks?

Automated tools use predefined rules and algorithms to identify and correct issues at scale, often integrating with ETL pipelines, databases, and analytics platforms during ingestion and processing.

#### Why integrate data quality checks into ETL pipelines?

Embedding checks into ETL pipelines catches errors early in the data lifecycle—for example through schema validation and transformation logic—before bad records reach downstream systems.

#### How does Dagster relate data quality to orchestration?

Dagster couples data quality with orchestration through features such as [Asset Checks](https://dagster.io/blog/dagster-asset-checks) (validation when assets materialize) and the [Dagster+ Catalog](https://dagster.io/blog/dagster-plus-calatog-a-new-built-in-asset-library) for discovery and observability tied to how assets are produced.

---

## Data Quality Platform: Benefits, Key Features, and 10 Tools to Know

> Data quality platforms work by automating the processes involved in identifying and correcting data errors. This automation reduces manual effort and minimizes the risk of human error.

Source: https://dagster.io/guides/data-quality-platform-benefits-key-features-and-10-tools-to-know

### What Is a Data Quality Platform? 

A data quality platform is a system that ensures organizational data is accurate, consistent, and accessible. These platforms integrate tools and techniques to monitor, manage, and improve data quality across the enterprise. They typically offer features like data profiling, cleaning, matching, and enrichment. 

By using a data quality platform, organizations can maintain data integrity, which is crucial for accurate analytics and informed decision-making. Data quality platforms work by automating the processes involved in identifying and correcting data errors. This automation reduces manual effort and minimizes the risk of human error. 

This is part of a series of articles about data platforms.

### What Data Quality Metrics Should You Track? 

#### Completeness

Data completeness is the extent to which all necessary data is present. Incomplete data can lead to incorrect analysis and faulty decisions. Tracking completeness involves identifying missing data fields or records and assessing their impact on overall data quality. Maintaining completeness can involve data audits or automated checks that flag incomplete entries. 

#### Accuracy

Data accuracy measures the correctness of data. Errors in data entry, outdated information, or incorrect data sources can significantly impede accuracy. Accurate data reflects what it is intended to represent without errors or misstatements. To ensure accuracy, data validation processes include regular audits and cross-verification against reliable sources. 

#### Validity

Validity ensures data conforms to set rules and formats. It means data must fall within the established parameters, such as correct formats for dates or numbers and adherence to standard codes or classifications. Ensuring data validity requires implementation of constraints and predefined rules. Valid data is essential for maintaining consistency and uniformity in datasets.

#### Timeliness

Timeliness refers to the availability of data when needed. For data to be useful, it must be up-to-date and accessible promptly. This metric measures how fast data becomes available after an event or transaction occurs. In industries like finance, real-time data delivery is crucial for decision-making. Systems that support timely data delivery often include automated updates and alerts.

#### Consistency

Consistency in data quality refers to the uniformity of data across various sources and datasets. Data should not conflict with other datasets and should maintain harmony across different systems. To achieve consistency, data synchronization and reconciliation practices are necessary.

#### Uniqueness

Uniqueness ensures that each data entry is distinct and not duplicated across datasets. Duplicate entries can corrupt analysis, inflate metrics, and lead to inefficiencies in data handling. This metric helps in maintaining the originality and reliability of data records. Organizations use deduplication processes to identify and eliminate duplicate records, ensuring clean and representative data. 

### Benefits of Using Data Quality Tools 

Data quality platforms helps organizations achieve the following benefits:

- **Improving data accuracy:** Data quality tools help eliminate errors by identifying inconsistencies and inaccuracies and providing methods to rectify them. By leveraging automated processes, organizations minimize manual data entry errors, thus enhancing overall accuracy. Consistent data entry and validation protocols further improve reliability, ensuring that data used in reports and analytics is dependable.
- **Enhanced decision-making:** High-quality data enables organizations to make informed decisions with greater precision and confidence. Tools that validate and clean data ensure that insights drawn from data reflect actual business conditions. By providing accurate and comprehensive data, these tools support strategic planning and operational decisions. 
- **Data governance and compliance:** Effective governance requires ensuring data is compliant with policies and regulations. These tools enable the enforcement of data standards and practices required for regulatory compliance. Data quality also helps adhere to legal and industry standards by verifying data accuracy, completeness, and security, helping organizations manage compliance risks through automated checks and documentation.

### Key Features of Data Quality Tools 

Here are some of the key features offered by a modern data quality tool:

- **Data profiling and discovery**: Most platforms offer tools to explore data sources, revealing structural insights, patterns, and anomalies. Profiling helps organizations understand their data and prepare it for quality checks by identifying trends and potential issues in real-time.
- **Automated data cleaning**: Automated rules for data correction, standardization, and formatting streamline the cleaning process, reducing manual intervention. This feature helps resolve inconsistencies, such as typographical errors or mismatched formats, to improve overall data reliability.
- **Data matching and deduplication**: Many tools include deduplication to detect and merge duplicate entries, and data matching to correlate records from disparate sources. This ensures that each entity (like a customer or product) is uniquely represented, enhancing the accuracy of analyses.
- **Real-time monitoring and alerts**: These platforms often support real-time data quality monitoring, alerting users to potential issues as they occur. Real-time alerts help prevent poor data from impacting operational processes, maintaining data reliability across platforms.
- **Customizable validation rules**: Custom validation rules allow organizations to apply specific criteria based on business requirements. This flexibility enables teams to enforce standards across different datasets, ensuring data quality aligns with unique operational needs.
- **Data governance and compliance support**: Many tools offer compliance and governance features, including support for industry standards and regulatory requirements. This ensures data adheres to legal and internal standards, helping organizations avoid compliance risks.
- **Integration with data pipelines**: Data quality platforms often integrate with ETL (Extract, Transform, Load) tools and other pipeline solutions, enabling data checks at each stage of data transformation. This ensures that quality issues are identified and resolved early in the data lifecycle.
- **Reporting and metrics tracking**: Built-in dashboards and reports enable organizations to track data quality metrics over time. Metrics tracking helps identify trends, measure improvements, and make data-driven adjustments to quality management strategies.

### Notable Data Quality Platforms and Tools 

#### 1. Dagster

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fe4-15d06e65.png)

‍

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle, with a declarative programming model, integrated lineage and observability, data validation checks, and best-in-class testability. 

**License:** Apache-2.0

**Repo:** <https://github.com/dagster-io/dagster/>

**GitHub stars:** 11K+

**Contributors:** 400+

**Key features:**

- **Data asset-centric:** Focuses on representing data pipelines in terms of the data assets (e.g., tables, machine learning models, etc.) that they generate, yielding an intuitive, declarative mechanism for defining data pipelines.
- **Data quality checks:** Natively supports running data quality checks at the time of materialization. Combining orchestration and data quality checks in the same platform ensures malformed or erroneous data is not transmitted to downstream systems.
- **Observability and monitoring:** Features a built-in data catalog, asset lineage, and a robust logging system.
- **Cloud-native and cloud-ready:** Provides a managed offering with robust, managed infrastructure, elegant CI/CD capability, and multiple deployment options for custom infrastructure.
- **Integrations:** Extensive library of integrations with the most popular data tools, including the leading cloud providers (AWS, GCP, Azure).
- **Flexible:** As a Python-based data orchestrator, Dagster affords you the full power of Python. It also lets you execute any arbitrary code in other programming languages and on external (remote) environments, while retaining Dagster’s best-in-class observability, lineage, cataloging, and debugging capabilities.
- **Declarative Automation:** This feature lets you go beyond cron-based scheduling and intelligently orchestrate your pipelines using event-driven conditions that consider the overall state of your pipeline and upstream data assets. This reduces redundant computations and ensures your data is always up-to-date based on business requirements and SLAs, instead of arbitrary time triggers.

‍

![](https://dagster.io/images/guides/689a19c2fd7eafa7831f8039-7244dcbc.png)

‍

#### 2. Monte Carlo

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7ff4-dbe7969b.png)

Monte Carlo is a data quality platform that automates monitoring and testing across production tables to ensure data reliability. By leveraging machine learning, Monte Carlo provides coverage recommendations and automatically checks for data timeliness, completeness, and validity. It supports deep monitoring on critical assets and allows the creation of custom monitors. 

**License:** Commercial

**Features:**

- **Automated out-of-the-box testing:** Automated data quality tests with machine learning-powered monitoring across all production tables.
- **Comprehensive coverage:** Monitors for timeliness, completeness, and validity of data without the need for manual configuration.
- **Customizable monitors:** Allows for custom SQL rules and monitors-as-code to meet specific data quality needs.
- **Incident detection and resolution:** Provides alerts and rich insights to detect, triage, and resolve data incidents efficiently.
- **Integrated notification channels:** Notifies data owners immediately when anomalies or data incidents are detected, ensuring rapid resolution.

‍

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f8007-5f74932a.png)

#### 3. IBM InfoSphere Information Server

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fde-b48bec78.png)

IBM InfoSphere Information Server is a data integration platform to help organizations manage, transform, and ensure the quality of their data. It offers scalable, massively parallel processing (MPP) capabilities, helping to clean, monitor, and integrate data from various systems.  

**License:** Commercial

**Features:**

- **Scalable data integration:** Offers scalable ETL (extract, transform, load) capabilities, allowing integration across multiple systems both on-premises and in the cloud.
- **Data governance:** Provides tools for discovering IT assets and defining a unified business language, improving understanding and governance of data.
- **Data quality monitoring:** Continuously assesses and monitors data quality, with integrated rules analysis to ensure clean, reliable data.
- **Business alignment and productivity:** Helps organizations align their data strategies with business objectives, enhancing productivity by integrating with related IBM products.
- **Cloud-ready deployment:** Supports hybrid, public, and private cloud environments through hyperconverged systems like IBM Cloud Pak for Data.

‍

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f8004-012c5b2b.png)

#### 4. Deequ

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fed-b52ce26a.png)

Deequ is a data quality library built on top of Apache Spark that enables users to create "unit tests" for large datasets, ensuring data quality before it is used in applications or machine learning models. It allows users to define constraints on data attributes and validate them against predefined rules, helping to detect and fix data errors early. 

**License**: Apache-2.0 

**Repo:** <https://github.com/awslabs/deequ>

**GitHub stars:** 3K+

**Contributors:** 60+

**Features:**

- **Data unit testing:** Provides tools for defining and running tests on data, ensuring data meets quality standards before being used in production.
- **Scalability with Spark:** Built to handle large datasets in distributed environments, making it suitable for data warehouses and big data platforms.
- **Customizable data quality checks:** Supports various checks such as ensuring non-null values, uniqueness, and compliance with predefined ranges or patterns.
- **Anomaly detection:** Automatically detects anomalies in data quality metrics over time, providing insights into data health trends.
- **Metrics repository:** Offers persistence and querying of computed data quality metrics, allowing users to track quality improvements and issues over time.

#### 5. dbt Core

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fd8-d1479c5a.png)

‍

dbt Core is a SQL-first data transformation tool that enables data teams to collaboratively build, test, and deploy data pipelines with a focus on data quality and governance. By applying software engineering best practices like modularity, version control, and CI/CD, dbt ensures that data transformations are reliable, documented, and production-ready. 

**License**: Apache-2.0 

**Repo:** <https://github.com/dbt-labs/dbt-core>

**GitHub stars:** 9K+

**Contributors:** 300+

**Features:**

- **Automated data testing:** Built-in testing features allow teams to validate data models, ensuring data accuracy and consistency.
- **Version control and CI/CD:** Supports Git-enabled version control and CI/CD processes, enabling safe deployment across dev, stage, and production environments.
- **Modular data transformations:** Allows data transformations in SQL or Python, managing dependencies automatically to reduce errors and improve development speed.
- **Data documentation:** Automatically generates and shares documentation with stakeholders, providing transparency into data pipelines and building trust in the data.
- **Governance and observability:** Ensures data governance through version control, logging, and alerting, helping teams maintain compliance and monitor data quality in real time.

‍

![](https://dagster.io/images/guides/689a19c2fd7eafa7831f8054-5b8431e3.png)

#### 6. Ataccama ONE

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fdb-c540ff6c.png)

‍

Ataccama ONE is an AI-powered data management platform that combines data quality, governance, and observability into a single, cloud-native solution. It helps organizations control, monitor, and improve the quality of their data while ensuring it is secure and accessible.

**License:** Commercial

**Features:**

- **Data quality management:** Provides tools to understand, monitor, and improve data quality, while preventing data issues through automated mechanisms.
- **Data cataloging:** Offers automated data discovery, a business glossary, and a data marketplace, ensuring users can find and leverage high-quality data efficiently.
- **Data observability:** Monitors critical data sources and sends timely alerts to ensure continuous data reliability and accuracy.
- **Master data management:** Enables organizations to create and maintain a single, trusted version of master and reference data, ensuring consistent data across the enterprise.
- **AI-driven matching and stewardship:** Uses AI for data mastering, ensuring accurate matching of records and supporting data stewardship practices.

‍

![](https://dagster.io/images/guides/689a19c2fd7eafa7831f803f-3e29dd39.png)

#### 7. Great Expectations

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fea-5098058e.png)

‍

Great Expectations (GX) is an open-source data quality framework that helps organizations build trust in their data by defining and testing data quality through "Expectations," which are data quality assertions. It provides an end-to-end solution for managing the data quality process, from initial exploration to continuous validation, ensuring that data remains reliable and accurate. 

**License**: Apache-2.0 

**Repo:** <https://github.com/great-expectations/great_expectations>

**GitHub stars:** 9K+

**Contributors:** 400+

**Features:**

- **Expectation-based testing:** Enables users to define clear, verifiable assertions about their data, covering key data quality dimensions such as schema, freshness, and missingness.
- **End-to-end solution:** Supports the entire data quality lifecycle, from exploratory profiling to continuous validation with historical results tracking.
- **Integrations:** Integrates easily with existing data stacks and can be deployed throughout pipelines to pinpoint and resolve data quality issues quickly.
- **Collaborative interface:** Offers a plain-language interface that allows both technical teams and business stakeholders to participate in the data quality process.
- **Scalability:** GX Cloud can scale across your organization, ensuring data quality processes can be extended wherever they are most needed.

‍

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f800a-7821dd33.png)

‍

#### 8. Informatica Cloud Data Quality

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fe7-82a3d7d7.png)

‍

Informatica Cloud Data Quality is an AI-powered platform to manage the entire data quality process, allowing companies of any size to analyze, clean, and enhance their data for reliable analytics and decision-making. 

**License:** Commercial

‍**Features:**

- **AI-powered:** Uses AI to automatically identify data issues, generate rules, and detect anomalies, speeding up the data quality process.
- **Data profiling and observability:** Offers tools for iterative data analysis, allowing users to continuously monitor and understand data health.
- **Prebuilt rules for data cleaning:** Provides a set of prebuilt rules to quickly profile, clean, standardize, and enrich data.
- **Data transformation tools:** Includes capabilities for data verification, standardization, deduplication, and cleaning to ensure data accuracy and consistency.
- **Elastic infrastructure:** Supports high-performance data pipelines with scalable infrastructure to handle large volumes of data efficiently.

‍

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f800d-eecf45ae.png)

‍

#### 9. SAS Data Quality

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7fe1-6f9cacd3.png)

‍

SAS Data Quality is a platform that simplifies the process of preparing, cleaning, and enriching data for analysis. With its low-code/no-code interface, users can easily manage data quality without needing advanced technical skills or coding expertise. 

**License:** Commercial

**Features:**

- **Low-code/no-code interface:** Offers a visual flow builder for data wrangling and preparation, eliminating the need for coding and enabling self-service data management.
- **Data profiling and cleaning:** Provides industry-leading data profiling tools and prebuilt transformations to detect and resolve data quality issues quickly and efficiently.
- **In-memory processing:** Executes data preparation tasks in memory, allowing for real-time data transformations and speeding up the data quality process.
- **Collaboration and reusability:** Supports collaboration on data projects with reusable workflows, ensuring consistent data quality across teams and throughout the data lifecycle.
- **Entity resolution:** Helps to identify and resolve duplicate or inconsistent data entries, ensuring accurate, high-quality data for analytics.

‍

![](https://dagster.io/images/guides/689a19c2fd7eafa7831f803c-94405a33.png)

#### 10. Collibra Data Quality & Observability

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f7ff1-a2d9f0f2.png)

‍

Collibra Data Quality & Observability is a platform that monitors data quality and pipeline reliability to help organizations quickly identify and resolve data anomalies. By leveraging AI-driven rules and real-time monitoring, Collibra ensures that bad data is caught before it impacts downstream systems. 

**License:** Commercial

**Features:**

- **AI-generated adaptive rules:** Automatically generates thousands of monitoring controls, adapting to data changes to prevent bad data from propagating downstream.
- **Data classification and enforcement:** Automatically classifies sensitive data using prebuilt, industry-specific rules, enforcing data quality controls across your data assets.
- **Generative AI for rule creation:** Supports natural language prompts to write complex data quality rules, accelerating the development of custom rules with reusable templates.
- **Pipeline health monitoring:** Provides visibility into data pipeline health by business unit and data source, allowing teams to detect and resolve data quality issues before they affect operations.
- **Schema change detection:** Monitors for unexpected schema changes to ensure data validity and prevent potential disruptions in dashboards, reports, and other assets.

‍

![](https://dagster.io/images/guides/689a19c1fd7eafa7831f8001-e64d6ad6.png)

‍

### Conclusion

Maintaining high data quality is critical for organizations to drive accurate analytics, informed decision-making, and overall operational efficiency. Data quality platforms offer robust tools and automations to ensure data is accurate, consistent, and reliable across diverse sources and systems. 

By implementing data quality management practices, organizations can mitigate the risks associated with poor data, enhancing business outcomes and ensuring compliance with regulatory standards.

Data quality can also be improved by tightly coupling data quality checks with the orchestration of the processes that generate the data. This is exactly what Dagster’s [Asset Checks](https://dagster.io/blog/dagster-asset-checks) feature enables, proactively alerting you to any data quality issues and preventing erroneous data from flowing to downstream systems.

To learn more about how Dagster can serve as the single pane of glass for the health of your data pipelines, see this [deep dive into Asset Checks](https://dagster.io/blog/dagster-asset-checks).

---

## Data Reliability: Challenges, Measurement & Best Practices

> Data reliability refers to the consistency and dependability of data over time.

Source: https://dagster.io/guides/data-reliability-challenges-measurement-best-practices

### What Is Data Reliability?

Data reliability refers to the consistency and dependability of data over time. It ensures that data is accurate, complete, and can be reproduced under consistent conditions. Reliable data is pivotal in decision-making processes, as it underpins trust and credibility and builds confidence in conclusions drawn from the data.

Ensuring data reliability involves adherence to standardized processes and continuous monitoring. Factors such as data collection methods, storage protocols, and processing algorithms can affect reliability. Organizations must establish data governance frameworks to maintain this reliability, incorporating checks and balances that safeguard the integrity of data at every stage.

This is part of a series of articles about.

### Data Reliability vs. Data Quality and Data Validity

While data reliability, data quality, and data validity are closely related concepts, they differ in their focus and application:

- **Data reliability**: This emphasizes consistency. Reliable data delivers the same results when collected, processed, or analyzed under the same conditions. It ensures data remains dependable over time but does not inherently guarantee accuracy or relevance.
- **Data quality**: [Data quality](https://dagster.io/platform-overview/data-quality) encompasses a broader spectrum, including accuracy, completeness, timeliness, and relevance. High-quality data meets the requirements of its intended use, whereas reliable data is just one component of high-quality data.
- **Data validity**: Validity addresses whether the data accurately represents the real-world phenomena it is meant to describe. Even if data is reliable, it may not be valid if the data collection process is flawed or biased.

For example, if a sensor consistently measures temperature at 2°C above the actual value, the data is reliable (consistent) but not valid (accurate). High-quality data, in contrast, would require both reliability and validity, alongside other factors like completeness and timeliness.

### Common Challenges in Achieving Data Reliability

Achieving data reliability is a complex process that involves addressing various technical, procedural, and organizational challenges. Key challenges include:

1. **Data integration issues:** Combining data from multiple sources often leads to inconsistencies. Variations in formats, structures, and standards can result in unreliable data unless a harmonized integration strategy is in place.
2. **Human errors:** Manual data entry or handling can introduce errors, such as duplication, incomplete records, or misclassifications. These issues compromise data reliability, especially in systems with minimal automation.
3. **System and software limitations:** Legacy systems and outdated software can lack the capabilities needed to ensure data consistency. Inadequate validation checks or limited scalability may lead to data inconsistencies and reliability gaps.
4. **Poor data governance:** A lack of clear policies, roles, and responsibilities can result in unreliable data. Without a governance framework, organizations struggle to enforce standards and ensure accountability in data management.
5. **Data drift:** Over time, data may lose relevance due to changes in collection methods, business processes, or external conditions. This phenomenon, known as data drift, affects the reliability of longitudinal datasets.
6. **Incomplete or missing data:** Gaps in datasets can skew analysis and lead to unreliable conclusions. These gaps often stem from incomplete data collection processes or system failures during data capture.

### How to Measure Data Reliability

Measuring data reliability involves evaluating the consistency, dependability, and reproducibility of data across different conditions and over time. Here are some of the key methods.

#### 1. Consistency Checks

Compare datasets collected under similar conditions to ensure they yield the same results. This involves identifying and resolving discrepancies that may arise from variations in data collection methods or processing. Techniques such as duplicate data analysis and correlation assessments are particularly useful for spotting inconsistencies. For example, identical inputs should produce identical outputs, and any deviation signals a need for further investigation.

#### 2. Reproducibility Testing

Test the same process or system multiple times to verify that it consistently produces the same results. For example, querying a database or running an analytics model repeatedly under identical conditions should yield identical outputs. Reproducibility testing helps detect issues arising from unstable processes, system errors, or data anomalies that might impact downstream applications.

#### 3. Accuracy Validation

Cross-reference data with trusted external sources or benchmarks to confirm its correctness. For example, financial data can be validated against industry reports or regulatory filings. This process ensures that the data reflects real-world conditions or complies with established standards, improving its dependability for decision-making.

#### 4. Completeness Audits

Evaluate datasets to confirm that all required data points are present. Missing values or incomplete records can skew analysis and diminish reliability. Regular completeness audits involve identifying gaps, assessing their impact, and addressing them through improved collection practices, such as enforcing mandatory fields in data entry systems or automated checks for missing attributes.

#### 5. Error Rate Analysis

Monitor and analyze the frequency of errors, such as duplicates, anomalies, or outliers, within the dataset. By tracking these issues over time, organizations can identify patterns that indicate systemic problems. A low error rate signifies that adequate data validation and quality assurance practices are in place, contributing to higher reliability.

#### 6. Stability Over Time

Assess the dataset's stability by monitoring how it evolves over a defined period. Unexpected or unexplained changes may signal reliability issues, such as data drift caused by shifts in collection methods or external influences. By regularly analyzing trends and deviations, organizations can ensure that their data remains consistent and dependable for long-term use.

### Which Data Reliability Metrics Should You Track?

Here are some of the metrics that can help assess data reliability:

- **Duplicate rate:** Measures the percentage of duplicate entries in a dataset. High duplicate rates often indicate flaws in data collection processes or integration issues between systems. Reducing the duplicate rate is crucial for ensuring accurate analysis and avoiding skewed results.
- **Error rate:** Tracks the frequency of incorrect or inconsistent data points within the dataset. Errors may include invalid values, mismatched formats, or logical inconsistencies (e.g., a date of birth after a hire date). Monitoring error rates provides insights into the effectiveness of validation and cleaning procedures.
- **Stability index:** Evaluates variation in key metrics over time. The stability index tracks changes such as fluctuations in data distribution, patterns, or trends, ensuring that unexpected deviations are flagged for review. Consistent data stability is vital for longitudinal analysis.
- **Coverage rate:** Measures the proportion of data points meeting completeness criteria. For example, a dataset might require specific fields to be non-null or a minimum number of entries to be present. A high coverage rate reflects thorough data collection processes and enhances reliability.
- **Timeliness metric:** Assesses how current or up-to-date the data is relative to its intended use. Timeliness is critical for dynamic datasets, such as those used in real-time decision-making or operational systems. Tracking this metric ensures that stale or outdated data does not impact outcomes.
- **Schema adherence rate:** Tracks the percentage of records that conform to predefined schema standards, including data types, formats, and constraints. Low adherence rates may indicate integration issues or input errors that need resolution.
- **Anomaly detection rate:** Measures the frequency of identified anomalies in the data, such as outliers, unexpected patterns, or inconsistencies. This metric highlights potential reliability risks and supports proactive resolution through anomaly detection systems.
- **Latency metric:** Evaluates the time delay between data collection and availability for use. Lower latency indicates a more efficient pipeline, improving the reliability of time-sensitive datasets, such as those used for monitoring or operational analytics.

### 6 Best Practices for Maintaining Data Reliability

#### 1. Establish Clear Data Governance Policies

Establishing clear data governance policies is fundamental for maintaining data reliability. These policies define the responsibilities, standards, and procedures for data management. Clear governance ensures consistency across the organization, reducing discrepancies that may compromise reliability.

Communication and training are critical to policy success. Staff must understand governance policies and their respective roles. This awareness fosters a culture of compliance and accountability, where employees prioritize data reliability practices in their daily operations.

#### 2. Ensure Proper Data Collection Processes

Reliable data starts with proper collection processes. Establishing standardized protocols for data entry and acquisition helps minimize errors at the source. This includes using validated forms, automated data capture tools, and enforcing mandatory fields to reduce incomplete or inaccurate entries.

Regularly reviewing and refining collection methods is equally important. Organizations should ensure that data sources remain credible, collection methods are secure, and procedures adapt to changing requirements. Proper data collection forms the foundation of trustworthy data.

#### 3. Invest in Data Cleaning Processes

Data cleaning eliminates inaccuracies, inconsistencies, and redundancies in datasets, enhancing reliability. Cleaning techniques include identifying and removing duplicate records, correcting formatting issues, and filling in missing values using established rules or algorithms.

Automated cleaning tools and scripts can streamline the process, especially for large datasets. Periodic cleaning ensures datasets remain useful and reliable over time. Clean data not only supports analytics but also reduces the risk of operational inefficiencies caused by poor-quality information.

#### 4. Implement Data Validation Techniques

Implementing data validation techniques is crucial for ensuring data reliability. Validation involves checking data for errors, inconsistencies, and compliance with predefined rules before it is processed or stored. Common techniques include range checks, format checks, and cross-referencing data against known sources.

Using automated validation tools can significantly improve efficiency and accuracy. These tools flag anomalies in real-time, enabling quick corrections and reducing the risk of propagating errors through the system.

#### 5. Regular Data Quality Assessments

Conducting regular data quality assessments ensures ongoing data reliability. These evaluations involve examining datasets for accuracy, completeness, and timeliness, identifying areas that need attention or improvement. Regular assessments can preempt potential reliability issues.

Automated data quality tools can streamline these assessments. By implementing such solutions, organizations can conduct frequent evaluations, identifying and addressing reliability challenges proactively.

***Learn more in our detailed guide to data quality checks (coming soon).***

#### 6. Invest in Staff Training and Awareness

Investing in staff training enhances data reliability by equipping employees with the skills to handle data accurately. Training programs should focus on best practices for data management, addressing common pitfalls and strategies for maintaining integrity. Well-trained staff are critical to embedding reliability into daily processes.

Promoting data literacy and awareness initiatives also supports reliability. Ensuring employees understand the implications of data misuse encourages responsible behavior.

### Improving Data Reliability with Dagster

You can improve the reliability of your data platform by using Dagster as the control plane for your data.

Dagster’s [Asset Checks](https://dagster.io/blog/dagster-asset-checks) feature also allows you to automate data reliability checks and validate data assets as soon as they are materialized, proactively alerting you to any issues and preventing malformed or erroneous data from flowing to downstream systems.

Its robust logging and metadata collection, alongside [Dagster+ Insights](https://docs.dagster.io/guides/observe/insights), lets you track and visualize the health and reliability of your data system at large, helping to monitor anomalies and long-term trends such as data drift.

The [Dagster+ Catalog](https://dagster.io/blog/dagster-plus-calatog-a-new-built-in-asset-library), meanwhile, enables master data management and easier discovery of trusted data assets across your organization. Unlike traditional data catalogs, Dagster is also aware of the context and processes surrounding the materialization of these assets, offering your team built-in monitoring and observability capabilities that allow you to identify issues and perform root-cause analyses from within the same platform.

To learn more about how Dagster can serve as the single pane of glass for the health of your data pipelines, see this [deep dive into Asset Checks](https://dagster.io/blog/dagster-asset-checks).

‍

---

## dbt Lineage in Action: 3 Use Cases, Challenges and Best Practices

> Data lineage refers to the end-to-end tracking of data as it moves through systems, from its origin to its final destination.

Source: https://dagster.io/guides/dbt-lineage-in-action-3-use-cases-challenges-and-best-practices

### Why Is Data Lineage Important in dbt Projects? 

Data lineage refers to the end-to-end tracking of data as it moves through systems, from its origin to its final destination. It maps the flow through every transformation, integration, and aggregation step, providing a detailed view of how data evolves. Data lineage answers questions about the sources, transformations, and usage of each dataset, making it possible to trace data anomalies back to their root causes.

Data lineage plays a critical role in dbt projects by giving data and analytics engineers the context they need to manage both upstream and downstream dependencies effectively. In dbt, where models are tightly coupled and changes in one can impact many others, lineage enables teams to prevent issues and respond quickly when problems arise.

### 3 Key Use Cases of Data Lineage in dbt

Here are some of the common ways data lineage is used in dbt projects:

1. **Understanding downstream impact** before deploying changes. When modifying a core model, engineers can trace its dependencies to see what downstream assets—like tables, dashboards, or reports—might be affected. This supports impact analysis during deployment testing, helping teams evaluate the criticality of downstream components and avoid breaking high-priority business assets.
2. **Faster root cause analysis** during incidents. If an issue appears, such as missing data or unexpected null values, lineage helps identify the upstream models causing the issue, as well as which downstream models or reports rely on the affected source. This enables teams to quickly take corrective action, assess who and what might be impacted, and notify stakeholders early.
3. **Aiding data discovery and data quality** on the upstream side. When business users rely on dashboards for important decisions, they can use lineage to trace metrics back to their sources. This allows them to inspect how metrics are defined, what data is used, and whether the inputs are reliable and trustworthy.

In both development and business contexts, data lineage makes dbt projects more transparent, reliable, and easier to manage at scale.

### How Data Lineage Works in dbt 

Data lineage in dbt is built around the dependency graph, which automatically maps how sources, models, snapshots, metrics, and exposures connect. This graph is generated when you use functions like ref() and source(), and it provides a visual view of how data flows from raw inputs to final outputs. Each node represents a dataset or transformation, and edges show the dependencies between them.

**Models** are the core of this process. They are SQL or Python files that apply transformation logic to raw data. Staging models handle standardization and cleanup, while transformation models build on them to create business-ready datasets such as summaries or metrics. Clear naming and defined dependencies keep the lineage graph accurate and maintainable.

**Snapshots** extend datasets by capturing historical changes in source data. For example, a customer’s changing address is stored with timestamps, making it possible to reconstruct data states over time. Snapshots automatically integrate into the graph when referenced by models.

**Metrics** add another layer by defining reusable business calculations directly in dbt. They encapsulate logic, such as revenue or average order value definitions, which can be consistently applied across multiple models. 

**Exposures** connect the lineage graph to external tools, documenting how transformed data powers dashboards, reports, or machine learning models.

**At the technical level, lineage relies on two functions:**

- source() marks the raw origin of data. Models referencing a source become dependent nodes.
- ref() links one model to another, ensuring dbt executes dependencies in the right order.

Together, these elements make lineage in dbt both explicit and automated, giving teams a clear, verifiable map of data movement from ingestion to business use.

### Data Lineage Challenges in dbt 

As dbt projects grow in size and complexity, maintaining clear and usable lineage becomes more difficult. Two of the main challenges are scaling large DAGs and capturing lineage at the column level.

#### Scaling Data Pipelines

When the number of models, sources, seeds, macros, and exposures increases into the thousands, the lineage graph (DAG) can become overwhelming. Large DAGs make it harder to spot inefficiencies, duplicated logic, or violations of best practices. 

Without a structured approach, teams may find it difficult to audit or even navigate the graph effectively. To manage this, projects need strong naming conventions, consistent documentation, and automated tools like the dbt project evaluator to surface issues that would be impractical to check manually.

#### Column-Level Lineage

Most lineage systems, including dbt’s DAG, operate at the table level. This works well for understanding dependencies but falls short when teams need visibility into how individual columns are created and transformed. For example, if a metric is derived from several joined or pivoted tables, table-level lineage alone does not reveal the exact transformations applied to the fields. 

As organizations demand more detailed transparency, column-level lineage becomes essential for debugging, auditing, and ensuring trust in data definitions. Features like column-level lineage in dbt Explorer are designed to close this gap, offering more granular insight into data flows.

Together, these challenges highlight the importance of balancing project structure, tooling, and detail level to keep lineage useful rather than overwhelming.

### Best Practices for Robust Data Lineage with dbt 

#### 1. Automate Lineage Capture and Maintenance

Automation is essential for reliable data lineage, especially in rapidly evolving dbt projects. By leveraging dbt’s built-in DAG, teams can ensure that lineage is captured automatically as part of the development process. This reduces manual effort, prevents outdated documentation, and eliminates the risk of missing critical dependencies. Scheduled builds and documentation generation help keep lineage information current, even as the project scales and evolves.

Automating lineage maintenance also involves integrating linting or continuous integration checks that validate changes to models and dependencies. When developers propose new models or modify SQL logic, automated checks can warn about missing references or potential impacts on downstream tables. These guardrails enable teams to catch issues earlier in the development lifecycle, minimizing the risk of breaking existing workflows.

#### 2. Ensure Both Upstream and Downstream Visibility

Complete lineage documentation requires visibility into both upstream sources and downstream consumers. dbt makes it easy to trace upstream dependencies through the use of references, but teams should also document where outputs are consumed—whether by dashboards, APIs, or data science workflows. This dual perspective enables a full understanding of impact, which is necessary for safely managing changes and troubleshooting failures.

With clear upstream and downstream lineage, teams can perform true impact analysis before making modifications. For example, changing a parameter upstream can trigger an evaluation of all affected downstream reports, preventing surprises in production. This end-to-end visibility is essential for organizations looking to maintain high reliability and accountability in their analytics stack.

#### 3. Enrich Lineage with Business Context

Technical lineage provides visibility into the mechanics of data transformation, but adding business context greatly increases its value. Enriching lineage with metadata such as data owner, business glossary terms, and usage notes enables stakeholders to understand not just how data is processed, but why it exists and what it represents. dbt’s documentation blocks and metadata features make it possible to annotate models and columns directly within the codebase.

Business context facilitates collaboration between technical and non-technical staff, ensuring data consumers know whether a dataset is fit for purpose. Tagging sensitive or regulated fields, for instance, helps meet compliance requirements and supports better governance. When business context is paired with technical lineage, organizations can bridge the gap between raw data and strategic decision-making.

#### 4. Integrate Lineage with Change Management and Alerts

Integrating lineage with change management processes ensures that all modifications to data models and pipelines are assessed for potential impact before being merged or deployed. dbt’s integration with version control systems and CI/CD pipelines can be leveraged to automate validation, flag risks, and require peer reviews for changes affecting critical dependencies. By aligning lineage with change management, broken pipelines and downstream disruptions can be minimized.

Real-time alerts tied to lineage-aware checks further enhance resilience. For example, if a source table is unexpectedly altered, automated alerts can notify responsible teams and identify all impacted models, dashboards, or reports. Proactive notification of schema or dependency changes allows teams to react quickly and maintain trust in data deliveries without relying solely on manual reviews or spot checks.

#### 5. Track Lineage of dbt Model Usage with Exposures

Lineage in dbt is not limited to transformations inside the warehouse; it should also extend to tracking how models are consumed. Many teams stop at documenting dependencies between models, but without visibility into where outputs are used, impact analysis remains incomplete.

Exposures in dbt provide a structured way to capture this usage. By defining exposures for dashboards, reports, or machine learning jobs that depend on dbt models, teams can formally document the connection between transformations and business outcomes. This makes it possible to trace the effect of a model change not just on downstream models, but on the actual decision-making tools used by stakeholders.

Beyond exposures, tracking lineage of dbt model usage can be enriched by integrating dbt with observability platforms that log all queries against warehouse tables. These tools reveal which teams, queries, or applications access a model, giving engineers evidence of actual consumption rather than assumed dependencies.

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to scale beyond dbt and track the lineage of not only your SQL transformations but also your entire data platform.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and AI workflows. With a Python-native approach, it unifies SQL, Python, and more into a single testable and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## dbt Python: Running Transformations with Python on Data Warehouses

> A dbt Python model is a type of transformation within the dbt (data build tool) ecosystem that lets developers write business logic using Python, instead of SQL.

Source: https://dagster.io/guides/dbt-python-running-transformations-with-python-on-data-warehouses

### What Is a dbt Python Model? 

A dbt Python model is a type of transformation within the dbt (data build tool) ecosystem that lets developers write business logic using Python, instead of SQL. Traditionally, dbt has relied on SQL-based transformations for analytics engineering, but with the support for Python models, users can execute Python code directly in data warehouses that support external languages, such as Snowflake, Databricks, and BigQuery. 

This extension bridges the gap between analytics engineering and data science, allowing for more complex data transformations and machine learning operations natively within dbt workflows. Python models in dbt are defined as .py files within the models directory of a dbt project. These scripts have access to input data as data frames, and allow the use of libraries like pandas or PySpark, depending on the data warehouse. 

The outputs are written directly back to the warehouse, enabling the results to be queried and joined just like any other table or view produced by SQL models. This integration keeps workflows consistent across SQL and Python, enabling teams to leverage both languages as needed for complex analytics requirements.

### Pros and Cons of Running Python in dbt 

**Here are a few reasons to use Python in dbt:**

- Python has a rich ecosystem of libraries and tools, and supports a wide range of data processing and machine learning workflows, which SQL alone can't handle efficiently. This makes it easier to implement transformations, statistical models, or predictive analytics directly in the warehouse.
- Python models return data frames rather than SQL query results, enabling flexible manipulation using tools like pandas or PySpark. This makes it easier to work with complex data types or apply custom business logic. 
- Python models integrate with dbt's existing structure, so they support referencing sources and models with dbt.ref() and dbt.source(), just like SQL models. Materialization options like table and incremental are also supported, allowing Python models to fit into standard dbt workflows.

**Here are some limitations and drawbacks to be aware of:**

- dbt’s Python support is currently limited to just three platforms—Snowflake, Databricks, and BigQuery. Users must rely on these platforms' ability to execute Python code, meaning local development or execution isn't possible. This reliance also adds overhead in terms of setup and environment management.
- Performance is another concern. Translating efficient SQL models into Python doesn't always yield good performance. Python offers many ways to write the same logic, and inconsistent coding practices can lead to slower models. 
- As of the time of this writing, dbt Python models don’t work with dbt Fusion, the next-generation rewrite of dbt with a significantly improved developer experience. 

### How Does dbt Interpret Python Code? 

When you run a Python model in dbt, the code isn't executed locally. Instead, dbt relies on the data warehouse's cloud services to interpret and execute the Python code. This design ensures that all transformations occur inside the warehouse, avoiding data movement and eliminating the need for updates or synchronization.

- On **Snowflake**, dbt uses the Snowpark API to handle Python models. The Python code is compiled by dbt, passed to Snowflake, and executed as a stored procedure. The entire computation runs within Snowflake’s environment, keeping the data in place.
- For **BigQuery**, dbt uses Dataproc, which provides managed Hadoop and Spark clusters to process Python code.
- **Databricks** integrates with dbt through the dbt-databricks adapter, which supports running Python models using PySpark. When a Python model is executed, dbt compiles the code and submits it to a Databricks cluster where it runs within a Spark session. This allows the model to process data in parallel across distributed nodes, leveraging Spark's scalability.

These cloud services are optimized for large-scale data workloads. However, they introduce considerations such as costs, cluster sizing, storage configuration, and network access. To ensure reliable performance, users must manage these factors according to the demands of their Python models.

### Tutorial: Defining and Configuring dbt Python Models with Snowpark 

**Prerequisites**

To define a Python model in dbt, and run the Python code on your data using Snowflake’s Python API, first create a .py file in the models/ directory. Each file must include a function named model(dbt, session). The dbt parameter provides access to project context and references, while session represents the warehouse connection, used to read and write data as data frames.

Inside the function, use dbt.ref() or dbt.source() to load upstream models or source tables as data frames. 

**Performing transformations with Python**

Perform your transformations using pandas, PySpark, or the platform-specific DataFrame API, and return a single DataFrame as the output. This returned DataFrame is what dbt will write back to the data warehouse.

```javascript no-line-numbers
from snowflake.snowpark.functions import col

def model(dbt, session):
	dbt.config(materialized="table")
    orders_df = dbt.ref("fct_orders")  # ref to an existing model/table
    filtered_df = orders_df.filter(col("status") == "completed")
    return filtered_df
```

‍

‍

![](https://dagster.io/images/guides/68b87cb0b4c6996317c0721f-fa8b595c.png)

**Options for configuring Python models**

You can configure Python models in three ways:

1. Inside `dbt_project.yml` to set defaults for multiple models
2. In a YAML file in the `models/` folder for per-model settings
3. Inline in the `.py` file using `dbt.config()`

Only literal values (like strings, numbers, and booleans) can be passed to dbt.config(). More complex configurations should be placed in a YAML file and accessed via dbt.config.get() in your model code, like this:

```javascript no-line-numbers
# models/config.yml
version: 2‍models:
	- name: my_python_model
    config:
    	materialized: table
        target_name: "{{ target.name }}"‍

# models/my_python_model.py
def model(dbt, session):
	target_name = dbt.config.get("target_name")‍
    df = dbt.ref("orders")‍
    if target_name == "dev":
    	df = df.limit(500)‍
        
    return df
```

‍**Using materializations**

Python models support table and incremental materializations. To build incremental models, use dbt.is\_incremental to conditionally filter for new data.

```javascript no-line-numbers
def model(dbt, session):
	dbt.config(materialized="incremental")  # Materialization type‍
    df = dbt.ref("events")  # Reference source table (can be a model or source)‍
    
    if dbt.is_incremental:
    	# Get max timestamp from existing incremental table
        max_ts_query = f"SELECT MAX(event_time) FROM {dbt.this}"
        max_ts = session.sql(max_ts_query).collect()[0][0]‍
        
        if max_ts:
        	# Filter only new data
            df = df.filter(df["event_time"] > max_ts)‍
            
    return df
```

‍

![](https://dagster.io/images/guides/68b87cb0b4c6996317c07225-f701b3d2.png)

‍

**Using helper functions and Python packages**

You can also define helper functions within the model or import third-party packages. To use packages, list them via dbt.config(packages=...) or in the model YAML.

```javascript no-line-numbers
import holidays‍

def is_holiday(date):
	return date in holidays.France()‍
    
def model(dbt, session):
	dbt.config(materialized="table", packages=["holidays"])
    df = dbt.ref("calendar").to_pandas()
    df["IS_HOLIDAY"] = df["DATE"].apply(is_holiday)
    return df
```

‍

![](https://dagster.io/images/guides/68b87cb0b4c6996317c07222-02870be9.png)

‍

![](https://dagster.io/images/guides/68b87cb0b4c6996317c0721c-35c4f2c8.png)

‍

While Python models offer flexibility, note that functions can't yet be reused across models. UDF support is platform-dependent, and currently limited on some warehouses like Snowflake.

### Best Practices for Running Python in dbt 

Here are some useful practices to keep in mind when dbt for Python code.

#### 1. Use Native Warehouse Data Frames

Always prefer the native DataFrame API provided by your data platform—such as Snowpark on Snowflake, PySpark on Databricks, or BigQuery’s Spark interface—over pandas. These APIs allow computations to run directly on the warehouse engine, which is optimized for distributed processing and avoids pulling large datasets into Python memory. 

‍

For example, with Snowpark, you can use .filter(), .withColumn(), or .join() to perform operations directly in Snowflake’s execution layer. Converting to pandas with .to\_pandas() should be reserved for small datasets or operations not supported by the native API. Pulling data into pandas bypasses warehouse execution, introduces memory constraints, and slows performance due to serialization and data transfer overhead. 

#### 2. Keep Your Python Models Well-Structured

Structure your model code for clarity and maintainability. Keep your main model(dbt, session) function focused and concise. Move non-trivial logic into helper functions defined within the same file, and isolate configuration retrieval at the top of the script. For example:

```javascript no-line-numbers
def transform_orders(df):
	return df.filter(df["status"] == "completed")‍
    
def model(dbt, session):
	dbt.config(materialized="table")
    orders_df = dbt.ref("fct_orders")
    return transform_orders(orders_df)
```

‍

Avoid deeply nested logic or large, monolithic functions. Name variables clearly and consistently, and use comments to document complex operations or business-specific logic. Following these conventions reduces onboarding time for new team members and makes debugging easier.

#### 3. Testing the Python Models

dbt allows you to test the integrity of your data transforms using data tests, including the built-in generic tests, such as unique, not\_null, or accepted\_values. These tests are defined in your model’s .yml file and dbt will ensure that your model outputs satisfy these requirements after the Python model is materialized. 

dbt currently doesn’t support unit tests for Python models. Since dbt executes Python models only in the data platform’s custom execution environment, locally testing the logic of Python models during development is not possible without mocking dbt’s Python interface.

#### 4. Integration with SQL Models

Python and SQL models in dbt are fully interoperable through dbt.ref() and dbt.source(). This allows Python models to read data from upstream SQL transformations, or to feed downstream SQL models. Ensure the naming conventions and materialization types are compatible. For example, avoid using ephemeral models as inputs to Python models since they are not materialized as physical tables.

#### 5. Implement Proper Error Handling in Your Python Models

Python models are susceptible to runtime errors, especially when querying external data or performing complex transformations. Wrap risky logic in try-except blocks to catch and log exceptions. For example:

```javascript no-line-numbers
try:
	df = dbt.ref("events")
    result = df.filter(df["event_type"] == "click")
except Exception as e:
	raise Exception(f"[{dbt.this}] Failed to process events: {e}\n{tb}") from e
```

‍

![](https://dagster.io/images/guides/68b87cb0b4c6996317c07228-7bcf2840.png)

‍

Always validate inputs and outputs. For example, check that input DataFrames are not empty and required columns are present before proceeding. If using dynamic SQL via session.sql(), verify the result shape and handle edge cases like None values or unexpected schema changes. 

***Related content: Read our guide to dbt test (coming soon)***

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to seamlessly expand your data platform to include Python-based data assets, while allowing dbt to focus on what it does best: SQL transformations.

Unlike dbt, which provides only limited visibility into Python models and a sub-par testing and developer experience for Python models, Dagster was built from the ground-up to run Pythonic data flows.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and ML workflows. With a Python-native approach, it unifies SQL, Python, and more into a single, testable, and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## dbt SQL Models: the Basics and Building Your First dbt Model

> SQL models in dbt are SQL files that define transformations in a data warehouse, each creating a view or table from a SELECT statement.

Source: https://dagster.io/guides/dbt-sql-models-the-basics-and-building-your-first-dbt-model

### What Are SQL Models in dbt? 

SQL models in dbt (data build tool) are SQL files that define data transformations within a data warehouse. Each model represents a transformation step, often a SELECT statement, that is materialized as a new view or table in the data warehouse. dbt handles dependency management and the orchestration of these models, allowing data teams to focus on writing SQL queries without handling pipeline execution code.

dbt models are modular and reusable. Users write familiar SQL, enhanced with dbt features like referencing, templating, and configuration blocks. These models are version-controlled, frequently tested, and documented, ensuring high data quality and transparency. As the heart of dbt projects, SQL models help teams standardize, govern, and optimize analytics engineering tasks.

### Why dbt SQL Models Democratize Data Pipelines

By making SQL models the central unit of work, dbt lowers the barrier for building production-grade data pipelines. Analysts and analytics engineers can contribute transformations without learning orchestration tools or writing procedural ETL code. The same skills used for ad-hoc analysis (writing SQL) apply directly to creating automated, maintainable pipelines. This reduces reliance on specialized data engineering resources and shortens the time from raw data to consumable insights.

Because dbt models are just SQL files with configuration metadata, they fit naturally into modern software practices like git-based version control, pull requests, and code review. Teams can collaborate with the same workflows they already use for application development, with clear history, testing, and deployment controls. This alignment between analytics and engineering practices makes it easier for more people in an organization to safely contribute to data pipelines while maintaining governance and quality.

### Key Concepts of dbt SQL Models 

#### Jinja Templating

dbt integrates Jinja, a popular templating engine, directly into its SQL models. This allows users to add logic, loops, variables, and conditionals to their SQL, making queries more dynamic and reusable. You can use Jinja to parameterize queries, generate repetitive SQL code, or implement logic that alters the structure or filters of a SQL statement on the fly.

The use of Jinja in dbt models encourages consistency and reduces errors by centralizing reusable snippets or macros. For example, implementing a column calculation or a filter condition once as a macro makes it accessible across the entire project. Developers benefit from efficient code maintenance because updates to business logic or transformations propagate to all dependent models.

#### Materializations

Materializations in dbt define how a SQL model's output is stored in the data warehouse. Common materializations include view, table, incremental, and ephemeral. A view does not store physical data but references the underlying query, updating dynamically every time it's queried. A table physically persists the results and is rebuilt on each execution, while incremental models update only changed data, improving performance for large datasets. Ephemeral models are inlined into downstream models as CTEs.

Each model declares its materialization type within its file or project configuration. Materializations give developers control over workflow performance and cost. Choosing the right materialization depends on business requirements such as freshness, speed, and data volume. dbt’s flexible approach means teams can optimize for query responsiveness or warehouse spend as their needs evolve.

#### Source Declarations and the ref() Function

Defining sources in dbt formalizes external tables as referenced inputs in a project. A source declaration captures metadata about raw data objects available in the warehouse, making downstream dependency management explicit. This allows dbt to validate input tables, enforce freshness, and help document upstream data origins within the dbt documentation site. The `ref()` function is the primary mechanism for referencing models within dbt.

Rather than hardcoding table names, `ref('model_name')` dynamically resolves dependencies and abstracts away environment-specific naming conventions. This approach enables dbt to build a directed acyclic graph (DAG) that orders model builds intelligently and provides traceability for all data lineage. 

#### Data Tests

In dbt, data tests are SQL queries designed to validate data quality. There are two main types: singular tests, which are custom queries that return zero rows if the data passes the test, and generic tests, which are reusable checks like uniqueness or not null constraints applied to columns or models. These tests run as part of dbt’s workflow, making data quality checks an integral part of the transformation process.

Recent updates to dbt have also added unit tests. Much like their counterparts in the software engineering world, dbt unit tests allow you to verify the expected outputs of your models given a set of static inputs.

Embedding tests directly in the analytics pipeline ensures errors are detected early. If a test fails, dbt stops execution, allowing teams to fix upstream issues before results are consumed in downstream analyses or dashboards. The automation of these integrity checks helps uphold confidence in data products.

#### Version Control and Collaboration

dbt is built around the principles of modern software development, emphasizing version control and collaboration through git. Each dbt project is a directory of SQL and YAML files that can be tracked, branched, and merged via a source control system. This ensures all changes are auditable, reversible, and can be peer-reviewed before deployment, improving development quality and accountability.

Collaboration is further supported by standardized project structures and documentation generation. Multiple team members can work on models simultaneously, contributing enhancements or bug fixes without disrupting production code. 

### Quick Tutorial: Writing Your First SQL Model in dbt 

This tutorial will show you how to create your first SQL model in dbt. Instructions are adapted from the [dbt documentation](https://docs.getdbt.com/docs/build/sql-models).

**Step 1: Create a .sql file**

Start by creating a `.sql` file inside the `models/` directory of your dbt project. Each SQL file corresponds to one model, and it must contain a single `SELECT` statement. The model’s name must exactly match the filename, including case sensitivity, and it's a best practice to use underscores instead of dots in names (e.g., `complaints_report.sql`).

Here's a simple example:

```javascript no-line-numbers
with customer_complaint_summary as (    
	select        
    	customer_id,        
        min(complaint_date) as first_complaint_date,        
        max(complaint_date) as most_recent_complaint_date,        
        count(id) as number_of_complaints    
    from jaffle_shop.customer_complaints    
    group by customer_id
)‍    			

select    
	c.customer_id,    
    c.first_name,    
    c.last_name,    
    ccs.first_complaint_date,    
    ccs.most_recent_complaint_date,    
    ccs.number_of_complaints
from jaffle_shop.customers c
left join customer_complaint_summary ccs    
	on c.customer_id = ccs.customer_id
```

‍**Step 2: Configure and run the model**

When you run `dbt run`, dbt will wrap this query in a `CREATE VIEW AS` or `CREATE TABLE AS` statement, depending on your model's materialization settings. By default, dbt creates views and places them in the target schema based on your active profile.

You can change how dbt builds the model using a configuration block at the top of the file:

```javascript no-line-numbers
{{ config(    
	materialized='table',       
    schema='jaffle_shop'    
) }}
```

Configurations can also be set globally in your `dbt_project.yml` file or scoped to directories within the `models/` folder.

![](https://dagster.io/images/guides/68dd432f26b8a2d759017b6a-4c58e24a.png)

‍**Step 3: Define dependencies between models**

To build dependencies between models, use the `ref()` function instead of hardcoding table names. For example:

```javascript no-line-numbers
select    
	c.customer_id,    
    c.first_name,    
    c.last_name,    
    cc.complaint_date,    
    cc.complaint_text
from {{ ref('customers') }} as c
left join {{ ref('customer_complaints') }} as cc    
	on c.customer_id = cc.customer_id
```

‍**Note:** It’s important to ensure that `customers` and `customer_complaints` exist in the model directory.

This ensures dbt understands model dependencies and can build a directed acyclic graph (DAG) to run models in the correct order. It also supports environment-aware builds, allowing dbt to adjust schema and table names based on your current target environment.

Once your models are defined and configured, execute `dbt run` to materialize them in your data warehouse. This simple workflow—writing SQL, configuring models, and running dbt—forms the foundation of the analytics engineering workflow in dbt.

### Best Practices for dbt SQL Development 

Here are some of the ways that developers can improve their use of dbt for SQL-based projects.

#### 1. Maintain Modular and Reusable SQL Code

Structuring dbt projects with modular SQL code makes transformation pipelines easier to build, debug, and evolve. Instead of placing all logic in a single large model, split transformations across multiple models, each focused on a discrete task—such as cleaning raw data, applying business logic, or joining data sources.

For example, a raw sales dataset might first pass through a staging model that standardizes field names and formats. An intermediate model might then calculate key metrics, while a final model joins data with dimensions to produce a reporting-ready dataset. Each of these layers can be tested and maintained independently.

Avoid repeating business rules or filters across models. If the same logic is applied in multiple places, abstract it into macros or upstream models. This reduces maintenance overhead and minimizes the risk of logic divergence. When working in teams, modular code allows developers to work concurrently without stepping on each other’s changes.

#### 2. Use Staged Sources

Staging models are foundational in dbt and should be used to clean and normalize raw data before applying any business logic. These models handle basic tasks such as renaming columns, casting data types, trimming whitespace, and applying consistent date formats. By standardizing input data early, downstream models become simpler and more predictable.

Prefix all staging models with `stg_` to distinguish them from other transformation layers. For example, raw customer data from a CRM might be modeled as `stg_crm_customers`. This model would handle inconsistencies like date formatting or inconsistent string casing, while retaining a one-to-one mapping with the source table.

Staging also supports source freshness testing. By clearly defining and isolating source inputs, dbt can automatically monitor whether source tables are updated as expected. This increases transparency and helps teams detect issues early in the pipeline.

#### 3. Choose the Correct Materialization

Selecting the right materialization in dbt depends on data volume, update frequency, and performance needs. The four primary materializations—view, table, incremental, and ephemeral—serve different purposes:

- Use **views** for lightweight, frequently updated models that don't require persistent storage. They’re simple to maintain and suitable for logic that changes often, but they can be slow for large datasets since queries execute on every access.
- Use **tables** when the data doesn’t change often or performance is a concern. Tables store results physically, offering fast access at the cost of data freshness.
- Use **incremental** models when dealing with large datasets that update over time. These models append or update only new data during subsequent runs, significantly reducing load times and warehouse costs. They require careful configuration to handle inserts, updates, or deletions correctly.
- Use **ephemeral** models for short-lived transformations reused in downstream models but not materialized in the warehouse. They are compiled as CTEs in dependent models, reducing clutter and improving query efficiency.

Each materialization balances performance, cost, and complexity. Understanding the trade-offs helps optimize model design across different data processing scenarios.

#### 4. Use Consistent Naming Conventions

Naming conventions in dbt help signal the purpose and lifecycle stage of each model. For clarity and uniformity, use lowercase with underscores (`snake_case`) for all model and column names. Prefix models to indicate their role: `stg_` for staging, `int_` for intermediate transformations, `dim_` for dimensions, and `fct_` for fact tables.

For example, `stg_salesforce_opportunities` might flow into `int_sales_pipeline`, which is used in a `fct_revenue` model. Naming models and fields consistently enables faster navigation, better documentation, and easier collaboration.

Avoid ambiguous or overly generic names like `data_table` or `model1`. Instead, use domain-specific and descriptive terms that clearly reflect the model’s content and function. Document naming conventions in your dbt project README or development guide to help onboard new contributors and ensure alignment across teams.

#### 5. Leverage Macros Wisely

dbt macros are a powerful mechanism for reducing repetition and centralizing complex logic, but they should be used selectively. A macro is most effective when a piece of SQL logic is repeated in multiple places or when you need to generate SQL dynamically. For example, if you calculate customer lifetime value in multiple models, implementing that calculation once in a macro ensures consistency and simplifies updates. This centralization helps avoid errors and reduces the maintenance burden.

However, excessive macro use can make SQL models harder to read and debug. When key transformation logic is hidden behind a macro, developers must navigate to the macro definition to understand what the model is doing. This adds friction during development and troubleshooting, especially for new team members. In some cases, keeping logic inline within a model makes it easier to review and maintain.

A good guideline is to avoid abstracting logic into a macro unless it’s repeated across at least two or three models or is too complex to maintain directly in the model. Macros should improve maintainability, not hide essential business logic. If you find that a macro is used only once or makes a model’s purpose less obvious, it’s better to keep the SQL explicit in the model itself.

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to go beyond just SQL transformations and seamlessly connects your dbt project with your wider data platform.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and AI workflows. With a Python-native approach, it unifies SQL, Python, and more into a single testable and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## dbt Unit Testing: Why You Need Them, Tutorial & Best Practices

> dbt unit tests are a feature within dbt that enable the validation of a dbt model's SQL logic in isolation, using a small, controlled set of static input data

Source: https://dagster.io/guides/dbt-unit-testing-why-you-need-them-tutorial-best-practices

### What Is dbt Unit Testing? 

dbt unit tests are a feature within dbt (data build tool) that enable the validation of a dbt model's SQL logic in isolation, using a small, controlled set of static input data. Unit tests are different from dbt's traditional data tests, which run assertions against live data, typically the outputs of materialized models in a production environment.

dbt unit tests let you catch logical flaws or regressions early, ensuring your transformations produce correct results under different scenarios. It’s analogous to unit testing in software engineering: you want precise, repeatable confirmation that a piece of logic works in isolation before it’s part of a larger pipeline.

In dbt, unit testing has become more practical with improvements in dbt Core, the introduction of generic tests, and increased community-driven support for seed data and macros. Unit tests can simulate edge cases, verify business logic, and catch problems that might not be evident using only data tests. 

### The Need for dbt Unit Tests: Why Data Tests Are Not Enough 

While dbt's built-in data tests are useful for catching data quality issues like null, duplicate, or invalid values, they come with limitations that reduce their effectiveness at scale:

- **dbt data tests validate assertions against live datasets:** This means the results can change unpredictably as either the data or the transformation logic changes. When a test fails, it’s often unclear whether the problem lies in the data itself or in recent code changes, making debugging more difficult.
- **These tests can be slow and resource-intensive:** Running checks on large tables, especially with multiple assertions across many columns, can consume significant compute and extend pipeline run times. This makes it hard to run them frequently, especially in development environments.

dbt unit tests use fixed input and output data, allowing them to run quickly and deterministically. They provide targeted validation of transformation logic without depending on the current state of the data. This makes unit tests a more scalable and precise tool for verifying business rules and preventing regressions in complex pipelines.

### When to Add a dbt Unit Test to Your Model 

**You should add unit tests when your model has any of these characteristics:** 

- **Includes logic that is complex or error-prone**. This includes SQL that uses regex, date math, window functions, or long `case when` chains. 
- **Is mission critical**, such as public-facing models, models bound by contracts, or ones that sit directly upstream of exposures. Testing here helps prevent downstream issues where errors would have a bigger impact.
- **Uses truncation or other custom transformations** that go beyond built-in functions.
- **Handles logic that caused issues in the past** or involves edge cases that might not always appear in production data. 
- **Is under active development** and you are concerned about regressions. Unit tests can help ensure refactoring doesn’t break expected behavior, especially during significant rewrites.

‍

**When you should not use unit testing**

dbt Labs advises running unit tests in development or CI only, since they rely on static inputs and do not add value in production runs. They can be excluded from production builds with the -`-exclude-resource-type` flag or the DBT\_EXCLUDE\_RESOURCE\_TYPES variable to save compute.

**Limitations of unit testing in dbt**

While dbt unit tests bring advantages for validating transformation logic, they also have some limitations to consider:

- **Setup overhead**: Defining mocked inputs and expected outputs requires extra effort compared to data tests, especially for complex models with many dependencies. This can slow down adoption if teams don’t establish clear patterns or reusable test frameworks.
- **Maintenance burden**: As business logic evolves, seed or mock data used in unit tests must also be updated. Out-of-date tests can become noisy, producing false failures or being ignored altogether.
- **Limited real-world coverage**: Unit tests are only as strong as the scenarios you anticipate. They won’t catch unexpected edge cases in production data unless those cases are explicitly mocked.
- **Not a replacement for data tests**: Unit tests validate logic but don’t ensure data quality or freshness in real datasets. They must be complemented by dbt’s schema and data tests to provide end-to-end coverage.
- **Tooling gaps**: Compared to software engineering ecosystems, dbt’s testing framework is still maturing. Features like debugging tools, test discovery, and built-in mocking utilities are less mature, requiring workarounds or community packages.

### Quick Tutorial: Unit Testing a Model in dbt 

In this tutorial we’ll show how to add a unit test to a regular dbt model and a more complex incremental model. The instructions are adapted from the [dbt documentation](https://docs.getdbt.com/docs/build/unit-tests).

#### Unit Testing a Regular dbt Model

Suppose you create a `dim_customers` model that includes a field `is_valid_sku` to check whether a customer’s SKU is valid. The logic uses a regex pattern (for example, enforcing `^PROD-[A-Z0-9]{5,8}$)` to verify the SKU structure and joins against a table of `production_skus` to ensure that the value exists in the authoritative list of products.

Because regex checks and SKU lookups are easy to get wrong, this is a good candidate for unit testing. You can define a test with controlled inputs and expected outputs to confirm that the logic works across common edge cases: SKUs missing the PROD- prefix, SKUs that are too short or too long, or SKUs that don’t appear in the production\_skus table.

**Step 1:** A unit test for this model might look like this:

```javascript no-line-numbers
unit_tests:  
	- name: test_is_valid_sku    
    description: "Ensure SKU validation logic handles edge cases"    
    model: dim_customers    
    given:      
    	- input: ref('stg_customers')        
        rows:          
        	- {customer_id: 1, customer_name: Alice,   sku: "PROD-ABC12"}          
            - {customer_id: 2, customer_name: Bob,     sku: "PROD-FOO777"}          
            - {customer_id: 3, customer_name: Charlie, sku: "PROD-BAD"}          
            - {customer_id: 4, customer_name: Dana,    sku: "prod-ABC12"}          
            - {customer_id: 5, customer_name: Emre,    sku: "ABC12345"}          
            - {customer_id: 6, customer_name: Fay,     sku: "PROD-123456789"}          
            - {customer_id: 7, customer_name: Grace,   sku: null}      
        - input: ref('production_skus')        
        rows:          
        	- {sku: "PROD-ABC12", active: true}          
            - {sku: "PROD-XYZ789", active: true}          
            - {sku: "PROD-12345", active: true}          
            - {sku: "PROD-HELLO1", active: true}          
            - {sku: "PROD-FOO777", active: false}    
    expect:      
    	rows:        
        	- {sku: "PROD-ABC12",    is_valid_sku: true}   # exists + regex OK        
            - {sku: "PROD-FOO777",   is_valid_sku: false}  # regex OK but inactive        
            - {sku: "PROD-BAD",      is_valid_sku: false}  # too short        
            - {sku: "prod-ABC12",    is_valid_sku: false}  # lowercase prefix        
            - {sku: "ABC12345",      is_valid_sku: false}  # missing prefix        
            - {sku: "PROD-123456789",is_valid_sku: false}  # too long        
            - {sku: null,            is_valid_sku: false}  # null not allowed
```

Here, you define mock input tables for `stg_customers` and `production_skus`, then specify the expected results. This lets you confirm that only valid SKUs pass and all invalid cases are caught.

**Step 2:** Before running the unit test, you need to make sure the direct parents of your model exist in the warehouse. You can do this without incurring large compute costs by creating empty versions:

`dbt run --select "stg_customers production_skus" --empty`

**Step 3:** Then you can run the test with one of several commands, depending on how specific you want to be:

- `dbt test --select dim_customers` runs all tests on `dim_customers`.
- `dbt test --select "dim_customers,test_type:unit"` runs only unit tests for the model.
- `dbt test --select test_is_valid_sku` runs just this single test.

‍

![](https://dagster.io/images/guides/68dd42b1231439c9ab93761a-e32a3846.png)

‍

**Step 5:** If a test fails, dbt shows a diff between actual and expected results.

‍

![](https://dagster.io/images/guides/68dd42b1231439c9ab937617-6507c185.png)

By catching the bug before materializing the model in the warehouse, the unit test saved time and ensured the logic was correct. This same process can be extended to incremental models or models depending on ephemeral inputs by overriding macros, variables, or using inline SQL for fixtures.

#### Unit Testing Incremental Models

Incremental models introduce another layer of complexity because their behavior depends on whether they are run in **full refresh** or **incremental** mode. You can unit test both cases by overriding the `is_incremental` macro, and in the case of an incremental update, passing a `this` input representing the current state of the incremental model in your test configuration.

```javascript no-line-numbers
unit_tests:  
	- name: int_customers_valid_skus_full_refresh_mode    
    description: "Full refresh should load all rows with valid SKUs"    
    model: int_customers_valid_skus    
    overrides:      
    	macros:        
        	is_incremental: false    
    given:      
    	- input: ref('stg_customers')        
        rows:          
        	- {customer_id: 1, customer_name: Alice,   sku: "PROD-ABC12"}          
            - {customer_id: 2, customer_name: Bob,     sku: "PROD-XYZ789"}          
            - {customer_id: 3, customer_name: Charlie, sku: "PROD-BAD"}        # invalid (regex)      
        - input: ref('production_skus')        
        rows:          
        	- {sku: "PROD-ABC12", active: true}          
            - {sku: "PROD-XYZ789", active: true}    
    expect:      
    	rows:        
        	- {customer_id: 1, customer_name: Alice, sku: "PROD-ABC12"}        
            - {customer_id: 2, customer_name: Bob,   sku: "PROD-XYZ789"}‍
            
	- name: int_customers_valid_skus_incremental_mode    
    description: "Incremental should only insert new valid rows beyond existing max customer_id"    
    model: int_customers_valid_skus    
    overrides:      
    	macros:        
        	is_incremental: true    
    given:      
    	- input: ref('stg_customers')        
        rows:          
        	- {customer_id: 1, customer_name: Alice,   sku: "PROD-ABC12"}          
            - {customer_id: 2, customer_name: Bob,     sku: "PROD-XYZ789"}          
            - {customer_id: 3, customer_name: Charlie, sku: "PROD-HELLO1"}          
            - {customer_id: 4, customer_name: Dana,    sku: "PROD-BAD"}       # invalid (regex)      
        - input: ref('production_skus')        
       	rows:          
        	- {sku: "PROD-ABC12",  active: true}          
            - {sku: "PROD-XYZ789", active: true}          
            - {sku: "PROD-HELLO1", active: true}      # simulate existing target table rows      
        - input: this        
        rows:          
            - {customer_id: 1, customer_name: Alice, sku: "PROD-ABC12"}    
    expect:      
    	rows:        
        	- {customer_id: 2, customer_name: Bob,     sku: "PROD-XYZ789"}  # new + valid        
            - {customer_id: 3, customer_name: Charlie, sku: "PROD-HELLO1"}  # new + valid
```

‍

![](https://dagster.io/images/guides/68dd42b1231439c9ab93761d-44f9b16a.png)

In these tests, we use the `overrides.macros.is_incremental` property to to simulate whether the model is running as incremental or full refresh. The expected results match only the rows that would be newly inserted or updated during that run. This ensures your incremental logic is tested without needing a full warehouse build.

### Best Practices for Implementing Unit Testing in dbt 

Here are some useful practices to keep in mind when using dbt unit tests.

#### 1. Integrate Tests into the Development Workflow

Integrate unit tests directly into your feature development lifecycle. When adding or modifying a model, write unit tests that confirm the logic works as intended before pushing changes. This helps prevent regressions early and builds confidence that your changes won’t break downstream dependencies.

In CI environments, configure your pipelines to run unit tests using flags like `--select state:modified test_type:unit` to test only changed models. This keeps feedback loops fast without the overhead of running every test on every model. Also, consider running unit tests locally as a first step before any dbt run or full test suite.

You can also enforce unit test coverage in code reviews by requiring that all business logic changes come with corresponding tests. This ensures the testing habit becomes part of your team's culture and reduces the likelihood of missing important edge cases.

**Note:** It’s important to make sure unit tests **are not included** as part of production runs of dbt, because they can introduce overhead and interfere with production runs. They should only run as part of CI/CD processes, to ensure SQL code is ready for production.

#### 2. Isolate Logic into Modular Models

Splitting logic into modular components makes unit testing easier and your dbt project more maintainable. Each model should handle a specific part of the transformation pipeline, for example, data cleaning, filtering, joining, or enrichment.

Avoid bundling multiple business rules into a single final model. Instead, create intermediate models that represent discrete steps in the logic. For example, if your final model calculates customer lifetime value, split it into:

- A staging model for cleaned transactions
- A model that aggregates customer spend
- A final model that adds calculated metrics

Each of these can be tested independently with focused unit tests. Smaller models with narrow scopes are easier to understand, debug, and validate.

#### 3. Organize Tests Clearly

Good organization of unit tests helps teams maintain clarity as projects scale. Place unit tests in a structured directory like` tests/unit/<model_name>/` and name them based on the logic they validate. For instance, for a model `dim_customers`, you might create:

- `test_sku_validation.yml`
- `test_age_brackets.yml`
- `test_loyalty_score_calculation.yml`

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Within each YAML file, separate tests using clear `description` fields and make use of `tags` to categorize them by model or test type. This allows you to select subsets of tests for faster runs using `dbt test --select tag:customer_tests`.

Also, include comments or documentation in your test definitions explaining what each test is validating. This reduces the learning curve for new developers and helps future maintainers understand why a test exists.

#### 4. Use Seed Data for Input Mocking

Seed files offer a scalable way to mock input datasets for your unit tests. Instead of writing row-level mocks directly in your test YAML, place your inputs in CSV files under `data/` and load them into the warehouse using `dbt seed`.

This is especially useful when:

- You need to reuse the same input data across multiple tests
- Your test inputs include many columns or rows, making inline YAML verbose
- You're testing models that rely on static reference data like country codes or product catalogs

Using seeds makes test setup clearer and avoids duplication. For example, if multiple tests rely on a fixed `production-skus.csv`, you only need to define it once. You can still override or extend it in individual tests as needed.

To maintain clarity, version seed files with your models and include them in your CI pipeline setup to ensure tests have all the required context to run.

#### 5. Make Use of dbt run-operation for Test Setup and Teardown

`dbt run-operation` allows you to call custom macros before or after tests to prepare your environment. This is especially useful in automated pipelines where you need to control the state of your test schema.

For setup, you might create a macro that truncates or recreates schemas, seeds test-specific tables, or ensures certain environment variables are set. For teardown, you can clean up temporary tables or reset materialized views that could interfere with other runs.

Example macro:

```javascript no-line-numbers
-- macros/reset_test_schema.sql
{% macro reset_test_schema(schema_name) %}
{% set sql %}  
    drop schema if exists {{ schema_name }} cascade;  
    create schema {{ schema_name }} ;  
{% endset %}
{% do run_query(sql) %}
{% endmacro %}
```

You could then call this with:

`dbt run-operation reset_test_schema --args '{"schema_name": "dbt_unit_tests"}'`

Automating this setup and teardown removes manual steps and ensures consistency across local and CI runs. It also enables parallel testing by isolating tests in separate schemas when needed.

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to go beyond just SQL transformations and seamlessly connect your dbt project with your wider data platform.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and AI workflows. With a Python-native approach, it unifies SQL, Python, and more into a single testable and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## ETL Pipelines: 5 Key Components and 5 Critical Best Practices

> An ETL (extract, transform, load) pipeline is a data processing system that automates the extraction of data from various sources.

Source: https://dagster.io/guides/etl-pipelines-5-key-components-and-5-critical-best-practices

### What Is an ETL Pipeline?

An [ETL (extract, transform, load)](https://dagster.io/learn/etl) pipeline is a data processing system that automates the extraction of data from various sources, its transformation into a format suitable for analysis, and its loading into destination systems like data warehouses or data lakes. This process ensures that data is clean, consistent, and ready for use in analytics. ETL pipelines handle large volumes of data, facilitating efficient data flow.

By automating these processes, ETL pipelines reduce manual workload and minimize the errors that can occur during data handling. They enable organizations to consolidate data from disparate sources, ensuring analytical uniformity and relational coherence.

### Key Components of an ETL Pipeline

#### 1. Data Sources

Data sources are the starting point of any ETL pipeline. They encompass various systems where data can originate, including databases, CRM systems, web services, ERP systems, and external APIs. These sources are rich with structured and unstructured data necessary for informed decision-making. The diversity of data sources highlights the need for ETL systems to handle differing data formats and structures.

Accessing data from these varied sources requires robust connectivity and compatibility. ETL pipelines must interface with different data repositories efficiently. This capability ensures the pipeline can ingest data regardless of format, enabling comprehensive data analysis.

#### 2. Data Extraction

Data extraction is the initial phase in the ETL process, focusing on retrieving relevant data from various sources. The goal is to collect data without altering its original state, maintaining fidelity for later processing. Effectiveness in extraction ensures that valuable data is accessible without distortion or loss.

Multiple modes of extraction exist, from real-time data retrieval to scheduled data fetching. Choosing the right method depends on the data characteristics and business requirements. Efficiency in this phase is crucial for minimizing the performance impact on source systems and ensuring quick data availability for subsequent processing tasks within the pipeline.

#### 3. Data Transformation

Data transformation refines raw data into structured formats suitable for analysis and business intelligence. This stage includes cleaning, normalization, aggregation, and validation, ensuring data consistency and integrity. Transformation prepares data to fit target system requirements, enabling reliable and insightful analytics.

The transformation process often includes complex operations like data enrichment and schema alignment, which enhance and organize data. It involves a combination of rules, functions, scripts, and mapping techniques. These ensure that heterogeneous data sources are aligned with business objectives.

#### 4. Data Loading

Data loading is the final step in an ETL pipeline, involving the transfer of transformed data into the target system. Target systems can include data warehouses, data lakes, or operational databases. The goal is to ensure efficient data delivery without data loss or corruption.

Efficient data loading strategies, such as batch processing or real-time streaming, impact system performance and query responsiveness. The choice of strategy is influenced by the analytical needs and time constraints of the business. Once completed, the loaded data is available for analysis, visualization, and decision-making.

#### 5. Orchestration and Workflow Management

Orchestration and workflow management are pivotal to maintaining order and efficiency in an ETL pipeline. They involve sequencing and scheduling tasks, managing dependencies, and monitoring progress. These tools ensure that data flows smoothly through the pipeline, with tasks executed in the right order.

Modern orchestration solutions provide advanced functionality such as error recovery, data lineage tracking, and load balancing. They work to optimize ETL processes, giving businesses the agility to adapt to changing data needs and volumes. By automating task coordination and management, these tools reduce manual intervention, minimizing the risk of errors and delays in the data pipeline.

### Examples of ETL Pipelines

Here are a few examples of real-life usage of ETL pipelines.

#### Retail Sales Analysis

In a [retail](https://dagster.io/use-case/retail-e-commerce) context, an ETL pipeline is often utilized to simplify sales analysis by consolidating data from multiple sources such as point-of-sale systems, e-commerce platforms, and customer databases.

The **extraction** phase pulls raw sales data from these various sources. During **transformation**, the data is cleaned, formatted, and enriched to ensure consistency, such as normalizing date formats, categorizing product types, and merging customer information.

The final data is then **loaded** into a data warehouse where business intelligence tools can perform comprehensive sales analysis. This enables retailers to identify sales trends, manage inventory more effectively, and optimize marketing strategies.

#### Healthcare Reporting and Patient Data Integration

In [healthcare](https://dagster.io/use-case/life-sciences), ETL pipelines aid in integrating patient data from various systems like Electronic Health Records (EHR), laboratory databases, and billing systems. The **extraction** process involves pulling structured and unstructured data related to patient history, test results, and medical billing.

**Transformation** includes data standardization to ensure all records follow the same medical codes and formats, cleaning to remove duplicates, and aggregating data for comprehensive patient profiles. The transformed data is then **loaded** into a centralized database or data warehouse, allowing for accurate reporting, patient care analytics, and compliance with healthcare regulations.

#### Financial Services Fraud Detection

[Financial institutions](https://dagster.io/use-case/finance) use ETL pipelines to enhance fraud detection systems by integrating data from transaction records, customer databases, and third-party sources. During **extraction**, the pipeline retrieves real-time and historical transaction data.

In the **transformation** stage, the data undergoes normalization, validation, and enrichment to detect patterns and anomalies indicative of fraudulent activities. This involves creating aggregated views and calculating metrics like transaction frequency or geographic patterns.

Finally, the data is **loaded** into fraud detection platforms or analytics systems, enabling the use of machine learning models and real-time alerts to mitigate financial risks.

### Benefits of ETL Pipelines

#### Data Integration

ETL pipelines facilitate data integration by consolidating various data inputs into a singular repository. Integrating data from disparate sources ensures that businesses have a unified dataset, critical for gaining holistic insights. ETL processes reconcile inconsistencies, supporting thorough, accurate analysis and reporting, fostering data governance, and enabling resource optimization.

#### Improved Data Quality

ETL pipelines enhance data quality by embedding validation and cleaning steps within the transformation phase. By automating these processes, they ensure that data entering the analysis phase is accurate, complete, and consistent. ETL pipelines also minimize error rates and outliers, mitigating potential biases and inaccuracies in data-driven decision-making.

#### Compliance and Security

Compliance and security play a critical role in the ETL process due to increasing regulatory pressures and the presence of sensitive data. ETL pipelines typically have integrated security measures, such as encryption and access controls, to safeguard data throughout its journey. ETL pipelines also aid in compliance by maintaining data lineage and traceability features.

### Challenges in ETL Pipelines

Here are some of the key challenges organizations face when building ETL pipelines.

#### Handling Large Data Volumes

Managing large data volumes in ETL pipelines is a key challenge, necessitating strategies to sustain performance and storage. High data throughput can strain resources, demanding efficient processes like parallelization and distributed computing to maintain speed without sacrificing accuracy. Effective architecture and tools are vital for maintaining scalability as data grows.

Solutions such as partitioning and indexing help optimize performance by streamlining data access and processing. As data volumes increase, pipeline designs must continually adapt to balance processing capabilities with business needs.

#### Ensuring Data Quality and Consistency

Maintaining high data quality and consistency in ETL pipelines requires robust validation and cleaning mechanisms throughout the pipeline. As data is extracted and transformed, rigorous quality checks must be applied to ensure its accuracy and reliability. Automated data cleaning rectifies errors and enhances dataset integrity.

Consistency demands harmonizing data sources, which may feature different formats, schemas, and redundancies. ETL pipelines must align these discrepancies through normalization and standardization processes, ensuring uniformity.

#### Managing Changes in Data Sources

Dynamic data sources present challenges for ETL pipelines, necessitating timely updates to align with source changes like schema alterations or data format shifts. Ensuring pipeline flexibility to handle these variations without disruptions is critical. Data source changes must be integrated smoothly to prevent data flow interruptions.

To manage these variations, ETL solutions incorporate features such as automatic schema discovery and adaptive parsing capabilities. Regular monitoring and agile adjustment processes ensure pipelines accommodate changes effectively.

### Best Practices for Building ETL Pipelines

#### 1. Design for Scalability

Designing ETL pipelines for scalability ensures they can handle growing data volumes and complexity without loss of performance. Scalable pipelines integrate flexible resource allocation, parallel processing, and distributed computing capabilities to efficiently process increasing datasets.

To achieve scalability, ETL designs should include modular architectures that allow easy addition or modification of components. Leveraging cloud-based infrastructure expands capacity and processing power as needed. Proactive scalability strategies, combined with regular performance assessments, enable ETL systems to evolve in pace with data demands.

#### 2. Implement Robust Error Handling

Robust error handling in ETL pipelines is vital for maintaining data flow integrity and reliability. By embedding detailed error detection and correction routines, pipelines can quickly identify and rectify issues without disrupting operations. Effective error handling includes logging, alerting, and automated retries to manage exceptions and prevent data loss or corruption.

Implementing tools that monitor and report errors ensure timely response to issues, minimizing operational impact. Learning from error patterns allows for iterative improvements in detection and resolution strategies.

#### 3. Automate Testing and Validation

Automating testing and validation in ETL pipelines guarantees data accuracy and robustness before, during, and after processing. Thorough testing phases highlight anomalies and verify that transformations and data loading porcesses meet specified requirements. Automated validations streamline the process while ensuring quality.

ETL testing automation incorporates testing frameworks and tools that simulate pipeline operations under various scenarios. These tools help detect edge cases and verify data transformations against expected outcomes.

#### 4. Monitor and Optimize Performance

Monitoring performance and optimizing ETL pipelines maximizes efficiency and effectiveness. Performance metrics, such as throughput, latency, and resource utilization, provide insights into bottlenecks and areas for improvement. Continuous monitoring enables proactive adjustments, ensuring pipelines maintain speed and reliability without resource strain.

Optimization strategies, including query tuning, resource scaling, and parallel processing, enhance overall pipeline performance. By regularly assessing and refining ETL processes, organizations reduce costs and improve processing cycles.

#### 5. Ensure Security and Compliance

Ensuring security and compliance within ETL pipelines safeguards data throughout its lifecycle, meeting legal and organizational specifications. Incorporating stringent access controls, data encryption, and secure data transfer protocols mitigate unauthorized access risks. Monitoring compliance with GDPR, HIPAA, and other regulations prevents legal infractions.

Effective ETL security strategies mandate regular audits and updates to align with evolving legal requirements and threat landscapes. Documenting data flows maintains transparency, supporting both internal compliance efforts and external audit processes.

***Related content: Read our guide to ETL tools (coming soon)***

### Build Your ETL Pipeline with Dagster

As a data-centric data orchestrator, Dagster allows data teams to execute their ETL pipelines as a cohesive part of their wider data platform, enabling tighter integration between data ingestion and downstream products that depend on this data.

With a built-in toolkit for data ingestion thanks to our [Embedded ELT](https://docs.dagster.io/integrations/libraries/embedded-elt) feature and [integrations with leading providers like Fivetran](https://docs.dagster.io/integrations/libraries/fivetran), Dagster allows you to orchestrate your ETL workloads on your platform of choice.

Other features like [Dagster Pipes](https://docs.dagster.io/integrations/external-pipelines) and [Asset Checks](https://docs.dagster.io/guides/test/asset-checks) also help streamline some of the most challenging aspects of running ETL pipelines at scale, like setting up parallel processing on remote, distributed infrastructure and enforcing data quality checks at the point of ingestion.

To learn more about building ETL pipelines with Dagster, see this rundown of ELT Options in Dagster.

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---

## ETL Tools: Key Features and 10 Tools to Know in 2025

> ETL (Extract, Transform, Load) tools are software solutions that help organizations manage and process data from multiple sources.

Source: https://dagster.io/guides/etl-tools-key-features-and-10-tools-to-know-in-2025

### What Are ETL Tools?

[ETL (Extract, Transform, Load)](https://dagster.io/learn/etl) tools are software solutions that help organizations manage and process data from multiple sources. They follow a three-step process: extracting data from different systems, transforming it into a structured format, and loading it into a central data repository, such as a database or data warehouse.

In the extraction phase, ETL tools gather raw data from diverse sources such as databases, APIs, or flat files. The transformation phase involves cleaning, filtering, and reformatting this data to meet business or analytics requirements. The load phase transfers the transformed data into its destination, whether that be a data warehouse, cloud storage, or analytics platform.

[ETL tools](https://dagster.io/solutions/etl-elt-pipleines) are useful for integrating and preparing data for analysis, enabling companies to make informed decisions based on consolidated, high-quality data. They are especially important in environments where data is scattered across multiple systems and needs to be combined for unified reporting or analysis.

### Types of ETL Tools

ETL tools can be categorized based on their licensing and access models.

#### Open-Source ETL Tools

Open-source ETL tools offer comprehensive capabilities without the licensing fees of proprietary solutions. Users benefit from a collaborative development environment, where community-contributed plugins and features continuously enhance the tool's functionality. These tools also allow users to modify and extend the source code.

However, relying on community support may require technical expertise to troubleshoot and customize setups. Organizations must weigh these tools against the potential need for internal expertise or external consultants to support system customization and troubleshooting.

#### Cloud-Based ETL Tools

Cloud-based ETL tools offer significant benefits in terms of scalability, reliability, and reduced IT overhead. They enable organizations to manage large volumes of data efficiently without investing in expensive physical infrastructure.

These tools typically integrate with other cloud services, providing a simplified approach to data processing across distributed environments. Advanced solutions support real-time data streaming, a critical requirement for modern analytics solutions.

#### Enterprise ETL Tools

Enterprise ETL tools are useful for organizations handling complex, large-scale data environments. They are typically deployed on-premises, and offer features such as advanced data transformation capabilities, comprehensive data integration, and extensive support.

[Enterprise tools](https://dagster.io/enterprise) also provide scalable architecture, which can handle vast volumes of data while maintaining high performance levels. These tools also emphasize security and compliance, ensuring that sensitive data is protected across the [ETL pipeline](https://dagster.io/guides/etl-pipelines-5-key-components-and-5-critical-best-practices). The investment in enterprise ETL tools often comes with dedicated vendor support.

#### Custom ETL Solutions

Custom ETL solutions are tailored to meet organizational needs, offering precision and flexibility that standard tools may not provide. These solutions can be developed in-house or by specialized third-party vendors to address unique data processes, integration requirements, and business objectives.

Custom tools are particularly beneficial for industries with complex regulatory requirements or highly specialized data handling needs. However, developing and maintaining custom ETL solutions can be resource-intensive.

### Key Features to Consider When Choosing an ETL Tool

When evaluating ETL tools, organizations should consider the following aspects.

#### Scalability and Performance

A scalable solution can adjust to increasing data requirements without compromising speed or efficiency, crucial for businesses looking to grow. High-performance ETL tools ensure data processes are completed within desired timeframes, supporting timely analytics and decision-making.

#### Ease of Use and Learning Curve

A tool with an intuitive interface reduces the learning curve, allowing users to become proficient quickly. Modern ETL tools offer visual interfaces, extensive documentation, and user-friendly design, enabling smoother onboarding and reducing training costs. For companies without specialized IT departments, these features are particularly beneficial.

#### Integration Capabilities

An ETL tool should integrate with various data sources, including databases, applications, and legacy systems, to centralize data efficiently. Organizations should seek ETL solutions that offer extensive connectivity options, including existing data connectors, as well as API integrations and support for integrating with proprietary and legacy systems.

#### Data Transformation Functions

Data transformation functions enable raw data to be converted into actionable insights. Effective transformation includes data cleaning, formatting, aggregation, and enrichment, ensuring data meets business intelligence requirements. Tools with strong transformation capabilities can execute complex logic to prepare data efficiently for analysis. Some ETL tools provide scripting languages or advanced transformation libraries for advanced scenarios.

#### Security and Compliance

Tools must ensure data protection through strong encryption, access control, and audit trails across data processing stages. For industries like healthcare and finance, compliance with regulations such as GDPR, HIPAA, or industry-specific standards is vital. Tools should support adherence to these requirements, reducing the burden on organizations to remain compliant.

### Notable ETL Tools

#### 1. Dagster

![Dagster](https://dagster.io/images/guides/681399f654933b29e12fbe9e-dd8c0884.svg)

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle, with a declarative programming model, integrated lineage and observability, data validation checks, and best-in-class testability.

**License:** Apache-2.0 **Repo:** <https://github.com/dagster-io/dagster/> **GitHub stars:** 11K+ **Contributors:** 400+

**Key features:**

- **Data asset-centric:** Focuses on representing data pipelines in terms of the data assets (e.g., tables, machine learning models, etc.) that they generate, yielding an intuitive, declarative mechanism for building ETL pipelines.
- **Observability and monitoring:** Features a robust logging system, with built-in data catalog, asset lineage, and quality checks.
- **Cloud-native and cloud-ready:** Provides a managed offering with robust, managed infrastructure, elegant CI/CD capability, and multiple deployment options for custom infrastructure.
- **Integrations:** Extensive library of integrations with the most popular data tools, including the leading cloud providers (AWS, GCP, Azure) and ETL tools (Fivetran, Airbyte, dlt, Sling, etc.).
- **Flexible:** As a Python-based data orchestrator, Dagster affords you the full power of Python. It also lets you execute any arbitrary code in other programming languages and on external (remote) environments, while retaining Dagster’s best-in-class observability, lineage, cataloging, and debugging capabilities.
- **Declarative Automation:** This feature lets you go beyond cron-based scheduling and intelligently orchestrate your pipelines using event-driven conditions that consider the overall state of your pipeline and upstream data assets. This reduces redundant computations and ensures your data is always up-to-date based on business requirements and SLAs, instead of arbitrary time triggers.

![](https://dagster.io/images/guides/681399f654933b29e12fbe9b-df4a5318.png)

#### 2. Fivetran

![Fivetran](https://dagster.io/images/guides/681399f654933b29e12fbea1-062982c7.png)

Fivetran is a fully managed ETL and ELT platform that simplifies data integration by automating the extraction and loading of data from diverse sources into a central location. With its focus on ease of use, Fivetran eliminates the need for manual intervention in managing data pipelines.

**License:** Commercial

**Key features:**

- **Prebuilt connectors**: Provides access to over 400 prebuilt connectors for integration with various data environments. Users can also build custom connectors or use Lite connectors for specialized needs.
- **Automated schema management**: Fivetran automatically adjusts to schema changes (schema drift), keeping your data clean and organized without manual effort.
- **Fully managed pipelines**: Handles issues like source changes or connectivity problems automatically, reducing the need for internal IT involvement.
- **High-performance data movement**: Ensures fast and reliable data transfer, even during high-volume processing, minimizing latency and system impact.
- **Security and transparency**: Enhances security with encryption and provides visibility into data flow, ensuring data integrity throughout the process.

![](https://dagster.io/images/guides/681399f654933b29e12fbeaa-239f9bd2.png)

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#### 3. Matillion

![Matillion](https://dagster.io/images/guides/681399f654933b29e12fbe9f-87c8ac1d.webp)

[Matillion](https://www.matillion.com/) is a cloud-native data integration platform designed to help data teams build and manage data pipelines efficiently in modern cloud environments. It supports both code-first and visual development, enabling any source to be connected to any Snowflake, Redshift, or Databricks.

**License:** Commercial

**Key features:**

- **Cloud-native ELT**: Executes transformations in-database for scalability, performance, and cost control.
- **Flexible development options**: Offers both a visual workflow designer and support high-code ETL with Python, SQL, and dbt core.
- **Broad data connectivity**: Includes pre-built, custom, and community connectors to ingest data from virtually any source.
- **Reverse ETL**: Built-in data activation by pushing transformed data back into business tools and operational systems.
- **Virtual Data Engineering**: Supercharge all the above with Maia, the purpose-built AI data workforce.

![](https://dagster.io/images/guides/681399f654933b29e12fbea5-460ade8d.png)

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#### 4. Estuary

![](https://dagster.io/images/guides/68472f831bc4c1e81ff54236-d03769d7.png)

Estuary Flow is a real-time data integration platform designed to unify batch and streaming ETL into a single, scalable system. Built for performance and flexibility, it enables data engineers to capture, transform, and move data with millisecond latency using a combination of SQL, TypeScript, and low-code configurations.

**License:** Business Source License (BSL) + Apache 2.0 (partial) **Repo:**[ https://github.com/estuary/flow](https://github.com/estuary/flow) **GitHub stars:** 700+

**Key features:**

- **Millisecond-latency pipelines:** Estuary enables real-time ingestion and delivery of data with millisecond-level latency, making it ideal for powering operational analytics, automation, and AI use cases. Users can also configure custom batch intervals for predictable scheduling.
- **Unified streaming + batch ETL:** Unlike traditional ETL tools, Estuary Flow treats streaming and batch data as first-class citizens in the same pipeline, helping teams consolidate tooling and reduce complexity.
- **Optimized in-house connectors:** Estuary offers a growing library of production-grade, in-house connectors for major databases, APIs, and cloud platforms. These are deeply optimized for each system, reducing ingestion costs and maximizing reliability when syncing with destinations like Snowflake, BigQuery, and Databricks.
- **Schema evolution + resilience:** Pipelines are automatically resilient to schema drift, thanks to built-in schema inference and evolution. This allows seamless handling of changes in source data without downtime or manual intervention.
- **Robust transformation layer:** Transform and validate data in real time using standard SQL or statically typed TypeScript. Developers can also integrate custom logic and control pipelines programmatically using the flowctl CLI or the intuitive web UI.
- **Flexible deployments:** Estuary Flow supports fully managed SaaS, Private Cloud, and Bring Your Own Cloud (BYOC) deployments, offering data residency, compliance, and security options for enterprise use cases.
- **Transparent pricing:** Pricing is based on data volume and the number of active connector instances, with a free plan available for small-scale pipelines and evaluations.

![](https://dagster.io/images/guides/6847306cd85998b9c3c11d89-e7b9ac0a.png)

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‍

‍

#### 5. Airbyte

![Airbyte](https://dagster.io/images/guides/681399f654933b29e12fbea0-d1e36925.png)

Airbyte is an open-source ELT platform designed to handle a range of data integration needs. It simplifies the process of extracting, loading, and transforming data by offering over 400 connectors that support various sources and destinations.

**License:** MIT, ELv2 **Repo:** <https://github.com/airbytehq/airbyte> **GitHub stars:** 16K+ **Contributors:** 900+

**Key features:**

- **Self-serve data extraction**: Airbyte makes it possible to extract data from sources without writing any code and without complex integration efforts.
- **Predictable data loading**: Users can load data into multiple destinations like warehouses, lakes, and databases. Handles data typing, deduplication, and integrates seamlessly with tools like dbt, Airflow, and Dagster for streamlined data processing.
- **No-code and low-code connector builder**: Users can build custom connectors in minutes using Airbyte’s no-code or low-code Connector Development Kit (CDK), allowing non-engineers to create integrations without needing a local development environment.
- **Scalable ELT platform**: Offers flexible synchronization frequencies (from 5 minutes to 24 hours) and manages source schema changes.
- **Built-in integrations**: Extracts data from over 400 sources, including APIs, databases, and files, with high-speed data replication. Supports incremental updates and captures changes from databases, making it suitable for continuous data synchronization.

![](https://dagster.io/images/guides/681399f654933b29e12fbea7-1394079a.png)

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#### 6. Meltano

![Meltano](https://dagster.io/images/guides/681399f654933b29e12fbea8-e4e13c2b.png)

Meltano is an open-source ELT platform that allows data engineers to build, manage, and customize data pipelines with full visibility and control. Its CLI-driven approach integrates with version control, offering a transparent and testable environment for managing data workflows.

**License:** MIT **Repo:** <https://github.com/meltano/meltano> **GitHub stars:** 1K+ **Contributors:** 100+

**Key features:**

- **Source and destination support**: Offers over 600 connectors for databases, SaaS APIs, files, and custom sources, ensuring data can be extracted and loaded into any destination, including data lakes, warehouses, and vector databases.
- **Customizable connectors**: Users can build and modify connectors using the Meltano SDK, allowing for rapid creation of connectors tailored to niche or internal systems without waiting on external support.
- **Centralized data pipeline management**: Manages all data pipelines in a single place, integrating databases, SaaS apps, Python scripts, and data tools like dbt.
- **CLI-driven workflow**: Helps create, test, and manage pipelines as code through a command-line interface that integrates with version control for easy collaboration, rollback, and debugging.
- **Transparent pipeline monitoring**: Provides detailed logs and visibility into pipeline operations, allowing for quick debugging.

![](https://dagster.io/images/guides/681399f654933b29e12fbeb0-7832f2af.png)

#### 7. DLT

![DLT](https://dagster.io/images/guides/681399f654933b29e12fbea2-64e053bf.png)

DLT (Data Load Tool) is an open-source solution to simplify the ELT process by offering scalability and ease of use for data engineers and analysts. Built with a focus on simplifying ETL workflows, DLT helps users efficiently manage data pipelines, from extraction to transformation and loading into various destinations.

**License:** Apache-2.0 **Repo:** <https://github.com/dlt-hub/dlt> **GitHub stars:** 2K+ **Contributors:** 70+

**Key features**:

- Ease of use: Can be deployed anywhere Python runs, without the need to configure Docker containers or set up complex infrastructure.
- **Prebuilt connectors**: Offers a range of prebuilt connectors for common data sources and destinations, minimizing setup time and effort.
- **Code-driven workflows**: Enables users to define workflows programmatically, ensuring flexibility and control over data processing pipelines.
- **Flexible:** Native support for schema evolution and data contracts ensures your pipelines can support rapidly evolving sources with changing schemas.
- **Error handling**: Includes detailed error tracking and recovery mechanisms, reducing pipeline downtime and ensuring data integrity.

![](https://dagster.io/images/guides/681399f654933b29e12fbeab-90942428.png)

#### 8. Stitch

![Stitch](https://dagster.io/images/guides/681399f654933b29e12fbea4-abdf2801.png)

Stitch is a cloud-based ETL platform that automates the extraction and loading of data, enabling organizations to centralize their data from various sources into a single data repository. With an emphasis on ease of use, Stitch allows data teams to set up and manage data pipelines without requiring IT expertise.

**License:** Commercial

**Key features:**

- **Prebuilt data connectors**: Connects to over 140 popular data sources, including Google Analytics, Salesforce, HubSpot, and Shopify, with no coding required.
- **Automated data pipelines**: Automatically updates and maintains data pipelines, eliminating the need for ongoing maintenance and freeing teams to focus on analysis.
- **Centralized data for insights**: Consolidates data from multiple systems into a single destination, creating a reliable and analysis-ready single source of truth.
- **Fast setup**: Deploys data pipelines in minutes, helping quickly move data from source to destination.
- **Enterprise-grade security**: SOC 2 Type II certified and compliant with HIPAA, GDPR, CCPA, and ISO/IEC 27001, ensuring data security and compliance.

![](https://dagster.io/images/guides/681399f654933b29e12fbea9-d3fceef1.png)

‍

#### 9. Integrate.io

‍

![Integrate.io](https://dagster.io/images/guides/681399f654933b29e12fbea6-3ec33462.png)

Integrate.io is a low-code ETL platform designed to automate data processes, helping businesses efficiently integrate, prepare, and transform data. It allows users to automate manual tasks like file preparation, Salesforce data integration, and database replication.

**License:** Commercial

**Key features:**

- **Low-code data pipelines**: Automates complex data preparation and transformation processes through a drag-and-drop interface, eliminating the need for extensive coding or manual effort.
- **Bi-directional Salesforce integration**: Integrates and syncs Salesforce data with other systems through a bi-directional connector.
- **Automated file data preparation**: Automates the ingestion, cleaning, and normalization of file-based data for smooth integration into business workflows.
- **REST API ingestion**: Offers API ingestion capabilities, allowing users to pull data from virtually any API without technical limitations.
- **60-second CDC database replication**: Supports data products with continuous, real-time Change Data Capture (CDC) replication, ensuring up-to-the-minute data accuracy.

![](https://dagster.io/images/guides/681399f654933b29e12fbeac-fbfc4005.png)

#### 10. IBM Datastage

![IBM Datastage](https://dagster.io/images/guides/681399f654933b29e12fbea3-d6e7b20e.png)

IBM DataStage is an AI-powered data integration platform that enables businesses to design, develop, and execute ETL and ELT pipelines efficiently. Built for multi-cloud and on-premises environments, DataStage integrates seamlessly with IBM Cloud Pak for Data, allowing for high-performance data transformations, scalability, and data governance.

**License:** Commercial

**Key features:**

- **Multi-cloud and on-premises deployment**: Runs ETL/ELT jobs on any cloud or on-premises with a hybrid architecture.
- **AI-powered automation**: Leverages machine learning-assisted design to automate data pipeline development and deployment, improving productivity and reducing development costs.
- **Parallel processing engine**: Processes large volumes of data at scale with IBM’s parallel engine, optimizing workload execution and balancing resources for high throughput.
- **In-flight data quality**: Ensures data integrity by automatically resolving quality issues using IBM InfoSphere QualityStage as data moves through the pipeline.
- **Extensive connectivity**: Offers prebuilt connectors to integrate data from cloud sources and data warehouses, including IBM Db2 and Netezza, reducing the need for custom connectors.

![](https://dagster.io/images/guides/681399f654933b29e12fbeae-5dc841d0.png)

#### Bonus - Oracle Data Integrator

![Oracle](https://dagster.io/images/guides/681399f654933b29e12fbeaf-4dba284a.png)

Oracle Data Integrator (ODI) is a data integration platform that supports both ETL and ELT processes. Designed for complex data environments, ODI offers high-performance data movement, transformation, and governance while leveraging existing database resources.

**License:** Commercial

**Key features:**

- **Declarative design**: Uses declarative rules like mappings, joins, filters, and constraints to simplify development and enhance productivity.
- **ELT architecture**: Minimizes data movement by pushing transformations to the target database, eliminating the need for a middle-tier engine and reducing network traffic.
- **Knowledge modules**: Offers prebuilt and customizable templates to handle various data integration tasks such as extraction, loading, integration, and data quality management.
- **Connectivity**: Integrates data from a range of sources including databases, files, message queues, and APIs through a unified interface.
- **Change data capture (CDC)**: Automatically captures and integrates data changes in real time using triggers or log-based mechanisms, ensuring timely updates for mission-critical data.

![](https://dagster.io/images/guides/681399f654933b29e12fbead-df0c1bac.png)

### Conclusion

ETL tools play a critical role in helping organizations manage the complexity of data integration from various sources, ensuring the data is clean, consistent, and ready for analysis. By automating the extract, transform, and load processes, these tools enable businesses to make data-driven decisions more efficiently and reduce the burden of manual data handling.

As a data-centric data orchestrator, Dagster allows data teams to augment the capabilities of their ETL tools by providing a rich set of observability, cataloging, lineage, and orchestration features, connecting your ETL tools and the downstream processes that depend on them.

It also features integrations with leading providers like [Fivetran](https://docs.dagster.io/integrations/libraries/fivetran) and [Airbyte,](https://docs.dagster.io/integrations/libraries/airbyte) and a [built-in toolkit for data ingestion using the most popular open-source data integration libraries.](https://docs.dagster.io/integrations/libraries/embedded-elt)

To learn more about building ETL pipelines with Dagster, see this rundown of ELT Options in Dagster.

‍

---

## How dbt Snapshots Work, Quick Tutorial & Best Practices

> dbt snapshots are a mechanism within dbt (data build tool) designed to track and preserve changes in data over time, specifically for tables that are mutable or have Slowly Changing Dimensions (SCD) Type 2. They allow users to maintain a historical record of how rows within a table evolve.

Source: https://dagster.io/guides/how-dbt-snapshots-work-quick-tutorial-best-practices

### What is a dbt Snapshot? 

dbt snapshots are a mechanism within dbt (data build tool) designed to track and preserve changes in data over time, which implements Type 2 Slowly Changing Dimensions for mutable tables. They allow users to maintain a historical record of how individual rows within a table evolve with updates.

Unlike models, which only reflect the latest state of your data, snapshots allow you to track historical versions of data as records are inserted, updated, or deleted. Think of snapshots as a method to analyze how your data has evolved without losing prior states.

When you define a snapshot in dbt, it automatically manages the logic for identifying when a record has changed and persists new versions to a snapshot table. These versions are annotated with metadata, such as validity timeframes and change timestamps, supporting robust historical analysis. Snapshots are especially valuable in analytics workflows where audits, change tracking, or time-based reporting is necessary.

### Why Do We Need dbt Snapshots? 

Operational data systems often overwrite records when updates occur. For example, when a customer changes their phone number or address, the old data is lost unless it’s explicitly preserved. This creates challenges for teams that need to track how data has changed over time.

Snapshots solve this problem by maintaining historical versions of records. This enables use cases such as:

- Tracking changes to customer data over time
- Analyzing how user behavior or segments evolve
- Ensuring compliance with audit requirements
- Recovering from incorrect updates in source systems

‍

They are particularly useful for implementing slowly changing dimensions. While type 1 SCDs overwrite old data, type 2 SCDs retain historical versions. dbt snapshots make it straightforward to implement type 2 SCDs by automatically detecting changes and preserving previous states, eliminating the need for custom ETL logic.

### How Snapshots Work in dbt 

Snapshots in dbt follow a structured process to manage data versioning without manual intervention.

- **Initial load:** On the first snapshot run, dbt captures and writes the current state of the source table into a snapshot table. This creates a baseline version for each record.
- **Change detection:** On subsequent runs, dbt evaluates each record based on user-defined criteria (such as comparing field values or timestamps). If any relevant field has changed, dbt considers the record updated.
- **Version creation:** When a change is detected, the previous version of the record is marked with an end timestamp, and a new version is inserted with a new start timestamp. This process continues over time, building a full version history.

This setup allows teams to query the state of a record at any point in time, using the generated validity ranges. The snapshot logic abstracts the complexity of managing this historical tracking, making it easier to implement reliable data auditing and temporal analysis.

### Snapshot Strategies in dbt 

There are two main strategies you can use for dbt snapshots: timestamp and check.

#### Timestamp Strategy

The timestamp strategy is used when source records include a field that updates with every change—typically a last\_updated or modified\_at column. dbt uses this column to determine whether a record has changed since the last snapshot run.

To use this strategy, you specify the column in the snapshot configuration. dbt compares the stored value with the current value during each run. If the timestamp has advanced, dbt treats the record as updated, ends the previous version, and inserts a new version.

This strategy is efficient and avoids comparing all rows in the snapshot with the current state. However, it requires a reliable and consistently updated timestamp column in the source data. If the timestamp doesn’t change when data changes, updates may be missed.

#### Check Strategy

The check strategy works by comparing a subset of columns to detect changes. Instead of relying on a timestamp, dbt hashes the specified columns and checks whether the hash has changed since the last snapshot.

This approach is useful when no reliable update timestamp exists. You define which columns to check, and dbt automatically detects changes by comparing the current and previous hash values.

While more flexible, this strategy can be more computationally expensive, especially for wide tables or large datasets. It's important to select only the columns that are expected to change, to avoid unnecessary versioning.

### Quick Tutorial: Add dbt Snapshots to Your DAG 

This tutorial will show you how to add a snapshot to a dbt project. Instructions are adapted from the [dbt documentation](https://docs.getdbt.com/docs/build/snapshots).

**To add dbt snapshots to your DAG:**

1\. Start by creating a snapshot configuration file in the `snapshots` directory. This YAML file defines how dbt will detect and store changes to your source table over time. For example:

```javascript no-line-numbers
snapshots:  
	- name: sales_snapshot    
    relation: source('my_source', 'sales')    
    config:      
    	schema: public_snapshots      
    	database: analytics      
    	unique_key: id      
    	strategy: timestamp      
    	updated_at: updated_at      
    	dbt_valid_to_current: "timestamp '2030-12-31 00:00:00'"
```

‍

![](https://dagster.io/images/guides/68dd46b7fd368b7ffe3f58b6-d0fa0331.png)

This configuration tells dbt to monitor the `sales` source table, using the `timestamp` strategy to detect changes based on the `updated_at` column. The `unique_key` identifies each row uniquely, and `dbt_valid_to_current` sets a future date for currently valid records.

You can also preprocess data before snapshotting. If the source needs filtering or transformation, use an ephemeral model to define this logic:

```javascript no-line-numbers
-- models/ephemeral_table1.sql
{{ config(materialized='ephemeral') }}
select * from {{ source('my_source', 'sales') }}
```

2\. Then, point your snapshot to the model using `ref('ephemeral_table1')` in the `relation` field.

3\. Once configured, run the snapshot with:` dbt snapshot`

‍

![](https://dagster.io/images/guides/68dd46b7fd368b7ffe3f58b9-4a3d55ba.png)

On the first run, dbt creates the snapshot table and captures the initial state of the data, adding metadata columns like dbt\_valid\_from and dbt\_valid\_to. On subsequent runs, dbt compares new data with the existing snapshot. If changes are detected, it closes out the previous record and inserts a new version with updated timestamps.

4\. To use the snapshot in downstream models, reference it like any other model:

```javascript no-line-numbers
-- models/changed_sales.sql
select * from {{ ref('sales_snapshot') }}
```

5\. To keep snapshots useful, schedule the `dbt snapshot` command to run regularly, ensuring data changes are tracked consistently.

### Best Practices for Using dbt Snapshots 

Here are some useful tips to consider when using snapshots in dbt.

#### 1. Clearly Define Snapshot Frequencies and Schedules

Snapshot frequency should match the rate at which your source data changes and the business requirements for change tracking. For example, if you're capturing updates to customer profiles that typically occur once a week, running snapshots hourly is wasteful. Conversely, for transactional data like orders that may change multiple times a day, daily or hourly snapshots ensure no updates are missed.

Use an orchestrator (like Dagster, Airflow, or dbt Cloud) to run `dbt snapshot` at regular intervals. Schedule snapshots during low-usage periods to reduce load on the warehouse. For critical datasets, consider incrementally increasing snapshot frequency and monitor whether the additional runs yield meaningful new versions.

Also, track snapshot freshness in your observability layer. This ensures teams are alerted if the snapshot task fails and historical data is not being captured on schedule.

#### 2. Keep Snapshot Models Simple

Snapshot logic should focus solely on detecting and recording changes. Avoid embedding filters, joins, or transformation logic directly in the snapshot. Instead, create a dedicated staging or ephemeral model that prepares the data, and point the snapshot to that model using `ref()`.

For example, if you need to snapshot only active customers, define a model like `stg_active_customers` with filtering logic, and configure the snapshot to reference it with

`relation: ref('stg_active_customers')`

This separation keeps snapshots maintainable, promotes reuse, and makes it easier to test or debug each part independently. Complex snapshots are harder to audit and more likely to fail silently.

#### 3. Monitor Snapshot Performance and Storage Usage

Snapshots grow linearly with the number of changes in your data. A high-churn table will result in many historical versions being stored. Monitor row counts and table sizes regularly to understand growth patterns. You can query metadata tables (e.g., `information_schema.tables` or dbt artifacts) to track this over time.

Use partitioning, clustering, or table expiration (if supported by your warehouse) to manage growth. For example, in BigQuery, you can cluster snapshots by `unique_key` and partition by `dbt_valid_from` to improve query performance and reduce costs.

If storage becomes a concern, implement a retention policy to archive or delete versions older than a certain threshold, depending on your data governance requirements.

#### 4. Establish Robust Data Quality Checks

Snapshots rely on accurate change detection. If the `updated_at` column doesn't update correctly or the `unique_key` isn’t truly unique, the snapshot logic will fail silently and produce bad data. Add tests in your dbt project to enforce these assumptions:

- Uniqueness tests on `unique_key`
- Non-null tests on key columns and timestamps
- Schema tests to detect unexpected column changes
- Custom tests to check for time gaps or overlapping validity ranges

You can also build monitoring queries that compare the number of snapshot records per key or detect unusual versioning patterns. This helps catch edge cases like out-of-order timestamps or duplicate entries early.

#### 5. Use a Dedicated Schema for Snapshots

Place all snapshot tables in a separate schema, such as `snapshots`, to keep your warehouse organized and prevent confusion with models or source tables. This schema acts as a historical archive and allows for targeted access control.

For example, grant read-only access to analysts while restricting write access to snapshot tables. This prevents accidental data changes and ensures the integrity of historical records.

Additionally, using a dedicated schema makes it easier to manage lifecycle tasks—like truncating, backing up, or archiving snapshots—without affecting your production models or staging layers. If you need to replicate or export historical data, isolating it simplifies the process.

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to go beyond just SQL transformations and seamlessly connects your dbt project with your wider data platform.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and AI workflows. With a Python-native approach, it unifies SQL, Python, and more into a single testable and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## Ultimate Guide to dbt Macros in 2025: Syntax, Examples & Pro Tips

> dbt (data build tool) macros are reusable, parameterized SQL and Jinja templates to automate repetitive SQL operations within a dbt project.

Source: https://dagster.io/guides/ultimate-guide-to-dbt-macros-in-2025-syntax-examples-pro-tips

### What are dbt Macros? 

dbt (data build tool) macros are reusable, parameterized SQL and Jinja templates to automate repetitive SQL operations within a dbt project. By allowing developers to write logic once and invoke it multiple times with varying arguments, macros address redundancy, consistency, and scalability challenges in manual SQL scripting. 

Macros are written in Jinja, a templating language, enabling dynamic code generation for models, tests, and snapshots. Unlike typical functions in programming languages, dbt macros operate at compile time. They generate SQL code before execution, providing flexibility and efficiency. 

Here’s a basic example of a macro that formats a column as a date:

```javascript no-line-numbers
{% macro format_as_date(column_name) %}    
	CAST({{ column_name }} AS DATE)
{% endmacro %}
```

‍

![](https://dagster.io/images/guides/68dd4b33ee5d8a70320575a5-c8629a82.png)

This macro takes a single argument, column\_name, and outputs a SQL expression that casts that column to a date type. You can use it in a dbt model like this:

```javascript no-line-numbers
SELECT  
	id,  
    {{ format_as_date('event_timestamp') }} 
AS event_dateFROM raw_events
```

When compiled, this would become:

```javascript no-line-numbers
SELECT  
	id,  
    CAST(event_timestamp AS DATE) 
AS event_dateFROM raw_events
```

### How dbt Macros Improve SQL Workflows

dbt macros improve SQL workflows by reducing redundancy, increasing reusability, and improving code maintainability. Instead of repeating complex SQL logic in multiple places, a single macro can be written and called wherever that logic is needed. This reduces the risk of errors and makes it easier to manage changes; if the logic within a macro needs to be updated, it only has to be changed in one place.

dbt macros also allow for parameterization, enabling users to pass in dynamic values, making the code more adaptable to different contexts. This flexibility can simplify the development process, as the same macro can be applied to various models, tests, and snapshots with different parameters.

Macros also improve collaboration among data teams. Since macros abstract away complex SQL logic, they make the code easier to understand for new team members or external collaborators. By using macros, teams can standardize their SQL workflows and enforce best practices.

### Understanding dbt Macro Syntax 

dbt macros are written in Jinja, a templating language used to generate SQL dynamically. The essential components of dbt macro syntax include the following:

**Macro definition**

A macro is defined using the `macros` block in dbt. The syntax follows a standard Jinja function declaration, beginning with the `{% macro %}` tag and ending with `{% endmacro %}`.\
\
Example:

```javascript no-line-numbers
{% macro my_macro(arg1, arg2) %}  
	-- Macro logic here
{% endmacro %}
```

**Parameters**

Macros can accept parameters to make them flexible. These parameters are specified inside the parentheses following the macro name. You can pass variables to these parameters when calling the macro.\
\
Example:

```javascript no-line-numbers
{% macro calculate_average(column) %}  
	AVG({{ column }})
{% endmacro %}
```

![](https://dagster.io/images/guides/68dd4b33ee5d8a70320575a8-9c7c9dbf.png)

**Jinja templating**: 

Macros in dbt leverage Jinja templating for dynamic code generation. Expressions like `{{ variable }}` or `{% if condition %}` are used to embed logic, variables, and control flow directly within SQL.\
\
Example:

```javascript no-line-numbers
{% macro filter_data(table, condition) %}  
	SELECT * FROM {{ table }} 
    WHERE {{ condition }}
{% endmacro %}
```

**Calling a macro**

To invoke a macro, you use the {{ }} syntax within dbt models or other macros. When called, the macro will generate the SQL code based on its parameters and logic.\
\
Example:

```javascript no-line-numbers
{{ filter_data('orders', "status = 'shipped'") }}
```

‍

![](https://dagster.io/images/guides/68dd4b33ee5d8a70320575ab-4b47ab55.png)

**Return values**

Macros typically generate a SQL expression or statement, which is returned as a string. This output is directly embedded into the SQL being executed by dbt.

### 5 Ways to Execute dbt Macros 

While macros are often used within models, tests, and snapshots, dbt also supports executing macros in various other contexts to extend automation and orchestration capabilities across a project.

1. **Inside models, tests, and snapshots:** This is the most common usage. Macros are invoked within SQL files using the `{{ macro_name(arguments) }}` syntax. This allows the same logic to be reused across multiple models or tests.

2) **CLI execution using run-operation**: Macros that don’t fit naturally into models—such as utility functions for seeding data, cleaning up schemas, or altering database configurations—can be executed directly via the command line using the dbt run-operation command: `dbt run-operation macro_name --args '{"arg1": "value1"}'`. This is useful for tasks like dropping stale tables, managing grants, or setting up test fixtures before a run.

3. **Custom materializations and hooks:** Macros can be embedded in custom materializations and pre/post hooks to control behavior during model runs. For example, you can use a macro to apply grants after a model is built or log metadata before execution.

4) **Jinja logic in YAML and other config files:** While not direct execution, macros can be referenced inside YAML files using Jinja to dynamically generate configurations for sources, tests, or exposures.

### Common Use Cases and Examples for dbt Macros 

#### Date and Time Manipulation Macros

Handling dates and timestamps in SQL can quickly become cumbersome, especially when supporting multiple data platforms. dbt macros encapsulate common date transformations and calculations, such as truncating to month, generating date ranges, or adding time intervals. These routines are wrapped in macros to promote code reuse and reduce the likelihood of logical errors.

For example, a typical macro might generate platform-specific SQL to extract the year or month from a timestamp, guaranteeing that data models behave consistently regardless of underlying data warehouse syntax. These macros are particularly useful in analytics projects that rely heavily on date-based partitioning, filtering, or rollup aggregations.

#### Data Quality and Validation Macros

Data quality checks are essential for reliable analytics, but repeatedly writing assertions such as checking for NULLs, unique keys, or threshold violations can lead to bloated and inconsistent code. dbt macros address this by centralizing data quality and validation logic. Teams create parameterized macros that encapsulate business rules and data validation patterns.

These macros are often consumed inside dbt tests, yielding standardized data quality enforcement in every model or staging table. By including validation checks as macros, engineers prevent the propagation of bad data and reduce technical debt associated with manual test script maintenance.

#### Custom Data Type Conversions and NULL Handling

Adapting SQL for custom data type conversions and ensuring proper NULL handling often introduces platform-specific code and inconsistencies. dbt macros enable teams to encapsulate these conversions, standardizing how data types are cast or how NULL values are managed across different data warehouses. 

A single macro can handle differences in syntax and logic, such as converting text to integers, truncating decimals, or enforcing NOT NULL constraints. By centralizing these operations, macros prevent the proliferation of one-off fixes and unpredictable code patterns. As a result, data transformations are easier to audit and maintain.

### Tutorial: How to Create and Use dbt Macros in a Model 

Creating and using dbt macros can significantly improve the maintainability and scalability of your SQL codebase. In this tutorial, we’ll walk through the process of creating a simple macro and using it in your dbt project. Instructions are adapted from the [dbt documentation](https://docs.getdbt.com/docs/build/jinja-macros).

#### Step 1: Define the Macro

The first step in using dbt macros is to define one. This involves creating a new file within the macros directory of your dbt project. The macro file can contain one or more macros, each defined using the Jinja templating syntax.

Let’s create a basic macro that converts pounds (lb) to kilograms (kg):

**`File:`**` macros/lbs_to_kgs.sql`

```javascript no-line-numbers
{% macro lbs_to_kgs(column_name, scale=2) %}    
	({{ column_name }} * 0.45359237)::numeric(16, {{ scale }})
{% endmacro %}
```

In this example:

- `column_name` is the parameter passed into the macro (e.g., the column storing amounts in pounds (lbs)).
- `scale` is an optional parameter to specify the number of decimal places to retain. It defaults to 2 if not specified.

#### Step 2: Use the Macro in a Model

Once your macro is defined, you can call it from any dbt model, test, or analysis file. To use the macro, simply reference it with the {{ }} syntax. For example, let’s use the lbs\_to\_kgs macro in a dbt model to convert a weight from pounds to kilograms:

**`File:`**` models/weight_conversion.sql`

```javascript no-line-numbers
select    
	id,    
    weight_lbs,    
    {{ lbs_to_kgs('weight_lbs', 2) }} as weight_kgs
from data
```

This will be compiled into the following SQL:

```javascript no-line-numbers
select    
	id,    
    weight_lbs,    
    (weight_lbs * 0.45359237)::numeric(16, 2)as weight_kgs
from data
```

#### Step 3: Compile and Run the Model

To see the results of your macro in action, you need to compile your dbt project. If you are using dbt Cloud, simply click the “Compile” button. For dbt Core, run the following commands:

```javascript no-line-numbers
dbt compile
dbt run
```

‍

![](https://dagster.io/images/guides/68dd4b33ee5d8a70320575b5-63b183e5.png)

‍

After compilation, dbt will generate the SQL code with the macro expanded and ready to be executed in your data warehouse.

#### Step 4: Use Jinja for Conditional Logic and Loops

Macros in dbt aren’t limited to simple SQL transformations. You can use Jinja’s full set of control structures to build more complex logic into your macros. For example, you might write a macro to conditionally generate SQL based on certain criteria:

**Example macro with conditional logic:**

```javascript no-line-numbers
{% macro generate_case_statement(column_name, condition) %}    
	{% if condition == 'positive' %}        
    	CASE WHEN {{ column_name }} > 0 THEN {{ column_name }} END    
    {% elif condition == 'negative' %}        
    	CASE WHEN {{ column_name }} < 0 THEN {{ column_name }} END    
    {% else %}        
    	{{ column_name }}    
    {% endif %}
{% endmacro %}‍
```

In this example, the macro generates a CASE statement depending on whether the condition is 'positive' or 'negative'. You can call this macro with a column name and a condition to generate the appropriate SQL.

**Usage example:**

```javascript no-line-numbers
select  
	id,  
    {{ generate_case_statement('amount', 'positive') }} as positive_amount,  
    {{ generate_case_statement('amount', 'negative') }} as negative_amount
from app_data.payments
```

This will result in the following SQL:

```javascript no-line-numbers
select  
	id,  
    CASE WHEN amount > 0 THEN amount END as positive_amount,  
    CASE WHEN amount < 0 THEN amount END as negative_amount
from app_data.payments
```

#### Step 6: Test and Debug Macros

When working with macros, it's important to test them thoroughly. To sanity check your macros, it’s a good idea to compile them from the CLI, without running them yet, to see if the SQL generated is what you expect.

For more comprehensive testing, you can use dbt’s built-in data and unit testing functionality. If the macro generates invalid SQL or doesn’t behave as expected, check the compiled SQL for clues about the issue. 

When debugging, you can add print statements within the macro using Jinja’s `{% do %}` tag. This will print debugging information to the dbt logs, helping you identify any issues in your logic. For example:

```javascript no-line-numbers
{% macro debug_example(column_name) %}    
	{% do log("Debugging column: " ~ column_name, info=True) %}    
    {{ column_name }}
{% endmacro %}
```

### Become a Jinja Ninja: Pro Tips for Using dbt Macros 

Here are some useful practices to consider when working macros in dbt.

#### 1. Clearly Document Your Macros

Documentation is a key element of macro development. Each macro should be thoroughly explained to ensure that team members can easily understand its purpose and how to use it. Start by including a brief description of the macro’s functionality. Then, describe the parameters, specifying their expected data types, default values, and the role they play within the macro. For example, if a macro accepts a date parameter, clarify its expected format (e.g., 'YYYY-MM-DD').

Also, document the expected output, ensuring users understand what the macro returns—whether it's a SQL expression, a number, a string, or another data type. Include examples of how the macro should be called, providing context for real-world use cases. For example, demonstrate how the macro can be used in a dbt model, or under different conditions, if applicable.

#### 2. Limit Macro Complexity

Complex macros can quickly become a liability. The more intricate a macro becomes, the harder it is to debug, test, and maintain. A single, complex macro can obscure logic that could be more clearly separated into simpler, smaller components. It's essential to keep macros focused on a single responsibility—this is often referred to as the *Single Responsibility Principle*, a concept borrowed from software engineering.

#### 3. Test Macros in Isolation

Testing macros in isolation is vital to ensuring their correctness and avoid introducing bugs into your dbt project. While dbt's testing framework allows for testing in the context of your data models, it’s a best practice to validate macros independently before they are used within models.

To test a macro in isolation, you can create a separate model only for testing purposes or use `dbt compile` to inspect the SQL generated by the macro. This allows you to review the raw SQL output and verify that it matches your expectations. For example, if you’ve written a macro that generates a CASE statement, you should test it with different inputs to ensure it produces the correct SQL output in all scenarios.

#### 4. Leverage Community Packages

The dbt community provides a wealth of shared resources, including macros, that can help you avoid reinventing the wheel. By using well-maintained community packages, you can build on best practices that have already been tried and tested by other developers in the field. These packages often include a range of macros that address common needs such as date handling, data transformations, or building calculated metrics.

When integrating a community macro, be sure to thoroughly review the package documentation to understand its functionality and limitations. These packages may need to be modified to meet business requirements. For example, a date manipulation macro in a community package might assume a specific date format or require adjustments to align with the data pipeline's standards.

#### 5. Review and Refactor Macros Periodically

Refactoring is essential to maintaining the long-term health and performance of your codebase. Over time, certain macros may become obsolete, or more efficient methods may be introduced. Refactoring macros involves revisiting older logic and optimizing it based on new insights, platform updates, or changes in project requirements.

A good practice is to schedule regular code reviews or audits of your macros. During these sessions, focus on identifying macros that are overly complex, inefficient, or poorly documented. Ensure that all macros are still relevant to your current data needs, and look for opportunities to consolidate similar macros or remove redundant ones.

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to go beyond just SQL transformations and seamlessly connect your dbt project with your wider data platform.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and AI workflows. With a Python-native approach, it unifies SQL, Python, and more into a single testable and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## Working with dbt Seeds: Quick Tutorial & Critical Best Practices

> dbt (data build tool) seeds are static CSV files stored within your dbt project that are loaded into your analytics warehouse as database tables.

Source: https://dagster.io/guides/working-with-dbt-seeds-quick-tutorial-critical-best-practices

### What Are dbt Seeds? 

dbt (data build tool) seeds are static CSV files stored within your dbt project that are loaded into your analytics warehouse as database tables. Seeds bring small, fixed reference datasets, such as mapping tables, default configurations, or lookup values, into your data models without an ETL process. 

**Why use seeds**

By defining the data in CSV files, typically placed in the seeds/ directory of the dbt project, teams maintain version control and reproducibility with minimal complexity. Seeds are useful for incorporating non-changing, foundational data that many transformations rely on. Teams may use seeds for things like currency conversion rates, country codes, feature flags, or business-specific mappings. 

**Seeds command syntax**

The dbt seed command loads all CSV files from the seeds/ directory into your data warehouse as tables. To load only specific seed files, use the --select flag followed by the seed name. For example: dbt seed --select "country\_codes". This command loads the country\_codes.csv file into your warehouse.

### When to Use (and Avoid) Seeds

**When to Use Seeds**

Use seeds when you need small, stable datasets that don’t change frequently and need to be tightly integrated into your dbt project. Typical use cases include:

- **Reference tables** like country codes, product categories, or customer tiers
- **Configuration mappings** such as feature flags or business rules
- **Historical lookups** including static currency conversion rates or shipping zones
- **Standardized data** like time zones or ISO codes

Seeds are helpful when data needs to be present immediately upon project setup, without relying on external systems or pipelines, and when data needs to be shared across multiple development environments.

**When to Avoid Seeds**

Seeds are not suitable for dynamic, large-scale, or sensitive datasets. Avoid seeds in the following situations:

- **Large datasets**: If the file exceeds 5–10MB or includes tens of thousands of rows, dbt seed becomes inefficient and can slow down both dbt runs and Git operations. In such cases, use an elt pipeline and treat the data as a source.
- **Frequently updated data**: When the dataset is updated regularly (e.g., from external systems or user inputs), seeds require manual updates, which breaks automation and increases maintenance overhead. Automate ingestion instead.
- **Collaborative write access**: Seeds are read-only once loaded. If other systems or teams need to update the data, seeds introduce version drift. Use a centralized database or API for shared data instead.
- **Sensitive data**: CSV files don’t support row-level security or access control. If privacy is a concern, rely on your data platform’s security features and load data through governed processes.
- **Business-editable content**: If non-technical users need to update the data, Git workflows are not practical. Build user-friendly tools for data entry and sync updates into your warehouse programmatically.

### Quick Tutorial: Add dbt Seeds to Your DAG 

To add a seed to your DAG, begin by placing your CSV file in the seeds/ directory of your dbt project. For example, you might create a file called seeds/country\_codes.csv containing static reference data like country codes and names.

Next, load the seed into your warehouse by running:

```javascript no-line-numbers
dbt seed
```

This command will create a table in your target schema with the same name as the CSV file (e.g., country\_codes). You can confirm that the table was created by checking the dbt logs, which will report how many rows were inserted and how long the operation took.

To use the seed in your dbt models, reference it with the ref function, just like you would a model:

```javascript no-line-numbers
-- models/example.sql
select * from {{ ref('country_codes') }}
```

This allows downstream models to treat the seed as a normal table in your DAG. Any changes to the CSV will be picked up the next time dbt seed is run.

For configuration, you can adjust behavior in your dbt\_project.yml, such as setting column types or enabling quoting. You can also document and test seeds using YAML property files, just like models, to ensure consistency.

### Best Practices for Using dbt Seeds 

Here are some important practices to consider when working with seeds in dbt.

#### 1. Clearly Documenting Seed Data Sources

Each seed file should include metadata about its origin, intent, and ownership. This documentation lives in a schema.yml file under the seeds: section, where you can specify descriptions for the table and each column. The documentation should answer questions like:

- Where did this data come from? (e.g., manually created, exported from Salesforce, derived from internal logic)
- What does each field represent?
- Who is responsible for maintaining it?
- How frequently is the data expected to change?

Additionally, if the seed is meant to replicate an authoritative source (e.g., ISO country codes), cite that source explicitly. This ensures team members understand whether a seed is the source of truth or a convenience copy, and whether they should trust or cross-reference it.

#### 2. Maintaining Seed File Size and Scope

Seeds should remain small and focused to ensure fast project execution and easy code review. Large seed files (e.g., over 10MB or 10,000 rows) can slow down the dbt seed command, increase memory usage, and cause performance degradation during warehouse loads.

To maintain performance:

- Split large datasets into logical segments across multiple seed files if needed
- Avoid storing transactional data, logs, or other high-volume records in seeds
- Use gzip compression outside dbt only if necessary, but prefer reducing data volume instead

Additionally, scope each seed narrowly: if a seed mixes multiple unrelated domains (e.g., combining country codes and currency mappings), break it into separate files. This improves maintainability, makes it easier to apply tests and documentation, and limits rework when part of the seed changes.

#### 3. Implementing Consistent Seed Naming Conventions

Use a predictable naming scheme to make seeds easier to reference and manage in your project. Recommended conventions include:

- Use lowercase letters and underscores (e.g., feature\_flags.csv, not FeatureFlags.csv)
- Avoid ambiguous names like lookup or misc\_data
- Match naming patterns used for models and sources to reinforce consistency
- Prefer plural names for datasets (e.g., user\_segments, not user\_segment)

This helps ensure naming alignment across models, documentation, tests, and ref statements. Consistent names also simplify collaboration across teams and make version control diffs easier to understand. Avoid embedding version numbers or temporary labels in filenames—manage changes through Git history and comments instead.

#### 4. Testing Seeds Data for Freshness and Validity

Although seeds are intended to be relatively static, they can become outdated or misaligned with business logic over time. Establish a recurring review process—monthly or quarterly—to ensure seed data remains accurate and relevant.

Use dbt’s testing framework to define basic constraints like:

- Unique keys (e.g., id fields)
- Non-null values for required columns
- Valid values in enumerated fields (e.g., status must be active or inactive)

Additionally, write custom tests or macros to compare seed values against other tables or external APIs where applicable. If a seed replicates a known source, set up automated jobs or alerts to detect drift or missing updates.

#### 5. Securely Managing Sensitive Information in Seeds

dbt seeds are stored as plain CSV files, often committed to Git, which makes them unsuitable for sensitive or regulated data like PII, PHI, or access tokens. These files lack encryption, access control, and auditing capabilities.

Avoid including sensitive data such as:

- Customer names, emails, or IDs
- Financial or health records
- Internal passwords, secrets, or tokens

If you must work with such data, use a secure ingestion method outside of dbt (e.g., via Dagster, Airbyte, Fivetran, or a secure ETL job) and treat it as a source, not a seed. For less sensitive but still private data, enforce Git access controls, and consider excluding seed directories from commits using .gitignore, or loading such data only in local dev environments.

***Related content: Read our guide to dbt test (coming soon)***

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to scale beyond the simple features offered by dbt seeds and seamlessly manage the complex upstream dependencies your dbt project depends on.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and ML workflows. With a Python-native approach, it unifies SQL, Python, and more into a single, testable, and observable platform.

Head over to our docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud) to learn more about using Dagster with dbt to unlock better scheduling, lineage, and observability.


# Learning articles

## 15 Data Pipeline Examples (Including Real Life Mega Pipelines)

> A data pipeline automates the movement and transformation of data from various sources to a destination such as a data warehouse, data lake, or analytics tool.

Source: https://dagster.io/learn/data-pipelines-examples

### What Is a Data Pipeline? 

A [data pipeline](https://dagster.io/learn/data-pipeline) automates the movement and transformation of data from various sources to a destination such as a data warehouse, data lake, or analytics tool. It consists of interconnected steps including ingestion, processing, transformation, validation, and loading. Each stage manages a specific aspect of data handling, ensuring that data moves efficiently and reliably from source to target, often in both batch and real-time modes.

Data pipelines underpin modern data analytics, operations, and decision-making by ensuring that clean, consistent, and up-to-date data is readily available for consumption. Their automation reduces manual effort, minimizes errors, and helps enforce data integrity rules. The architecture and complexity of a data pipeline vary depending on factors such as data volume, velocity, variety, and end-use requirements, but every pipeline is structured to deliver data in a timely, reliable, and consistent manner to its consumers.

### Real-Life Mega Pipeline Examples

Let’s start with a few real-life examples of mega-scale data pipelines. These descriptions are based on publicly available information and accurate as of the time of this writing.

#### 1. Meta ETL Pipeline

Meta’s ETL pipeline is built to handle over 4 petabytes of data daily from its global user base. The architecture emphasizes distributed ownership, dynamic resource scaling, and strict data immutability to support real-time applications like news feed ranking, ad targeting, and infrastructure monitoring. Instead of relying on a centralized pipeline, Meta allows individual teams (e.g., Ads, Engagement) to own and process their data, enforcing schema consistency with Avro and Protobuf. 

The pipeline runs both streaming and batch jobs using tools like Apache Kafka, Spark, Flink, and Presto, while Hudi and Iceberg ensure data remains append-only with time-travel capabilities. AI systems tune resource allocation and schedule tasks based on usage patterns, optimizing performance and cost. Orchestration is handled with Airflow and MetaFlow, enabling reliable delivery for analytics, dashboards, and machine learning models.

**Data pipeline:**

1. Kafka and Scribe stream tens of billions of events (likes, ad clicks, video views) into Meta’s internal data lake built on HDFS, RocksDB, and Tao
2. Spark and Hive handle large-scale batch aggregations; Flink manages low-latency streaming analytics
3. Data is stored in an append-only format using Hudi and Iceberg for immutability and time-travel querying
4. Schema standardization is enforced across producers using Avro/Protobuf, enabling validation at ingestion
5. AI-driven autoscaling adjusts Spark and Flink clusters based on workload, scheduling non-urgent jobs during off-peak times
6. Orchestration is managed with Airflow and MetaFlow, routing data to dashboards, model training pipelines, and ad systems
7. Quality gates and lineage tracking are built in, with metadata tags on records for auditing and debugging
8. Final outputs power recommendation engines, real-time fraud detection, and infrastructure monitoring systems

#### 2. Airbnb Data Infrastructure

Airbnb’s data infrastructure is designed to support a fast-growing, data-informed organization by balancing reliability, scalability, and flexibility. The core architecture centers around two HDFS-based clusters, Gold and Silver, which isolate mission-critical processing from ad hoc workloads. Data flows into the system via Kafka (for events) and RDS snapshots via Sqoop (for relational data). 

Hive-managed tables are the backbone of data storage, with Presto enabling interactive querying and Spark supporting complex transformations and machine learning workflows. Job orchestration is handled with Airflow, and tools like Airpal allow non-engineers to query data easily. A key principle in Airbnb’s design is minimizing intermediary systems to ensure lineage transparency and reduce maintenance overhead. The team has evolved away from complex Mesos-based deployments to simpler, more maintainable EC2-based clusters, optimizing for cost, performance, and operational clarity.

**Data pipeline:**

1. Event data is streamed into Kafka; relational data is imported via RDS snapshots and Sqoop
2. Data lands in the Gold cluster for primary ETL processing, transformation, and validation
3. Processed data is replicated one-way into the Silver cluster for broader analytics access
4. Hive-managed tables store all datasets; Presto serves interactive SQL queries across both clusters
5. Airflow orchestrates ETL workflows and schedules jobs across Hive, Spark, and Presto
6. Spark is used heavily for ML workloads and data preprocessing, with support for stream processing
7. Airpal provides a web UI for employees to run SQL queries against the warehouse
8. Data is archived in S3 for long-term storage; Hive tables can point to S3 files to maintain queryability
9. The infrastructure shift to EC2 with local storage improved read/write throughput by 2–3x and reduced costs by 70%
10. Monitoring and cluster management are handled through Cloudera Manager, integrated with Chef for automation

#### 15. Spotify ETL Pipeline

Spotify’s ETL pipeline leverages a fully serverless architecture on AWS to collect, process, and visualize music metadata from the Spotify API. The system is structured around four main stages: extraction, transformation, loading, and analytics. It starts with a scheduled AWS Lambda function using the Spotipy library to pull playlist data such as track names, artist details, and album information. 

This raw data is stored in S3, where a second Lambda is triggered to transform and split it into normalized files by entity type (songs, albums, artists). AWS Glue Crawlers then catalog the structured data into tables for query via Athena. Finally, dashboards are built in Quicksight for trend analysis and visualization. The design emphasizes automation, modularity, and ease of scaling, making it a practical blueprint for music data exploration using cloud-native tools.

**Data pipeline:**

1. A scheduled AWS Lambda function (via EventBridge) calls the Spotify API using Spotipy to extract playlist metadata
2. Raw JSON data is saved to Amazon S3 under `raw_data/to_processed/`, with timestamps for versioning
3. A second Lambda function transforms the raw data into flattened records grouped by songs, albums, and artists
4. Transformed JSON files are stored in structured S3 folders: `songs_data/`, `album_data/`, and `artist_data/`
5. AWS Glue Crawlers detect schema from each folder and register tables in the Glue [Data Catalog](https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices)
6. Data is queried in Amazon Athena using SQL to analyze trends such as artist popularity and release patterns
7. Dashboards in Amazon Quicksight visualize query results, auto-updating as new data arrives
8. IAM roles and folder-level permissions ensure secure and scoped access to different stages of the pipeline

### Examples of Data Pipeline Architectures 

[Data pipeline architectures](https://dagster.io/guides/data-pipeline-architecture-5-design-patterns-with-examples) have evolved significantly over the past decade in response to shifts in data volume, infrastructure, and business needs. The following are common architecture patterns used by organizations today, each optimized for different scenarios.

**Note:** From this section onwards, we provide fictional, realistic examples to give a better idea about each architecture or use case. 

#### 4. Traditional ETL Pipelines (Hadoop Era)

In the early 2010s, data pipelines were typically built on-premises using frameworks like Hadoop. These pipelines followed a classic extract-transform-load (ETL) model. Data was first cleaned and transformed before being loaded into structured databases. Due to limited compute and storage resources, data engineers had to invest significant effort in modeling data efficiently and optimizing queries. These pipelines were tightly coupled and often hardcoded, making them less adaptable to changing needs.

**Pipeline example:**

1. Raw customer and sales data are exported nightly from on-premise OLTP databases
2. Data is transferred via Sqoop into HDFS for distributed storage
3. MapReduce jobs perform data cleansing, filtering, and joins to produce structured output
4. Hive tables are populated with transformed data and queried for business reports
5. Final datasets are exported to Excel or embedded in PDF reports for executive stakeholders

#### 5. ELT and the Modern Data Stack

With the rise of cloud-based platforms (starting around 2017), data pipeline architectures shifted toward a more flexible ELT (extract-load-transform) model. Storage and compute became decoupled, enabling data to be ingested quickly and transformed later as needed. This model supports modular and scalable architectures built from SaaS tools, which make up the modern data stack. It allows faster iteration and supports more use cases like analytics and machine learning.

**Pipeline example:**

1. Raw events are continuously extracted from SaaS applications (e.g., Salesforce, Stripe) using Fivetran
2. Data is loaded directly into a Snowflake data warehouse with minimal pre-processing
3. dbt models apply SQL-based transformations, including joins, filtering, and metric calculations
4. Cleaned and versioned tables are exposed to Looker for dashboards and ad hoc analysis
5. A scheduler like Airflow manages pipeline orchestration and dependencies

#### 6. Streaming Pipelines

To meet the demand for near-real-time insights, many organizations now run streaming pipelines in parallel with batch systems. These architectures ingest and process data continuously through a stream-collect-process-store-analyze pattern. While they enable faster decision-making and are well-suited for data science and machine learning, they often come with trade-offs such as limited opportunities for thorough data validation or modeling.

**Pipeline example:**

1. IoT devices publish telemetry data (e.g., temperature, humidity) to Apache Kafka topics
2. Kafka Streams applies real-time transformations such as unit conversions and alert detection
3. Transformed data is ingested by Apache Flink for windowed aggregations and joins
4. Results are stored in Apache Druid for sub-second querying and dashboard integration
5. Anomaly alerts trigger webhooks and notifications via integrated automation tools

#### 7. Zero ETL Pipelines

Emerging “zero ETL” architectures aim to simplify data movement by tightly integrating source systems and analytical platforms. Unlike traditional ELT pipelines, these systems clean and normalize data before it is loaded. Data often remains in the lake and is queried directly. However, these setups typically require both the transactional and analytical systems to be hosted by the same cloud provider, such as AWS (Aurora to Redshift) or Google Cloud (Bigtable to BigQuery).

**Pipeline example:**

1. Application writes transactional data to AWS Aurora in near real time
2. Aurora's integration with Amazon Redshift Serverless replicates changes automatically
3. Redshift performs lightweight transformations using views and SQL functions
4. Analysts query data directly without explicit pipeline code or staging layers
5. Monitoring and schema evolution are managed through integrated AWS services

#### 8. No-Copy Data Sharing

In contrast to pipelines that move or transform data, no-copy data sharing enables direct access to data in its original location. Pioneered by platforms like Snowflake (Secure Data Sharing) and Databricks (Delta Sharing), this architecture uses access permissions rather than replication, reducing duplication and streamlining governance.

**Pipeline example:**

1. Partner A stores customer insights in Snowflake and enables Secure Data Sharing
2. Partner B accesses shared tables via Snowflake's governance controls without data duplication
3. Shared data is joined with Partner B’s internal data for campaign performance analysis
4. Changes in Partner A’s source data reflect immediately for Partner B without refreshes
5. Audit logs ensure compliance with data sharing agreements and privacy policies

### Examples of Data Pipeline Use Cases

#### 9. eCommerce Analytics Pipeline

An e-commerce analytics pipeline typically ingests data from transactional databases, web logs, and third-party tracking systems. The pipeline extracts sales records, user activity logs, and inventory data, transforming it to ensure consistent formats and to enrich records with additional context such as product categories or user demographics. This transformed data is loaded into a centralized data warehouse for analysis, powering dashboards and reports for business intelligence.

Real-time extensions of such a pipeline might include stream processing frameworks like Apache Kafka and Spark Streaming, allowing for timely insights such as trend detection or fraud monitoring. Throughout, the pipeline must handle large data volumes efficiently and support frequent schema changes due to evolving platform features or marketing campaigns.

**Data pipeline:**

1. PostgreSQL stores transactional sales and customer activity data
2. Web logs and tracking events are collected via Segment and routed to Amazon S3
3. A batch job normalizes timestamps, categorizes SKUs, and enriches user sessions
4. Data is loaded into BigQuery via Cloud Dataflow for analysis and dashboarding
5. Alerts are triggered via email or Slack when conversion rates drop below thresholds

#### 10. Real-time IoT Data Ingestion Pipeline

A real-time IoT data ingestion pipeline collects continuous streams of sensor data from edge devices, such as smart meters, industrial equipment, or wearables. These devices transmit data points—often in JSON or binary formats—via protocols like MQTT or HTTP. The pipeline ingests the data, queues it in a messaging system like Apache Kafka, and then forwards it to stream processing engines for filtering, aggregation, and anomaly detection in near real-time.

Processed data is loaded into time-series databases or cloud data warehouses, where it can be visualized or used to trigger automated actions. Scalability is crucial, as millions of devices might be generating data concurrently, and the system needs to prioritize both speed and reliability. The architecture must also include mechanisms for device authentication, message deduplication, and ensuring data integrity to support use cases in predictive maintenance, remote monitoring, and smart automation.

**Data pipeline:**

1. Edge devices publish sensor data every few seconds using MQTT
2. A gateway forwards the data to Apache Kafka for buffering and decoupling
3. Apache Flink processes incoming streams, detecting anomalies and aggregating metrics
4. Validated results are written to InfluxDB and AWS Redshift for different analytics needs
5. Grafana dashboards show real-time equipment status, updated every 5 seconds

#### 11. Change Data Capture (CDC) Pipeline

A change data capture (CDC) pipeline efficiently monitors and replicates change events in production databases, such as inserts, updates, and deletes, without full data reloads. Specialized capture tools read database logs or subscribe to native change streams, capturing only the differences between two points in time. Captured changes are serialized and transmitted via streaming or batch jobs to downstream systems.

The pipeline then applies transformations, such as data masking or enrichment, before loading the deltas into data warehouses, search indices, or even other synchronized databases. CDC pipelines are vital for maintaining up-to-date analytics, enabling event-driven architectures, and reducing latency in data synchronization.

**Data pipeline:**

1. Debezium monitors MySQL binlogs to detect inserts, updates, and deletes
2. Kafka Connect streams the CDC events to a central topic per source table
3. A Kafka Streams app enriches the change records with metadata and user context
4. Transformed deltas are batch-loaded into Snowflake every 10 minutes
5. Metadata tables track offset positions to ensure idempotency and recovery

#### 12. Machine Learning Training Data Pipeline

A machine learning training data pipeline orchestrates the collection, preprocessing, and storage of input data specifically for ML model development. It pulls raw data from transactional sources, logs, or third-party datasets, applying transformations such as normalization, deduplication, and feature extraction. Data is then split into training, validation, and testing sets, ensuring balanced representation to avoid bias.

The pipeline typically includes automated quality checks to validate schema consistency, flag invalid records, and enforce labeling standards. This ensures that training data remains accurate and up-to-date, supporting continuous integration and deployment (CI/CD) of ML models. By automating repetitive data preparation steps and enforcing strict version control, these pipelines enable reproducible experiments and efficient model iteration cycles.

**Data pipeline:**

1. Raw logs from web and mobile apps are collected into Amazon S3 in Parquet format
2. A scheduled Spark job cleans and deduplicates user sessions
3. Feature engineering pipelines create inputs like session duration, device type, and cohort
4. Final datasets are versioned and registered in MLflow for model reproducibility
5. Training, validation, and test splits are stored in Google Cloud Storage for model training

#### 13. Healthcare Data Integration Pipeline

A healthcare data integration pipeline aggregates information from disparate sources like electronic health records (EHRs), imaging systems, lab management modules, and billing databases. It standardizes incoming data across inconsistent formats (e.g., HL7, FHIR, proprietary XML) and resolves ambiguities in patient identifiers. This is followed by normalization to ensure consistency, detection of data anomalies, and enrichment with external reference information.

Next, the pipeline securely stores harmonized patient and clinical event data in a centralized repository, streamlining advanced analytics, regulatory reporting, and care coordination. Data privacy, HIPAA compliance, and strict audit logging are prioritized at every stage. The resulting integrated datasets empower healthcare providers with holistic views of patient histories, supporting clinical decision-making, population health management, and medical research initiatives.

**Data pipeline:**

1. EHR and lab systems export data using HL7 messages into a secure staging area
2. Apache NiFi routes and parses data, converting records into FHIR-compliant JSON
3. A Spark job maps, normalizes, and joins patient and encounter data from multiple systems
4. De-identified results are stored in Google BigQuery with audit logs for traceability
5. Looker dashboards support population health studies and outcomes-based research

#### 14. Migration Pipeline (On-Premise to Cloud, Legacy to Modern)

Migration pipelines facilitate the transition of data from on-premise legacy systems to modern cloud-based platforms or upgraded databases. The process involves secure extraction of data, thorough cleansing to remove inconsistencies, and transformation to match new schema requirements. Mapping and conversion routines address differences between source and target systems, ensuring accuracy during transfer.

The pipeline orchestrates staged data loads, often with parallel validation, rollback, and reconciliation steps to ensure completeness and integrity post-migration. It can handle both one-time bulk migrations and incremental updates to sync ongoing changes until full cutover. Properly designed migration pipelines minimize downtime and mitigate risks associated with data loss or corruption.

**Data pipeline:**

1. Oracle legacy data is exported using Data Pump and stored as CSV on-premise
2. Files are encrypted and transferred to Azure Blob Storage via secure FTP
3. A custom script validates record counts and schema conformity post-upload
4. Azure Data Factory loads the data into Synapse Analytics using mapping data flows
5. Incremental sync jobs run nightly until final cutover, with rollback options enabled

#### 15. Social Media Real-Time Analytics Pipeline

A social media real-time analytics pipeline ingests vast quantities of public and private data from platforms like Twitter, Facebook, and Instagram through APIs or firehoses. The data includes posts, comments, likes, retweets, and multimedia content, arriving at high velocity and varying structure. The pipeline applies parsing, text mining, and enrichment to extract entities, topics, and sentiment from unstructured posts.

Events are further aggregated, filtered for relevance, and loaded into distributed analytics warehouses or visualization dashboards. Stream processing enables real-time trend detection, influencer identification, and alerting for breaking topics or brand risks. The architecture must ensure scalability, low-latency processing, and strict adherence to privacy policies and API rate limits.

**Data pipeline:**

1. Twitter firehose streams tweets containing brand keywords via Kafka Connect
2. NLP processing with spaCy and sentiment scoring runs in Apache Flink
3. Enriched events are written to a ClickHouse cluster for fast querying
4. A real-time dashboard shows trending topics and sentiment breakdowns by geography
5. Alerts are triggered if negative sentiment spikes beyond a threshold within one hour

### Best Practices for Building Robust Data Pipelines 

#### Start with Clear Data Requirements

Establishing clear data requirements is essential before designing any data pipeline. This means identifying all data sources, understanding the structure and quality of the incoming data, and defining the forms and uses of the final outputs. Engage stakeholders early—business analysts, data engineers, and end-users—to document what data is necessary, which attributes are critical, and the frequency or timing for delivery.

This planning phase reduces design errors and missed expectations during development. Clear requirements also help outline data privacy and compliance constraints. Rigorous upfront analysis shortens feedback cycles and paves the way for smoother scaling or feature changes.

#### Optimize for Scalability from the Beginning

Design pipelines with scalability in mind to handle data growth and new use cases without constant re-architecture. Opt for modular, loosely coupled components, and stateless processing where possible. Use cloud-native services or distributed frameworks that enable automatic scaling based on workload, so the pipeline supports both daily batch processing and sudden surges in demand.

Plan for flexible storage and compute resources, and consider the implications of storage format choices, partitioning, and data sharding. Factor in planned future integrations—like new data sources or analytics engines—so horizontal scaling, schema updates, or location migration is not disruptive.

#### Implement Strong Data Quality Checks

Quality checks must be embedded throughout the pipeline to detect anomalies, enforce schemas, and validate data integrity. Use tools for automated schema validation, constraint enforcement (such as unique keys or referential integrity), and checks for null values or invalid entries. Early detection and automated alerting minimize the propagation of errors downstream to data warehouses or business reports.

Logging and issue tracking should be rigorous, with metrics on quality failures, rejection rates, or transformation errors. Implement quarantine zones for suspicious records, and empower engineers to review, correct, and re-ingest data efficiently. Regular audits and feedback loops foster trust in downstream analytics.

#### Ensure Observability and Monitoring

Comprehensive monitoring is necessary for identifying bottlenecks, failures, or latency spikes within the pipeline. Implement observability using logs, metrics, and distributed tracing from the ingestion source to the final data sink. Use monitoring tools that provide real-time dashboards, automated alerts, and historical trend analysis to support quick diagnosis and remediation.

Beyond technical monitoring, also track pipeline SLAs and data delivery times to meet business requirements. Capturing granular lineage and processing metadata improves transparency and helps with auditing, troubleshooting, and compliance.

#### Plan for Fault Tolerance and Recovery

Design for failures by introducing redundancy, checkpointing, and recovery logic at every pipeline stage. Use distributed processing frameworks capable of restarting failed tasks, and ensure message queues provide exactly-once or at-least-once delivery guarantees. Implement data storage with versioning and rollback capabilities to prevent data loss or corruption after faults.

Automate backup, failover, and disaster recovery processes, and regularly test recovery scenarios to validate the pipeline’s resilience. Document failover procedures and ensure support teams are trained on them.

#### Secure Pipelines for Compliance and Privacy

Data pipelines must enforce security at all layers, from secure data ingestion over encrypted channels to access controls at each processing and storage stage. Implement authentication, authorization, and auditing for every user and automated system interacting with the pipeline. Apply encryption to sensitive data both in transit and at rest, and anonymize or tokenize personally identifiable information where required.

Factor in applicable regulatory standards such as GDPR, HIPAA, or CCPA, automating compliance checks and maintaining detailed records for audits. Regularly review and update security policies as new data privacy risks or legislation emerge.

### Building Your Data Pipeline with Dagster

As data pipelines grow in complexity, orchestration becomes less about running jobs on a schedule and more about managing data as a product. Teams need clear visibility into what data exists, how it is produced, and how changes or failures propagate through the system. Dagster is designed to support this shift by treating data pipelines as software systems built around the data itself.

Dagster models pipelines using data assets such as tables, files, or features, along with the logic that produces them. This makes dependencies explicit and aligns naturally with modern ELT and analytics workflows, where raw data, transformations, and downstream outputs evolve independently. Instead of thinking in terms of isolated tasks, teams reason about which data needs to be updated and why.

This approach works well across diverse architectures, including batch pipelines, streaming systems, and machine learning workflows. Dagster orchestrates across tools like dbt, Spark, and cloud warehouses without forcing them into a single execution model. It provides a unified layer for coordination while allowing each system to operate in the environment best suited to it.

Operational reliability is a core focus. Dagster provides visibility into data freshness, lineage, and failure impact, helping teams understand the state of their pipelines at any point in time. Data quality checks can be embedded directly into pipelines to prevent bad data from moving downstream, reducing the risk of incorrect analytics or degraded models.

By making backfills, reprocessing, and recovery routine rather than exceptional, Dagster helps teams adapt to change without sacrificing trust in their data. The result is a more maintainable and observable foundation for building modern data pipelines at scale.

---

## 4 Types of Data Integrity and 4 Methods to Improve Yours

> Data integrity refers to the accuracy, consistency, and completeness of data throughout its entire life cycle, ensuring it remains trustworthy and uncorrupted. It's a crucial aspect of data management and security, protecting data from human error, accidental alteration, or malicious attacks.

Source: https://dagster.io/learn/data-integrity

### What Is Data Integrity?

Data integrity refers to the accuracy, consistency, and completeness of data throughout its entire life cycle, ensuring it remains trustworthy and uncorrupted. It's a crucial aspect of data management and security, protecting data from human error, accidental alteration, or malicious attacks to maintain data's reliability for business decisions and compliance. 

Data integrity is maintained through technological safeguards, organizational policies, validation routines, and regular monitoring. Key practices include implementing strong access controls, using backup and versioning systems, and performing regular data audits to prevent data loss and corruption.

Data integrity is important for ensuring:

- **Trustworthy data:** High data integrity means the data is reliable and accurate, leading to better, more informed business decisions. 
- **Compliance:** Maintaining integrity is essential for meeting regulatory requirements in various industries, such as healthcare and finance. 
- **Reputation:** A data breach or consistent data errors can erode customer trust and damage an organization's reputation. 
- **Operational efficiency:** Reliable data ensures that systems function correctly and that operations run smoothly without disruptions caused by data inconsistencies.

This is part of a series of articles about [data quality](https://dagster.io/learn/data-quality)

### Why Data Integrity Matters 

Data integrity is foundational to reliable business operations and decision-making. Without it, organizations risk basing decisions on inaccurate or incomplete data, leading to poor outcomes, customer dissatisfaction, and regulatory non-compliance. Just as quality control ensures the reliability of physical products, data integrity ensures that information is fit for its intended use.

The consequences of poor data integrity are far-reaching. In analytics, flawed data produces misleading insights. In customer interactions, it can result in unfair treatment or misrepresentation. In compliance, it can lead to reporting errors and violations. Once bad data is operationalized, reversing the damage becomes difficult and costly.

Ensuring data integrity means preserving every data element throughout its lifecycle, without loss, truncation, or unauthorized modification. This supports valid comparisons over time, protects against inconsistent testing conditions, and upholds the reliability of historical data. Strong data integrity practices also reinforce security, with safeguards like authentication, encryption, and access control preventing unauthorized access or tampering.

Beyond internal processes, data integrity protects the privacy and trust of individuals. Mishandling personal or sensitive data, whether due to errors or breaches, can damage reputation and customer relationships. Even minor inaccuracies in first-party data can distort how users are categorized or engaged.

### Types of Data Integrity 

#### 1. Physical Integrity

Physical integrity relates to the protection of data from physical threats such as hardware malfunctions, power outages, natural disasters, or environmental degradation. It’s preserved through redundant storage systems, environmental controls, and backup strategies. Physical integrity ensures that even if a device fails or an external event occurs, the data remains accessible and uncorrupted.

Protective measures often include temperature- and humidity-controlled environments, uninterruptible power supplies, and off-site backups. By designing and maintaining resilient infrastructure, organizations can minimize the risk of physical threats compromising their data. 

#### 2. Entity Integrity

Entity integrity ensures that each row in a database table is uniquely identifiable using primary keys. It prevents the duplication of records and prohibits null values in primary key columns. This principle is essential in relational databases, where the uniqueness and existence of each entity must be guaranteed for the database to function properly.

Violations of entity integrity result in ambiguous or conflicting records, making it impossible to accurately reference or retrieve data. Ensuring entity integrity is achieved through key constraints and validation during data entry or import processes. Organizations depend on entity integrity to avoid data duplication and maintain the reliability of database relationships.

#### 3. Referential Integrity

Referential integrity deals with maintaining valid relationships between tables in a relational database. It relies on enforcing proper use of foreign keys, ensuring that relationships between parent and child tables remain logical and unbroken. If a foreign key references a non-existent record, referential integrity has been violated, which can lead to orphaned records and unreliable query results.

Enforcing referential integrity usually involves constraints that prevent deletion or modification of referenced records unless all associated references are updated or removed accordingly. This helps maintain a coherent structure, allowing databases to deliver reliable, interconnected data across different entities or modules. Organizations implementing referential integrity rules avoid broken relationships that can cause operational errors or inconsistent business logic.

#### 4. Domain Integrity

Domain integrity ensures that each column in a database contains only valid, predefined values. This is enforced through data type restrictions, rules, constraints, and validation procedures. For example, a date field should not accept numbers or strings, and categorical fields should only allow an approved set of options. These constraints prevent the entry of nonsensical or out-of-bounds data.

By maintaining domain integrity, organizations can minimize the risk of data anomalies that could otherwise propagate through applications or analytical pipelines. Domain integrity is typically enforced through database schemas, lookup tables, or code-level validations.

### Core Principles of Data Integrity 

#### 1. Accuracy

Accuracy means that data correctly represents the real-world objects, events, or values it is intended to describe. In practice, this involves capturing, processing, and maintaining data with a high degree of precision, avoiding transcription mistakes, and eliminating discrepancies between the source and stored information. Accuracy is essential for conducting reliable analytics, making effective decisions, and presenting trustworthy insights to stakeholders.

To achieve accuracy, organizations implement validation checks at entry points, standardize data collection processes, and train staff to follow precise procedures. Inaccurate data can mislead users or trigger flawed automated responses, so accuracy must be maintained at all stages of the data life cycle. 

#### 2. Consistency

Consistency ensures that data remains uniform and compatible across different systems, formats, and timeframes. It addresses the need to avoid conflicting or mismatched values in related datasets. This can be achieved by enforcing standard naming conventions, data formats, and synchronization procedures across all platforms or business units.

Inconsistent data can result in process failures, reporting errors, and corrupted databases, leading to confusion and costly manual corrections. Data consistency is often supported through transactional controls, strong governance practices, and regular synchronization checks. 

#### 3. Reliability

Reliability refers to the assurance that data will remain intact, accessible, and correct over time and through various processes. Reliable data maintains its integrity despite routine operations, migrations, or technological changes. This principle is vital for ensuring that critical business functions, analytics, and customer-facing applications work as expected.

Reliability is strengthened through redundancy, regular maintenance, failover mechanisms, and error-handling routines. Organizations monitor and test their systems periodically to identify potential weaknesses that could lead to unreliable data. Investing in reliability minimizes downtime, supports regulatory compliance, and builds user confidence in information systems.

#### 4. Completeness

Completeness means all required data is present, with no missing or unintentionally omitted elements. For datasets to be complete, every necessary attribute, record, or value must be included according to the use case or business requirement. Missing values can distort analytics, disrupt workflows, and undermine operational or regulatory outputs.

Organizations improve completeness by designing comprehensive schemas, mandatory field constraints, and routine checks for missing data. Data entry systems often enforce field-level validations to prevent incomplete submissions. Ensuring completeness allows organizations to maximize the value and usability of their information for all intended purposes.

### Data Integrity vs. Related Concepts 

#### Data Integrity vs. Data Quality

While data integrity and data quality are closely related, they are not interchangeable. **Data integrity** focuses specifically on maintaining accuracy, consistency, and reliability during data creation, storage, and handling. It enforces rules that protect data from corruption, unauthorized modification, or loss, focusing primarily on structural and technical correctness.

**Data quality** is a broader concept that considers fitness for purpose, relevance, timeliness, and usability alongside integrity. High-quality data not only maintains integrity but also meets additional contextual needs such as being up-to-date and relevant to business goals. Organizations must address both to ensure that data supports all operational and strategic requirements.

<strong><em>Learn more in our detailed guide to data quality vs data integrity (coming soon)</em></strong>

#### Data Integrity vs. Data Security

Data integrity and data security share some overlap but serve different primary objectives. **Data security** focuses on protecting data from unauthorized access, theft, or manipulation using methods like encryption, authentication, and access controls. Its central concern is confidentiality and protection against breaches or leaks.

**Data integrity** is about ensuring the correctness and trustworthiness of data, regardless of whether a breach has occurred. While security incidents can compromise integrity, it’s possible to have secure systems containing corrupted or inaccurate data if integrity processes are not enforced. Both pillars must be strong for systems to deliver trustworthy, compliant information management.

### Data Integrity Use Cases and Examples

#### Data Integrity in Healthcare

Healthcare relies on data integrity to ensure patient records, diagnoses, prescriptions, and billing information are precise and consistent. Loss of integrity can lead to critical errors, such as incorrect treatments, misdiagnoses, or fraud. Strong data integrity assists in meeting strict compliance mandates like HIPAA, protecting sensitive patient data against errors and unauthorized alterations. Measures such as electronic health records (EHR) audit logs, validation routines, and role-based access controls are used to enforce data integrity. 

**Use case example:** 

A hospital implements an integrated electronic health record (EHR) system to manage patient data across departments. During a system upgrade, improper data migration causes patient allergy information to be truncated. However, integrity checks using validation scripts detect the issue before the system goes live. The IT team restores the data from backups and patches the migration process. This integrity safeguard prevents incorrect medication from being prescribed and ensures continued compliance with HIPAA regulations.

#### Data Integrity in Finance

Financial data integrity is essential for accurate transaction processing, compliance, and reporting. Compromised data can result in financial discrepancies, regulatory violations, or loss of customer trust. Regulatory standards like SOX and PCI DSS place strict requirements on financial institutions to preserve transactional integrity, maintain audit trails, and rigorously validate data inputs.

**Use case example:** 

A bank processes thousands of transactions daily across online and branch systems. During a nightly batch operation, a power outage interrupts the update of customer account balances. Thanks to ACID-compliant transaction processing and automated recovery logs, the database detects the incomplete transaction and rolls it back. Account data remains consistent and accurate, preventing potential overdrafts or audit discrepancies and meeting SOX compliance requirements.

#### Data Integrity in eCommerce

In eCommerce, data integrity ensures accurate product listings, pricing, inventory counts, and customer order records. Discrepancies can lead to order fulfillment failures, over-selling, customer disputes, and revenue loss. Effective eCommerce platforms use real-time synchronization, strict validation routines, and frequent audits to keep product, transaction, and customer data accurate.

**Use case example:** 

An online retailer uses real-time inventory tracking integrated with its sales platform. During a peak sales event, a software bug causes some sold-out items to still appear as available. However, domain integrity rules enforce valid stock levels and trigger alerts when inconsistencies are detected. The system automatically disables the affected listings, and engineers deploy a patch. This preserves customer trust and prevents order cancellations caused by inaccurate inventory data.

### Common Risks and Threats to Data Integrity 

#### Human Error

Human error is a leading cause of compromised data integrity, manifesting in the form of incorrect data entry, accidental deletion, or misapplied updates. Even well-designed systems remain vulnerable if staff aren’t properly trained, procedures are unclear, or manual data handling is prevalent. Small mistakes can propagate quickly, corrupting large datasets or disrupting business operations.

#### Cybersecurity Attacks

Cybersecurity attacks like ransomware, malware, sql injection, or unauthorized system access can directly threaten data integrity. Attackers may modify, delete, or encrypt data, leaving it unusable or untrustworthy. Even targeted integrity attacks, such as tampering with database values or injecting fraudulent transactions, can have significant business or legal repercussions.

#### Hardware and Software Failures

Hardware failures like disk crashes, memory errors, or power supply breakdowns can result in sudden and irreversible loss or corruption of data. Likewise, buggy or outdated software may process, store, or transmit data inaccurately, leading to silent integrity violations. System incompatibilities or configuration errors may also introduce subtle, hard-to-detect data anomalies.

#### Transfer and Synchronization Errors

Errors during data transfer or synchronization between systems often result in lost, incomplete, or mismatched data. These errors may occur due to network interruptions, protocol mismatches, buffer overflows, or logic flaws in synchronization routines. Even minor inconsistencies between distributed systems can trigger major integrity problems if left unchecked.

#### Legacy Systems and Technical Debt

Legacy systems and technical debt represent significant threats to data integrity, as outdated software or hardware may not support modern standards, validation routines, or security controls. These systems can introduce obsolete formats, lack integration capabilities, or become susceptible to unpatched vulnerabilities. As organizations evolve, legacy platforms often fail to enforce consistent rules and constraints, increasing the risk of data anomalies.

### Methods and Technologies for Ensuring Data Integrity 

Here are some of the main ways that organizations evaluate data integrity.

#### 1. Checksums, Hashing, and Digital Signatures

Checksums and hashing algorithms are used to detect alterations to data during storage or transmission. A checksum is a small, fixed-size datum derived from a block of data, while a hash function produces a unique digital fingerprint. When the data is read or received, a new checksum or hash is calculated and compared to the original; any discrepancy indicates tampering or corruption.

Digital signatures build upon hashing by adding cryptographic authentication, assuring not only the integrity of the data but also its origin. These tools are essential for securing sensitive transactions, verifying downloaded files, and maintaining trusted communication channels. 

#### 2. Transaction Management and ACID Properties

Transaction management in databases ensures that operations are completed accurately and reliably. Central to this is the concept of ACID (atomicity, consistency, isolation, durability) properties. Atomicity guarantees that all steps in a transaction are completed successfully or rolled back entirely; consistency ensures rules are enforced at every transaction; isolation preserves independence between operations; and durability makes results permanent after a transaction is committed.

Implementing ACID-compliant transactions protects against data loss, partial updates, and conflicting changes, especially in high-concurrency environments. Transaction logs and rollback mechanisms further enhance data protection. 

#### 3. Backups and Disaster Recovery

Regular backups are a fundamental practice for maintaining data integrity. By creating and storing periodic copies of critical data, organizations can recover accurate information in the event of accidental deletion, corruption, or disaster. Disaster recovery plans extend this capability by orchestrating a rapid, coordinated response to major incidents, minimizing downtime and integrity loss.

Modern backup strategies involve versioning, geographically distributed storage, and automated validation to ensure restorations deliver uncorrupted data. Testing backup and recovery procedures under realistic conditions further ensures readiness when needed. 

#### 4. Role-Based Access Control and Authentication

Role-based access control (RBAC) limits data modification and access privileges to designated users based on role or responsibility. Coupled with strong authentication mechanisms, like biometrics, two-factor authentication, or cryptographic tokens, RBAC ensures only authorized actors can alter sensitive information. This minimizes intentional or accidental integrity violations by internally or externally compromised accounts.

Administrators should regularly review and update access rights to reflect personnel changes and evolving business requirements. Using audit trails in combination with RBAC further strengthens oversight. Strong access controls are often mandated by industry regulations due to their significant impact on reducing insider and outsider threats to data integrity.

### Best Practices for Maintaining Data Integrity 

Organizations should implement these practices to maintain the integrity of their data.

#### 1. Validate and Cleanse Data Regularly

Ongoing validation checks ensure that data complies with schema requirements, business rules, and external standards. Data cleansing processes correct inaccuracies, outliers, and inconsistencies by reconciling data against source-of-truth records or applying automated correction algorithms. Regular validation and cleansing reduce errors, improve decision-making accuracy, and guard against the gradual buildup of integrity issues.

These processes should be scheduled based on data volume, usage criticality, and compliance requirements. Automated tools can simplify and standardize validation routines, providing early warnings of integrity concerns. Routine cleansing and validation form the foundation of a sustainable, high-integrity data environment.

#### 2. Automate Error Detection and Correction

Manual error detection is slow and prone to oversight, so automation is essential for identifying and addressing data issues in real time. Automated systems can flag anomalies, trigger alerts, and apply rule-based corrections or quarantine affected records. These capabilities reduce the dwell time and impact of integrity violations.

Implementing real-time validation during data entry or transfer, as well as post-processing checks, enhances early detection. Machine learning algorithms can also improve detection accuracy over time by learning new error patterns. Automation minimizes disruption and maintains reliable data flows.

#### 3. Implement Strong Access and Security Controls

Data integrity benefits from robust access and security controls that restrict who can view or change sensitive data. This includes strong authentication, encryption in transit and at rest, regular review of access privileges, and prompt deactivation of dormant accounts. Access policies should be tailored to organizational roles and regularly audited for compliance.

Security controls should be consistently applied across all environments (on-premises, cloud, and hybrid deployments) to maintain a unified defense posture. Integrating security best practices with integrity controls further reduces insider and external threats. Clear policy documentation and training reinforce strong practices across the organization.

#### 4. Maintain Comprehensive Audit Trails

Detailed audit trails log every data action (creation, modification, deletion) along with user, timestamp, and contextual information. These logs support investigations, error correction, regulatory reporting, and operational monitoring. Comprehensive auditing increases transparency and accountability for all data handling activities.

Organizations can automate log analysis and generate reports for compliance or incident response purposes. Retaining logs according to retention policies helps balance forensics needs against storage costs and privacy obligations. Consistent use of audit trails makes it easier to prove data integrity and respond quickly to any anomalies.

#### 5. Integrate Data Integrity in DevOps and DataOps Workflows

Embedding data integrity controls into DevOps and DataOps pipelines ensures that integrity is maintained across code releases, data migrations, and ongoing operations. This may include automated validation tests, schema checks, data masking, and rollback procedures as part of continuous integration and deployment cycles. Early detection and prevention of integrity issues are key benefits of integration.

Cross-functional collaboration between development, operations, security, and data management teams creates shared accountability for data integrity. Continuous improvement using metrics, feedback loops, and post-incident reviews helps refine workflows. Integrating integrity throughout the [data pipeline](https://dagster.io/guides/data-pipeline) ensures new applications and processes uphold organizational standards from the outset.

### Improving Data Integrity with Dagster

Dagster provides a modern data orchestration platform that helps teams maintain data integrity throughout the entire data lifecycle, from ingestion through transformation to delivery. By embedding integrity checks directly into pipeline code and operational workflows, Dagster makes it easier to detect issues early, prevent corrupted data from spreading, and ensure that data remains accurate and consistent across systems.

Dagster applies software engineering principles to data processes, including type safety, version control, observability, and integration with CI and CD systems. These capabilities ensure that data pipelines stay maintainable, reliable, and transparent as they evolve.

Here are some of the ways organizations use Dagster to improve data integrity:

<strong>1. Enforced Schemas and Type Safety<br></strong>Dagster supports strong typing for inputs, outputs, and intermediate assets. This ensures that every step produces data in the expected format and prevents malformed or incomplete data from moving downstream. Schema checks catch issues at development time and during execution, which reduces runtime errors and silent corruption.

<strong>2. Built-in Asset Dependencies and Lineage<br></strong>Dagster tracks asset dependencies and materialization history, which allows teams to understand exactly how data was produced and which upstream sources influenced an output. Clear lineage helps teams detect where integrity issues originate and prevents the use of incomplete or stale data.

<strong>3. Automated Testing and Continuous Verification<br></strong>Dagster integrates with automated tests and validation logic so that data and pipeline code can be verified before deployment. Developers can embed custom checks into pipelines to validate assumptions, enforce business rules, and quarantine problematic records. This ensures that integrity issues are caught before they affect production systems.

<strong>4. Observability for Data and Pipelines<br></strong>Dagster provides detailed logs, event metadata, and monitoring for every step in a workflow. Teams can quickly trace errors, identify unusual behavior, and confirm that data was processed successfully. Observability makes it possible to validate the completeness, consistency, and accuracy of data as it moves through a system.

<strong>5. Safe Deployment and Version Control<br></strong>Dagster captures versions of code, assets, and configurations so that data workflows can be reproduced exactly. This supports rollback workflows, repeatable testing environments, and consistent execution across teams and environments. Version control also helps ensure reliability when pipelines are updated or refactored.

<strong>6. Integration with DataOps and DevOps Practices<br></strong>Dagster fits naturally into DataOps and DevOps practices through support for CI and CD systems, isolated development environments, and infrastructure-as-code. By integrating validation and integrity checks into existing development processes, organizations reduce the risk of introducing errors during updates or migrations.

<strong>7. Alerts and Automated Failure Handling<br></strong>Dagster can send alerts when validation checks fail or when anomalies are detected. Automated retries, partition awareness, and targeted reprocessing allow teams to respond quickly with minimal disruption. These features reduce the risk of incomplete or inconsistent data reaching consumers.

Using Dagster, organizations can maintain high levels of accuracy, consistency, reliability, and completeness across their data pipelines. This strengthens data integrity at every stage and provides a foundation for trusted analytics, regulatory compliance, and reliable business operations.

---

## 6 Benefits of a Modern Data Pipeline & How to Build One

> A data pipeline automates the transfer of data between systems and its subsequent processing.

Source: https://dagster.io/learn/data-pipeline

### What Is a Data Pipeline?

A data pipeline automates the transfer of data between systems and its subsequent processing. It integrates various sources, processes the data, and delivers it to destinations like databases or analytics tools. Traditionally, this involved extracting raw data, transforming it into a usable format, and loading it into a system for analysis or further processing, known as the ETL process. Many pipelines use more modern patterns, suitable for big data and cloud environments, such as extract-load-transfer (ELT).

Data pipelines are integral for efficient data handling in any organization dealing with large quantities of data. They enable data to be processed in real time or in batches, depending on the requirements. This infrastructure allows businesses to maintain data integrity and consistency across different systems.

### Benefits of Modern Data Pipelines

#### 1. Scalability

Data pipelines handle expanding data volumes by scaling to meet increasing demands. As organizations collect more data from a growing number of sources, pipelines can scale horizontally by distributing tasks across multiple servers or scale vertically by utilizing more computing resources. Scalability also allows organizations to handle seasonal or temporary spikes in data volume.

#### 2. Cost Efficiency

By automating data processes, pipelines streamline data movement, processing, and transformation without requiring constant manual oversight, thereby [reducing labor costs](https://dagster.io/platform-overview/cost-insights). Additionally, pipelines enable efficient resource allocation by scheduling processing tasks to avoid unnecessary compute time. In cloud environments, data pipelines dynamically scale to match workload demands, meaning organizations only pay for the resources they need at any given time.

#### 3. Developer Productivity

Data pipelines improve developer productivity by offering structured, reusable frameworks for data ingestion, transformation, and delivery. Automated ETL steps eliminate repetitive tasks, allowing developers to focus on more complex work rather than manually managing data flows. Pipelines often include pre-built connectors and integrations with popular data sources and destinations, reducing development time for new projects.

#### 4. Real-time Insights

Modern data pipelines enable real-time or near-real-time data processing, allowing organizations to access current data for immediate insights. This capability is essential for industries like [finance](https://dagster.io/use-case/finance), [retail](https://dagster.io/use-case/retail-e-commerce), and [eCommerce](https://dagster.io/use-case/retail-e-commerce), where understanding real-time customer behavior or market trends can drive impactful decisions. Real-time data access also allows for responsive operational adjustments.

#### 5. Data Quality

Data pipelines incorporate data quality checks and validation mechanisms to ensure that the data entering the system is clean, consistent, and reliable. These checks can include deduplication, type validation, normalization, and rule-based transformations to catch and correct errors early in the process. Consistent [quality control](https://dagster.io/platform-overview/data-quality) across the data lifecycle leads to higher-quality datasets for analytics, reporting, and machine learning.

#### 6. Observability

Observability in data pipelines refers to the ability to monitor and understand data flows, transformations, and system performance at every stage. Observability includes tools for logging, metrics, and alerts that help detect anomalies, bottlenecks, or failures in real-time. This level of transparency enables data teams to track data lineage, understand how data changes over time, and quickly identify the source of errors or latency issues.

### How Does a Data Pipeline Architecture Work? Key Components

#### Data Sources

Data sources are the entry points where raw data originates. They can include databases, APIs, file systems, and IoT devices. In a data pipeline, identifying and connecting to these sources is the first crucial step. It involves setting up connectors to systematically pull data, ensuring the incoming data is captured accurately and timely.

Managing diverse data sources can be complex, especially when dealing with unstructured or semi-structured data. An effective data pipeline architecture accommodates various data formats and conditions, facilitating smooth ingestion into the pipeline.

#### Data Ingestion

Data ingestion involves capturing and Ingestion mechanisms must be robust and scalable, handling varying data volumes while maintaining performance. This stage often involves data transformation, ensuring that incoming data conforms to the system's schema requirements.

#### Data Processing

Data processing involves transforming raw data into a usable format. This stage may include filtering, sorting, aggregating, or enriching the data as required. It often involves processing frameworks such as Apache Spark or Beam to handle complex transformations efficiently and at scale.

Efficient data processing is critical for extracting meaningful insights and maintaining computational efficiency. Processing engines ensure that large volumes of data are handled swiftly, optimizing system resources and reducing latency.

#### Data Storage

Data storage involves saving processed data in a format accessible for analysis or reporting. Choosing the right storage solution depends on the data's structure, volume, and access requirements. Common options include data lakes, databases, and cloud storage services offering scalability and reliability.

Effective data storage optimizes retrieval performance, security, and cost-efficiency. It handles metadata management and indexing to ensure data is easily discoverable and usable.

#### Data Consumption

Data consumption refers to how end-users or applications access and use stored data. This involves delivering data to visualization tools, dashboards, or machine learning models. It's essential that the consumption process is streamlined, providing users with timely access to the data they need for analysis and decision-making.

Data consumption often involves APIs, SQL-based query languages, or non-relational data formats, depending on the needs of the target system. Efficient pipelines manage data delivery that supports batch and real-time use cases, tailoring outputs as necessary.

#### Data Management and Governance

Data management and governance involve policies and practices ensuring data quality, security, and compliance. Effective governance frameworks establish rules for data usage, access controls, and data lifecycle management. These practices ensure data integrity and enhance user trust in the data pipeline outputs.

Strong governance mitigates risks of data breaches and ensures compliance with regulatory standards like GDPR or HIPAA. It involves collaboration between IT and business units to align data practices with strategic goals.

#### Orchestration, Scheduling, and Event-Driven Automation

[Orchestration](https://dagster.io/platform-overview/data-orchestration), scheduling, and event-driven automation are essential components for managing the flow and timing of data tasks in a pipeline. Orchestration tools like Dagster help coordinate data workflows by defining task dependencies, sequencing actions, and managing resource allocation to optimize performance. These tools ensure that each step of the pipeline — from ingestion to processing to storage — runs in the correct order and at the appropriate time.

Scheduling allows teams to automate data tasks at specific intervals, such as hourly, daily, or weekly, supporting batch processing or periodic data refreshes. Event-driven automation triggers tasks in response to specific events, like new data arrivals or file updates, which enables real-time processing.

### Common Types of Data Pipelines

#### Batch Processing

Batch processing involves handling and processing data in large volumes at scheduled intervals. This approach is ideal when real-time data processing is unnecessary, significantly reducing system resource requirements. Batch processing can be managed efficiently using frameworks like Apache Spark, which process large datasets across distributed systems.

Batch pipelines are used in scenarios like end-of-day reporting or monthly data aggregation, where delays are acceptable and data is gathered over a period.

#### Streaming Data

Streaming data processing handles data in real-time as it arrives from sources like IoT devices or social media feeds. This type of pipeline is crucial for applications requiring immediate data influence, such as live analytics or fraud detection. It leverages tools like Apache Kafka or Apache Flink to process data continuously as it enters the pipeline.

Managing streaming data requires architectures that can handle bursts of high-velocity data without cascading failures. Organizations benefit from timely insights, enabling decisions based on current data rather than historical records.

#### Data Integration Pipelines

Data integration pipelines aim to consolidate data from disparate sources into a cohesive dataset. This process often involves data cleaning, transformation, and unification to ensure compatibility and utility. Integration pipelines are essential in organizations where data silos exist, thereby enabling holistic analysis and insights.

These pipelines employ ETL tools or data integration platforms that streamline data merging processes. Efficient integration ensures that organizations can leverage data from multiple sources, enhancing insights and analysis.

<strong><em>Learn more in our detailed guide to data pipeline frameworks (coming soon)</em></strong>

### How Cloud-native Infrastructure Has Changed Data Pipelines

Cloud-native infrastructure has transformed data pipelines by introducing flexibility, scalability, and cost-efficiency that traditional on-premises systems struggle to match. Unlike fixed-capacity systems, cloud-native pipelines operate on scalable infrastructure provided by management platforms like Kubernetes and cloud providers like AWS, Google Cloud, and Azure. These platforms allow pipelines to automatically adjust resources based on demand, ensuring efficient performance during high data volumes without incurring unnecessary costs during low usage.

One of the most impactful changes is the ability to leverage managed services, such as fully managed databases, serverless functions, and automated machine learning tools. This reduces the need for specialized infrastructure management and accelerates deployment, enabling faster iterations and upgrades in data workflows. Cloud-native architectures also enable seamless integration with other cloud services, such as machine learning and data warehousing tools, creating a unified environment for end-to-end data processing and analytics.

Additionally, cloud-native infrastructure supports hybrid and multi-cloud setups, allowing organizations to maintain data pipelines across multiple cloud providers or between on-premises and cloud environments. This flexibility enhances resilience, mitigates vendor lock-in risks, and provides more control over data locality and compliance.

### Key Use Cases and Examples of Data Pipelines

#### Exploratory Data Analysis

Exploratory data analysis (EDA) involves sifting through data to identify patterns, trends, and anomalies, often as a preliminary step in data-driven projects. Data pipelines facilitate EDA by automating data collection and cleaning, ensuring analysts work with reliable datasets. EDA helps in refining strategies, informing product developments, and uncovering actionable insights within datasets.

Pipelines enable repeated EDA tasks without manual redundancy, automating data aggregation and pre-processing stages. By standardizing data preparation, teams ensure consistency across analyses.

**Example**A large eCommerce company uses a data pipeline to conduct exploratory data analysis (EDA) on customer transaction data, clickstream logs, and product reviews. Data sources include a MySQL database for transaction records, a Kafka stream for clickstream data, and an S3 bucket storing review data.

The pipeline ingests this data, performing deduplication and filtering to focus on specific time ranges and product categories. Next, data is aggregated by key metrics (e.g., average spend, most-viewed products) and stored in a data lake. Analysts then access the prepared dataset in the data lake, using it to perform EDA that informs marketing and product development strategies.

#### Data Visualizations

Data visualizations transform complex datasets into graphical representations for easy interpretation. Effective data pipelines feed these visualization tools with clean, structured data, ensuring accuracy and timeliness.

They convert raw data into charts, graphs, and dashboards, supporting real-time updates and interactive exploration. By ensuring continuous data flow to visualization platforms, businesses can monitor key metrics and KPIs efficiently.

**Example**A financial analytics company, has a pipeline to generate data visualizations that track real-time stock market data and user portfolio performance. The pipeline pulls data from APIs providing live stock prices, user data from a PostgreSQL database, and financial news from an external data feed.

This data is then processed, cleaned, and aggregated by stock, industry sector, and region. Finally, the processed data is sent to a visualization tool, enabling the company to create dashboards for users, displaying portfolio performance, stock trends, and market news, updated in near real-time to support user decision-making.

#### Machine Learning

Data pipelines support [machine learning](https://dagster.io/solutions/ai) by automating data collection, preparation, and feeding models with requisite data. They streamline ML workflows, reducing time from data acquisition to model deployment. Pipelines ensure that machine learning models receive consistent, high-quality data, critical for training and prediction tasks.

These systems provide scalable data inputs required for iterative training and validation phases, enhancing model accuracy. By integrating with ML frameworks, data pipelines drive efficient learning cycles and reduce bottlenecks in ML operations.

**Example**A healthcare provider has implemented a data pipeline to support its ML models for patient readmission prediction. Data is sourced from electronic health records (EHRs), patient admission data from a SQL database, and real-time monitoring data from wearable devices.

The pipeline ingests this data, anonymizes it for privacy, and applies transformations like feature engineering to derive useful indicators (e.g., average heart rate, length of previous hospital stays). Processed data is then fed into ML models, which predict readmission likelihood. These predictions are delivered to clinicians via a dashboard, aiding in early intervention and better patient management.

#### Data Observability

Many organizations implement data pipelines with the objective of improving data observability. In this scenario, the data pipeline enables monitoring and analyzing data metrics, identifying issues before they impact data outputs. Observability tools integrated into pipelines detect anomalies in data flow or quality, supporting data governance.

By implementing observability practices, organizations maintain trust in their data systems, ensuring timely interventions when anomalies occur. This proactive approach minimizes data downtime and errors.

**Example**A data compliance firm uses a data pipeline focused on data observability to monitor and audit data flow from client systems. The pipeline ingests data from multiple client databases and logs, capturing data lineage and tracking transformation steps.

Observability tools within the pipeline generate alerts for anomalies such as missing data, format inconsistencies, or unusual latency in data transfer. The data pipeline logs these issues, enabling the company to notify clients of data quality issues and provide compliance reports to meet regulatory standards, ensuring data consistency and integrity across client operations.

### Data Pipeline vs. ETL Pipelines vs. ELT Pipelines

**A data pipeline** is a broad term that refers to any set of processes that automate the movement and transformation of data from one system to another. This includes gathering data from sources, processing it in various stages, and delivering it to destinations such as databases, data lakes, or analytics tools. Data pipelines can handle real-time (streaming) or batch data, and they may perform any combination of [ELT and ETL steps](https://dagster.io/solutions/etl-elt-pipleines), depending on the needs of the workflow.

**ETL pipelines** specifically refer to pipelines that follow the extract, transform, load pattern. Here, data is first extracted from source systems, then transformed (cleaned, structured, and modified as needed), and finally loaded into a destination system, such as a data warehouse. ETL is well-suited for scenarios where complex data transformations are required before data is stored, ensuring data quality and consistency upfront.

**ELT pipelines** follow a different pattern: extract, load, and transform. In this approach, data is extracted from source systems and directly loaded into a storage destination, often a scalable data lake or cloud-based warehouse. Once the data is stored, transformations are applied as needed. ELT pipelines are particularly efficient in cloud-native environments where powerful data warehousing tools can handle transformations at scale.

### How Do You Create a Data Pipeline?

#### 1. Define Your Objectives

Before starting to build a data pipeline, it’s essential to clearly define its objectives. This involves identifying the technical goals and the business requirements the pipeline must meet. Begin by determining what kind of data will be handled — structured, unstructured, or both — and what insights or outputs the pipeline needs to produce.

Consider if you are aiming to process data in real time to support dynamic decision-making, or leveraging batch processing for historical analysis. It is also important to consider data quality requirements, latency, and throughput as these factors will influence the choice of tools and the overall architecture.

#### 2. Select Your Tools and Technologies

Selecting the right tools and technologies is a critical step in the development of a data pipeline. The tools you choose will depend largely on your objectives and the scale of your pipeline. For example, if you need to process streaming data in real-time, you might choose a tool like Apache Kafka, which is designed for high-throughput, fault-tolerant real-time data feeds. If your pipeline is more focused on processing large datasets in batch mode, then Apache Spark or Hadoop might be more appropriate due to their ability to handle distributed processing.

Cloud services also play a major role in modern pipelines, offering pre-built integrations and scalability features. Many data pipelines are built on a cloud platform like Amazon Web Services or Microsoft Azure, leveraging the provider’s storage and compute services and relevant data services.

Data orchestration tools like Dagster play a crucial role in complex data pipelines. They manage and coordinate the flow of data through different stages of the pipeline, ensuring smooth execution of tasks from data ingestion to consumption. This includes defining task dependencies, handling task sequencing, and allocating resources efficiently.

#### 3. Design Your Pipeline Architecture

Designing the pipeline architecture is a crucial step that determines how data will flow from source to destination. A well-thought-out architecture breaks down the pipeline into various stages, such as data ingestion, processing, transformation, storage, and consumption.

The architecture should be flexible enough to handle future scalability needs and be resilient to failures. A key decision at this stage is whether to design for batch processing or real-time streaming:

- **For batch processing**, the architecture might involve periodic extraction of large datasets from databases, transforming them using a tool like Apache Spark, and loading the results into a data warehouse.
- **A real-time streaming architecture** will involve continuously ingesting data from sources like IoT devices, social media feeds, or event logs and processing it in real time using a tool like Apache Flink or Kafka Streams.

Some organizations opt for a hybrid model that uses batch processing alongside real-time streaming for different use cases.

Another important design aspect is fault tolerance — ensuring that data isn’t lost or corrupted in case of system failure. This can be achieved by designing for redundancy and using technologies that offer built-in fault tolerance. Finally, your architecture should account for data governance, ensuring that data is handled in compliance with relevant regulations and internal policies.

#### 4. Implement Data Processing Logic

Once the architecture is in place, the next step is to implement the data processing logic. This is the phase where you develop the code or configure tools that will perform the necessary transformations on the data. The processing logic will depend heavily on the type of data and the pipeline’s objectives. For example, raw data may need to be filtered, sorted, aggregated, or enriched before it can be stored or analyzed. This step could involve writing SQL queries for relational databases, configuring transformations in a tool like Apache Beam, or developing custom processing scripts in Python or Java to handle more complex transformations.

It’s important to implement processing in a way that maximizes efficiency and minimizes resource usage. For large-scale data, distributed processing frameworks like Apache Spark can handle complex transformations at scale by distributing workloads across multiple nodes. Additionally, ensure that data integrity is maintained throughout the pipeline. Error handling mechanisms should be implemented to catch and resolve issues like missing data, outliers, or formatting errors during the processing stage.

#### 5. Test Your Pipeline

Testing is one of the most critical phases in the development of a data pipeline. It ensures that all components work together seamlessly and that the pipeline can handle different data loads and edge cases without failing:

- **Begin by conducting unit tests** for each stage of the pipeline, such as data ingestion, processing, and storage. Each component should function correctly on its own before integrating it into the larger system. For example, test whether the ingestion process correctly captures data from all designated sources, whether data transformations are accurate, and whether the storage solution is properly indexing and storing data for later retrieval.
- **Run integration tests** to ensure that they work together as expected. Stress tests should also be conducted to assess how the pipeline performs under different conditions, such as increased data volume or high-velocity streaming data. Simulate high loads to identify any bottlenecks and optimize resource allocation accordingly.
- **Validate data quality** by testing the pipeline for its ability to handle dirty data, missing values, and other anomalies without compromising the final dataset’s accuracy.

### Orchestrating Your Data Pipelines with Dagster

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle.

It features a declarative programming model, representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for defining complex data architectures.

With a modular design and rich feature set, Dagster serves as the unified control plane for data processes across the organization, with native support for lineage and observability, an asset catalog, data validation checks, and best-in-class testability.

Thanks to intuitive, composable integrations with popular data tools, native support for executing non-Python workflows in remote environments, declarative automation, and intuitive partitioning, Dagster lets you seamlessly centralize and scale your data processes.

[Learn more about the Dagster platform here](https://dagster.io/platform-overview).

‍

---

## 7 Components of a Modern Data Platform, Challenges & Best Practices

> A modern data platform is an ecosystem for managing data across its lifecycle. It integrates technologies for ingestion, storage, processing, and analysis to simplify data handling.

Source: https://dagster.io/learn/modern-data-platform

### What Is a Modern Data Platform? 

A modern data platform is an ecosystem for managing data across its lifecycle. It integrates technologies for ingestion, storage, processing, and analysis to simplify data handling. The platform emphasizes scalability, flexibility, and real-time processing, addressing the need for instant access to insights. 

This system harmonizes with evolving architectures, such as cloud-native solutions, ensuring organizations can adapt swiftly to changing data requirements and volumes. In addition to supporting various data types, a modern data platform provides governance and compliance features.

### Core Components of a Modern Data Platform 

Modern data platforms typically include the following components.

#### 1. Data Sources and Ingestion

Data sources for modern platforms range from traditional databases to web services and IoT devices. Ingestion systems capture raw, structured, and unstructured data, supporting diverse data formats. This stage focuses on efficiently and accurately inputting data to ensure reliability for subsequent processing. 

Ingestion involves ETL (extract, transform, load) or ELT (extract, load, transform) processes. These methods ensure data is optimally prepared for analysis by cleansing and organizing it into a usable state. Automation tools in data ingestion minimize manual intervention, reducing errors, increasing efficiency, and enabling continuous data flow.

#### 2. Data Storage and Processing

Data storage in modern platforms involves scalable, versatile solutions like cloud-based storage and data lakes. These solutions support vast data volumes from diverse sources, enabling easy access and management for processing needs. Secure storage options further ensure data privacy and protection.

Processing data efficiently entails leveraging distributed computing and parallel processing techniques. This approach accelerates complex computations, making it possible to handle large datasets swiftly. Modern platforms often integrate with big data frameworks like Hadoop or Spark, providing the ability to run efficient processing jobs.

#### 3. Data Transformation and Modeling

Data transformation converts raw data into meaningful formats, supporting analytical purposes. This process involves cleaning, aggregating, and enriching data to optimize its quality for decision-making. Effective transformation practices prioritize maintaining consistent data quality.

Modeling translates processed data into structures that enable faster querying and analysis. Dimensional models, for instance, simplify complex data relationships, offering a more intuitive view for analytical purposes. Data modeling is essential to preparing data for BI (business intelligence) tools and machine learning algorithms.

#### 4. Data Analytics and Visualization

Data analytics aids in extracting insights, using statistical and analytical tools to uncover patterns and trends. Analytics platforms should support various analytical models, from descriptive to predictive.

Visualization tools interpret complex data through easy-to-understand graphics and dashboards. They play a critical role in data platforms by making insights accessible to non-technical stakeholders. Interactive dashboards improve data exploration, enabling users to drill down into specifics without technical expertise.

#### 5. Data Governance and Compliance

Data governance establishes policies and procedures for secure, compliant data management. It ensures data integrity, focusing on data quality, availability, and security. Effective governance aligns with regulatory requirements, minimizing risks related to data breaches and non-compliance.

Compliance in data platforms involves adhering to industry regulations like GDPR or HIPAA, emphasizing data protection and ethical usage. Modern platforms must implement controls and audits to validate compliance continuously. This involves establishing role-based access controls, encryption, and retention policies.

#### 6. Data Orchestration and Automation

[Data orchestration](https://dagster.io/learn/data-pipeline-orchestration-tools) coordinates data processes, ensuring coherent workflows across different platform components. It manages dependencies, optimizing execution sequences to achieve efficiency and reliability in data operations. Orchestration tools automate these processes, reducing manual workload and errors.

Automation drives efficiency by simplifying repetitive tasks, using techniques like job scheduling and event-triggered processes. Automating workflows leads to faster turnaround times in data processing. By reducing manual interventions, organizations can focus on analytics and insights.

#### 7. Integration with AI/ML Platforms

By connecting to machine learning frameworks and AI tools, organizations can automate decision-making, identify patterns, and generate actionable insights at scale. These integrations often utilize APIs or native connectors to feed data from the platform into AI/ML models for training and inference.

A key aspect of AI/ML integration is ensuring data readiness. This includes providing clean, high-quality datasets and offering tools for feature engineering directly within the platform. Many modern platforms support AutoML features, allowing users with minimal expertise to build and deploy models. Additionally, integration with machine learning and deep learning frameworks provides support for training and developing custom models.

### Key Characteristics of Modern Data Platforms 

A modern data platform should have the following capabilities.

#### Scalability and Flexibility

Scalability is crucial for data platforms to manage increasing data volumes and complexity. Modern data platforms leverage cloud technologies to scale infrastructure dynamically, ensuring performance even during peak data loads. They accommodate both vertical and horizontal scaling, allowing seamless resource addition to meet demand.

#### Cloud-Native Architecture

A cloud-native architecture emphasizes microservices, containers, and orchestration tools like Kubernetes. These technologies enable efficient resource management and deployment, central to maintaining scalable and resilient data operations. Cloud-native platforms benefit from faster updates and reduced downtime due to their distributed nature.

#### Self-Service Capabilities

Self-service capabilities democratize data access, enabling users to derive insights independently without technical intervention. They provide intuitive interfaces for data querying and reporting, increasing analytical efficiency and reducing reliance on IT. Users are empowered to explore datasets and generate insights.

**Tips from the expert:**

In my experience, here are tips that can help you better implement and optimize a modern data platform:

1. **Leverage event-driven architectures for real-time processing:** Adopt event-driven approaches like Apache Kafka or AWS Kinesis to capture, process, and analyze data in near-real-time. This reduces latency and aligns with use cases that demand instant insights, like IoT and fraud detection.
2. **Utilize infrastructure-as-code (IaC) for reproducibility and scaling:** Treat the platform infrastructure as code using tools like Terraform or CloudFormation. This enables repeatable deployments, version control, and automated scaling, which are essential for modern, cloud-native platforms.
3. **Implement intelligent cost governance frameworks:** Integrate FinOps tools or cloud-native cost analyzers to monitor resource utilization and optimize costs. Auto-scaling strategies and resource tagging can help align spending with business priorities without compromising performance.
4. **Adopt data contracts to manage schema changes effectively:** Enforce data contracts between producers and consumers to define expected data structure, format, and quality. This minimizes disruptions caused by upstream schema changes and improves pipeline reliability.
5. **Incorporate ML-driven data quality monitoring:** Use machine learning models to identify anomalies, missing values, and inconsistencies in data streams. 

### Challenges in Building a Modern Data Platform

There are several elements that can make it more challenging to build a modern data platform compared to traditional platforms:

- **Data integration complexity:** Integrating diverse data sources presents challenges due to varying formats and protocols. This complexity can delay data availability and impact platform performance. A modern data platform must accommodate hybrid and multi-cloud integrations, offering seamless connectivity to internal and external systems.
- **Ensuring data security:** Data security is critical in the context of increasing cyber threats and tighter regulations. Ensuring data privacy requires strong encryption, access controls, and monitoring mechanisms. Modern platforms must proactively secure data throughout its lifecycle, minimizing vulnerabilities that could lead to unauthorized access. 
- **Maintaining data privacy:** Privacy considerations include compliance with regulations such as GDPR, which prioritize the protection of personal data. Platforms need to implement consent management, secure storage, and data anonymization methods. 
- **Managing data quality:** Ensuring data quality involves challenges such as inconsistencies and inaccuracies, which can impair analytics and insight generation. Effective data quality management requires automated data profiling, cleansing, and validation processes to maintain high standards. The platform must address data quality at all stages, from ingestion to processing and storage.
- **Skill gaps and change management:** Organizations need professionals skilled in relevant technologies and practices, such as data engineering and AI. This can be a problem if in-house teams lack the necessary skills. Change management becomes essential as new technologies and processes are adopted, ensuring teams are trained and adaptable to these changes.
- **Cost management:** Organizations must balance investment in infrastructure, tools, and talent with the need to demonstrate tangible ROI. Key cost drivers include data storage, computational resources, licensing fees for software and analytics tools, and operational costs for maintaining and scaling the platform.

### 6 Best Practices for Implementing a Modern Data Platform 

Organizations should consider the following practices when building modern data platforms.

#### 1. Design for Data Agility

Data agility involves creating systems that adapt quickly to changing requirements and diverse workloads. Start by adopting a modular architecture that separates components like ingestion, storage, and analytics. This ensures that updates to one part of the system don't disrupt the entire platform. Use containerization and orchestration tools to improve flexibility and simplify scaling.

Another strategy is to implement event-driven pipelines, allowing the platform to handle asynchronous data flows effectively. Event-driven designs enable organizations to respond to real-time triggers, supporting use cases like fraud detection or live monitoring. Ensuring schema flexibility and employing tools for on-the-fly transformations makes the platform more adaptable.

#### 2. Emphasize Data Governance by Default

Data governance should be embedded as a foundational feature rather than an afterthought. Establish automated governance frameworks that monitor data access, enforce compliance, and maintain quality at every stage of the data lifecycle. Use role-based access controls and encryption to ensure secure and compliant data handling.

Integrate governance tools into the platform's pipelines to validate data compliance in real time. For example, applying dynamic data masking can protect sensitive information without impacting usability. Embedding governance as default ensures adherence to regulations like GDPR while reducing manual interventions.

#### 3. Promote Data Quality

Promoting data quality involves implementing strategies to ensure accuracy, consistency, and reliability across the data lifecycle. Begin by establishing clear data governance policies that define quality standards and accountability. Regularly monitor data quality using automated tools for profiling, validation, and cleansing to identify and correct issues promptly.

Data quality frameworks should include procedures for managing duplicate entries, missing values, and inconsistent formats. Engaging stakeholders in defining what constitutes high-quality data ensures alignment with business objectives. Additionally, maintaining metadata and lineage tracking aids in understanding data origins and transformations.

<p></p>

#### 4. Prioritize Real-Time Data Capabilities

Real-time data capabilities are essential for applications requiring instant insights. Leverage streaming platforms like Apache Kafka or AWS Kinesis to capture and process data in motion. These tools enable low-latency pipelines, delivering actionable insights in milliseconds for use cases such as dynamic pricing or anomaly detection.

Ensure that downstream systems, such as storage and analytics layers, are optimized for real-time workflows. This involves selecting databases and query engines that support low-latency operations, like Redis or Apache Druid. Real-time monitoring tools should also be in place to track pipeline performance and troubleshoot latency issues quickly.

#### 5. Enable Democratized Data Access

Democratized data access empowers non-technical users to work with data independently. Provide user-friendly self-service tools, such as drag-and-drop interfaces for querying and prebuilt templates for reporting. These tools reduce reliance on technical teams, enabling faster decision-making.

Encourage a culture of data literacy by offering training programs and documentation for platform users. Establish clear access policies to ensure security while promoting transparency. Role-based permissions can allow appropriate access levels for different user groups, ensuring both usability and compliance.

#### 6. Invest in Continuous Monitoring and Optimization

Continuous monitoring ensures data platform health, identifying potential issues before they escalate. It involves real-time analysis of platform components, measuring performance and efficiency. Proactive monitoring tools and techniques improve system reliability and data availability.

Optimization follows monitoring, incorporating adjustments to improve platform capabilities based on insights from performance analysis. Continuous feedback loops between monitoring and optimization drive incremental improvements and strategic agility.

### Building a Modern Data Platform with Dagster

Dagster provides the orchestration backbone that modern data platforms need to achieve reliability, scalability, and maintainability. As a data orchestration platform built for the full lifecycle of data assets, Dagster offers a unified framework for defining, scheduling, monitoring, and governing data pipelines, whether they run batch, streaming, ML, or interactive workloads.

A few reasons organizations use Dagster as the foundation of their modern data platform:

#### 1. Software-Defined Assets for Clear Data Lineage and Ownership

Dagster introduces *software-defined assets*, which represent data products as first-class, versioned objects. SDAs create a clear contract between upstream and downstream processes, enabling strong lineage tracking, schema enforcement, and reproducibility. This aligns closely with modern practices such as data contracts, observability, and end-to-end governance.

#### 2. A Universal Orchestrator for Every Tool in the Stack

Today’s platforms use a mix of cloud services, warehouses, and AI and ML tools. Dagster’s extensible integration ecosystem connects easily with systems like Snowflake, BigQuery, dbt, Spark, Airbyte, and ML frameworks. This enables an orchestrator-first architecture that standardizes how teams develop and run pipelines across diverse environments.

#### 3. Built-In Observability and Operational Insights

Dagster provides real-time visibility into asset states, job runs, logs, and dependencies. Modern platforms prioritize proactive monitoring, and Dagster’s observability features, including event logs, materialization history, and an intuitive UI, give teams the transparency they need to diagnose issues quickly and maintain consistent data quality.

#### 4. Strong Governance, Type Safety, and Testing

Governance is a core theme of modern platforms. Dagster brings structure through:

- type checking and schema validation
- automated dependency management
- asset-level versioning
- integration with data quality tools and tests

This reduces the risk of breaking changes and ensures trust in downstream analytics and ML workloads.

#### 5. Cloud-Native, Scalable, and Developer-Friendly

Dagster Cloud offers a fully managed control plane with hybrid execution options, enabling organizations to scale data operations without managing complex infrastructure. Developers benefit from a modern Python-based workflow, local development tools, CI/CD support, and declarative definitions that make pipelines easier to maintain over time.

#### With Dagster, Your Modern Data Platform Gets:

- A unified orchestration framework that connects storage, compute, transformation, and ML systems
- Reliable and maintainable pipelines built with software engineering best practices
- Strong governance and observability across every data asset
- Scalable execution via Dagster Cloud or self-hosted deployments
- Faster delivery of data products with reduced operational overhead

Dagster turns your data platform into a cohesive and well-governed ecosystem, helping teams deliver high-quality data and insights consistently, even as your architecture and requirements evolve.

---

## Apache Airflow API: Basics & Quick Tutorial [2025]

> Apache Airflow API is a programmatic interface provided by Apache Airflow, an open-source platform used to author, schedule, and monitor workflows (DAGs).

Source: https://dagster.io/learn/airflow-api

### What Is Apache Airflow API? 

Apache Airflow API is a programmatic interface provided by Apache Airflow, an open-source platform used to author, schedule, and monitor workflows (DAGs). Through its API, Airflow exposes endpoints that let users manage, trigger, and inspect DAGs, tasks, and other system components over HTTP. This API enables integration with external systems, automation scripts, monitoring tools, and CI/CD pipelines.

The Airflow API supports both a stable REST API (from version 2.x onward) and a legacy experimental API (available in earlier versions). The stable REST API follows the OpenAPI specification, which provides documentation, client code generation, and predictable request/response formats. 

Get the Apache Airflow API documentation [here](https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html).

### Key Aspects of the Apache Airflow API

#### Core Resources

The Apache Airflow API is organized around resources, which represent distinct entities in the Airflow system. These resources are primarily managed through HTTP endpoints that are grouped by their type. Examples of resources include `dagRuns`,` tasks`, `dags`, and connections.

Resource names are typically plural and expressed in camelCase (e.g., `dagRuns`, `taskInstances`). These names are consistent throughout Airflow, appearing in URLs, API parameters, and responses. The API allows users to interact with these resources via standard HTTP methods like GET, POST, PATCH, and DELETE.

The structure of a resource's metadata is consistent across most objects, containing fields like `name`, `description`, and other relevant attributes. The exact fields can vary depending on the type of resource, and they are expressed in snake\_case (e.g., `created_at`, `last_modified`).

#### Endpoint Structure

Airflow's API endpoints follow a clear and consistent structure. The base URL for API requests typically begins with `/api/v2/`, followed by the specific resource name. Each resource has several possible operations (e.g., listing, creating, updating, deleting) defined by different HTTP methods.

For example:

- **`GET /api/v2/dags`** — Retrieves a list of DAGs.
- **`POST /api/v2/dags/{dag_id}/dagRuns`** — Creates a new DAG run.
- **`PATCH /api/v2/dags/{dag_id}/dagRuns/{dag_run_id}/ taskInstances/{task_id}/{map_index}`**— Updates the specified task in a DAG.

The API also supports query parameters to refine requests. Common parameters include` limit`, `offset`, and filtering criteria (e.g., `state`, `execution_date`).

#### CRUD Operations

The Airflow API supports Create, Read, Update, and Delete (CRUD) operations on most resources:

- **Create (POST)**: To create a new resource, send a POST request with the resource's metadata in the body. A successful request returns a 201 status code and the resource's metadata.
- **Read (GET)**: To retrieve resource details, send a GET request with the resource ID as a parameter. If no ID is provided, the request returns a list of resources. The response will include the resource metadata.
- **Update (PATCH)**: Updates to a resource can be performed using the PATCH method, where only the fields specified in the request body are updated. This is often accompanied by the `update_mask` parameter, indicating which fields should be modified.
- **Delete (DELETE)**: To delete a resource, a DELETE request with the resource ID is sent. A successful deletion returns a 204 status code with no content.

These operations support additional features such as pagination for listing resources and partial updates using the update\_mask query parameter.

#### Query Parameters

Airflow's API supports various query parameters to refine API calls. Some common query parameters include:

- **limit**: Specifies the maximum number of objects to fetch in a list. The default is typically 25.
- **offset**: Defines the offset to start returning objects after a specified number, useful for pagination.

Example query:

‍

`GET /api/v2/connections?limit=25&offset=50`

‍

These query parameters allow users to control the size of responses and navigate through large datasets in a structured manner.

#### Authentication and Authorization

The Airflow API supports multiple authentication methods, ensuring that only authorized users can interact with the system. The default authentication backend is basic authentication, but it can be customized to integrate with other authentication systems (e.g., LDAP, OAuth). The authentication method used must be configured within Airflow’s settings.

When interacting with the API, users must provide valid credentials. If authentication fails, the API will respond with a 401 Unauthorized status code. Additionally, the system employs role-based access controls (RBAC) to ensure users can only access resources they have permission to.

### Using the Apache Airflow API in MWAA

Amazon Managed Workflows for Apache Airflow (MWAA) provides a managed environment for running Airflow, and it includes support for the stable REST API. The API in MWAA works the same way as in self-managed Airflow, but access is restricted to ensure security and integration with AWS services.

To call the Airflow API in MWAA, you must send requests through the MWAA environment’s web server endpoint. This endpoint is private to your VPC by default, but it can be configured for public access if required. Since the endpoint is fronted by AWS Identity and Access Management (IAM), API requests must be signed with AWS Signature Version 4. This ensures that only authorized IAM users, roles, or services can interact with the Airflow environment.

Typical steps for using the API in MWAA are:

1. **Obtain the MWAA web server URL** from the AWS console or CLI.<br><br>
2. **Sign API requests** using AWS SigV4. You can use tools like `aws-sigv4-proxy`, the AWS SDK, or the` requests-aws4auth` library in Python.<br><br>
3. **Send requests to the Airflow API endpoints** under `/api/v2/`, the same as in standard Airflow (e.g., `/api/v2/dags`, `/api/v2/dags/{dag_id}/dagRuns`).

Because authentication is IAM-based, you don’t use Airflow’s basic auth or RBAC accounts directly. Instead, IAM permissions determine who can call the API, while Airflow’s RBAC still controls what actions a user can perform once inside the system.

This setup allows you to integrate MWAA with CI/CD pipelines, AWS Lambda, or external workflow orchestrators without exposing static credentials. It also ensures that API access follows the same security model as the rest of your AWS infrastructure.

### Tutorial: Working with Airflow Taskflow API 

The Taskflow API, introduced in Airflow 2.0, simplifies DAG creation by allowing tasks to be defined as standard Python functions. This abstraction reduces boilerplate code, manages XComs automatically, and makes the DAG definition more readable and Pythonic. 

The tutorial instructions below are adapted from the [Airflow documentation](https://airflow.apache.org/docs/apache-airflow/2.1.4/tutorial_taskflow_api.html).

‍**Note:** Ensure your AWS Airflow UI is up and running.

![](https://dagster.io/webflow-assets/281a16c9afbaa6b02d7e.webp)

##### **Defining a DAG with the Taskflow API**

To use the Taskflow API, decorate your DAG function with `@dag()` and your task functions with `@task()`. For example, a simple ETL workflow can be built as follows:

‍

`@dag(default_args=default_args, schedule_interval=None, start_date=days_ago(2), tags=['example'])`

`def tutorial_taskflow_api_etl():`

`    @task()`

`    def extract():`

`        data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}'`

`        return json.loads(data_string)`

`‍`

`    @task(multiple_outputs=True)`

`    def transform(order_data_dict):`

`        total = sum(order_data_dict.values())`

`        return {"total_order_value": total}`

`‍`

`    @task()`

`    def load(total_order_value):`

`        print(f"Total order value is: {total_order_value:.2f}")`

`‍`

`    order_data = extract()`

`    order_summary = transform(order_data)`

`    load(order_summary["total_order_value"])`

`‍`

`tutorial_etl_dag = tutorial_taskflow_api_etl()`

‍

![](https://dagster.io/webflow-assets/e1059176601c1a4be5ba.webp)

This defines three tasks—`extract`, `transform`, and` load`—and wires them together using function calls. The return value of one task is passed directly to the next, with Airflow handling inter-task communication under the hood using XComs.

##### **Comparison to Pre-Airflow 2.0 DAGs**

Prior to the Taskflow API, DAGs required manual task definitions using `PythonOperator`, explicit XCom handling, and manual setting of task dependencies. The Taskflow API removes much of this complexity by:

- Automatically generating task IDs from function names
- Handling XComs transparently
- Creating task dependencies through function invocation<br>

##### **Virtual Environment Support**

As of Airflow 2.0.3, tasks can be executed in isolated virtual environments using the `@task.virtualenv` decorator. This allows developers to define custom Python dependencies or versions specific to a task:

‍

`@task.virtualenv(`

`    use_dill=True,`

`    system_site_packages=False,`

`    requirements=['funcsigs'],`

`)`

`def extract():`

`    data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}'`

`    return json.loads(data_string)`

‍

This feature is useful for isolating dependencies or running Python code that isn't supported in the default Airflow environment.

##### **Mixed Task Dependencies**

The Taskflow API supports integration with traditional tasks such as `BashOperator` or `FileSensor`. You can define dependencies between decorated and traditional tasks using standard Airflow syntax:

‍

`file_task = FileSensor(task_id='check_file', filepath='/tmp/order_data.csv')`

`order_data = extract_from_file()`

`file_task >> order_data`

‍

This flexibility allows Taskflow-based DAGs to interoperate with existing Airflow components seamlessly.

##### **Multiple Outputs**

Tasks can return dictionaries as outputs. When the return type is annotated with `Dict`, Airflow infers multiple outputs automatically:

‍

`@task`

`def identity_dict(x: int, y: int) -> Dict[str, int]:`

`    return {"x": x, "y": y}`

‍

Alternatively, set `multiple_outputs=True` in the `@task()` decorator explicitly if type hints are not used.

### Best Practices for Using Apache Airflow API 

#### 1. Securing Endpoints with SSL

When using the Apache Airflow API, especially in production environments, it’s critical to secure communication between clients and the server. Enabling SSL (Secure Sockets Layer) ensures that data transmitted over the network is encrypted, protecting sensitive information from eavesdropping and tampering. SSL certificates can be configured on the web server hosting Airflow, ensuring that all API traffic is encrypted using HTTPS rather than HTTP.

To implement SSL, you need to configure the web server hosting Airflow to use an SSL certificate. This typically involves:

1. Generating or obtaining an SSL certificate from a trusted certificate authority.
2. Configuring the Airflow web server to listen on port 443 (HTTPS).
3. Updating Airflow’s configuration files to specify the certificate and key paths for SSL.

By securing the API endpoints with SSL, you ensure that only authorized parties can interact with your Airflow API securely.

#### 2. Implementing Request Retries

The Apache Airflow API may occasionally face temporary network issues or server unavailability that result in failed requests. In such cases, it is important to implement retries to ensure that operations are completed successfully. When calling Airflow’s API programmatically, you can use libraries such as `requests` or `boto3` to implement retries with exponential backoff.

Here’s a simple approach to implement retries:

1. Set the maximum number of retry attempts.
2. Introduce a delay between retries, with an increasing delay after each failed attempt (exponential backoff).
3. Handle specific HTTP error codes (like 500 or 502) that are commonly transient.

#### 3. Documentation and Change Management

Maintaining up-to-date documentation is essential when using the Apache Airflow API, especially when managing and automating complex workflows. Documenting the API endpoints, their parameters, and expected responses makes it easier for developers to use the API correctly and troubleshoot issues.

Version control of both Airflow DAGs and API scripts is also essential. Keep track of changes to the Airflow API’s behavior, such as changes in request/response structures or deprecations, to ensure compatibility. Using tools like Swagger for interactive API documentation or writing inline code comments can enhance the understanding and usage of the API.

#### 4. Testing Endpoints in CI/CD

Automated testing of the Apache Airflow API endpoints is crucial for validating the functionality of integrations and ensuring that any updates to workflows or system configurations don’t break the API functionality. Including tests in your CI/CD pipeline will ensure that API requests and responses are functioning as expected.

You can use frameworks like Postman or Pytest for API testing. For example, in Pytest, you can write tests to verify the behavior of API calls:

‍

`import requests`

`‍`

`def test_get_dags():`

`    url = "https://airflow.example.com/api/v2/dags"`

`    response = requests.get(url)`

`    assert response.status_code == 200`

`    assert "dags" in response.json()`

‍

Automate these tests within your CI/CD pipeline to continuously monitor the integrity of the API and ensure that failures are caught early in the development process.

#### 5. Ensure Compatibility Between Airflow Versions and API Clients

As Apache Airflow evolves, the API may undergo changes that affect how requests are handled or how endpoints function. It's essential to ensure that the API clients used in your organization are compatible with the version of Airflow you are running.

To avoid compatibility issues:

1. Regularly check the Airflow release notes for any breaking changes or new API features.
2. Use versioned endpoints (e.g., `/api/v2/`), as they help prevent issues when Airflow releases new versions of the API.
3. Test your client applications against staging or development environments running the same Airflow version to ensure compatibility before deployment.

---

## Azure Data Catalog is EOL: Getting Started with Microsoft Purview

> Azure Data Catalog is a cloud metadata repository that lets organizations register, discover, understand, and consume data sources across Azure and beyond.

Source: https://dagster.io/learn/azure-data-catalog

### What Is Azure Data Catalog? 

Azure [Data Catalog](https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices) is a cloud-based metadata repository offered by Microsoft. It enables organizations to register, discover, understand, and consume data sources on Azure and beyond. With Data Catalog, data assets are indexed with metadata and searchable via a web-based portal, improving collaboration among data consumers, data stewards, and technical teams.

**Azure Data Catalog is now End of Life (EOL)**

Microsoft officially retired Azure Data Catalog on **May 15, 2024**. As of this date, the service is no longer available and new instances cannot be created. 

Users of Azure Data Catalog were encouraged to **migrate to Microsoft Purview**, which offers a more advanced data governance platform, including a data catalog solution called Unified Catalog. It supports automated data discovery, sensitive data classification, and end-to-end data lineage tracking. Purview enables organizations to manage data across on-premises, multi-cloud, and SaaS environments.

The shift to Microsoft Purview ensures continued support for data cataloging needs while introducing stronger **data security** features and broader **governance tools**. Organizations are advised to review migration plans and associated pricing well ahead of the retirement deadline. Support for existing Azure Data Catalog resources will remain in place until the cutoff date.

### What Is Microsoft Purview Unified Catalog? 

Microsoft Purview Unified Catalog is the evolution of data cataloging in Azure, built to unify governance, discovery, and access management in a single SaaS platform. Unlike earlier catalog tools, it secures and standardizes data while helping organizations extract business value from it.

The catalog introduces a federated governance model. This combines centralized policy setting with decentralized ownership, so teams can manage their own data while still complying with enterprise-wide standards. This approach helps reduce bottlenecks while maintaining consistency in security, quality, and access control.

Unified Catalog supports multiple roles across the data lifecycle. Data consumers can search, discover, and request access to datasets through governance domains, data products, or AI-powered search. Data stewards and owners can curate, annotate, and manage data with policies tied to glossary terms and critical data elements. Executives and officers can monitor the overall health of the data estate, track quality scores, and align usage with organizational objectives.

Key capabilities of Microsoft Purview include:

- **Access policies** that balance self-service access with compliance
- **Governance domains** to organize assets by business concepts
- **Data products** that group related assets for easier discovery and reuse
- **Glossary terms** and **critical data elements** that standardize context and enforce consistent governance
- **Health management** tools that score [data quality](https://dagster.io/learn/data-quality) and provide actions to improve governance

### Tutorial: Discovering Data with Microsoft Purview 

To discover data in Microsoft Purview Unified Catalog, you use the **Discovery** tools available in the portal. Depending on your role and permissions, you can search and browse either **data products** or **data assets**.

Screenshots in this section were sourced from [azure.microsoft.com](http://azure.microsoft.com). 

#### Step 1: Searching for Data Products

Data products are curated, ready-to-use collections of assets created for business use.

1. In the Purview portal, go to **Unified Catalog > Discovery > Data products**.

![](https://dagster.io/webflow-assets/1cc172118d9482bda348.webp)

2. You can search by **keywords** (for example, “sales”) or use **natural language search** (preview), which accepts queries in English, French, or Spanish.
3. Results are matched against product metadata such as name, description, associated glossary terms, governance domains, and included assets.Filters let you refine results by governance domain, product type, owner, glossary term, or critical data element.

![](https://dagster.io/webflow-assets/539b7aff5da2002fe7f2.webp)

4. Filters let you refine results by governance domain, product type, owner, glossary term, or critical data element.

Selecting a product opens its **details page**, which shows its description, terms of use, glossary terms, and linked data assets. Each linked asset can be opened further for schema, lineage, and ownership details.

#### Step 2: Browsing by Governance Domain

Instead of searching, you can browse data products organized by governance domains.

1. From **Discovery > Data products**, select a domain to view its products.

![](https://dagster.io/webflow-assets/2f417438bb45fd4e24b3.webp)

2. You can also apply **custom attribute filters** (such as Equals or Starts with) to narrow down the list.

![](https://dagster.io/webflow-assets/3f9010d051ef062871e5.webp)

#### Step 3: Searching for Data Assets

If you need raw assets instead of curated products, you can search within **Unified Catalog > Discovery > Data assets**.

1. Use the search bar to enter keywords, classifications, glossary terms, or asset names. Search results are ranked by relevance and highlight matched terms.
2. Filters allow refinement by attributes such as asset type, classification, collection, contact, endorsement, tags, or update activity.
3. Bulk editing is available to update glossary terms, classifications, or contacts across multiple assets.

![](https://dagster.io/webflow-assets/79dc8d5eb2e34d97c917.webp)

Selecting a data asset opens its **details page**, which includes description, schema, lineage, owner, and related products.

#### Step 4: Browsing Data Assets and Searching Connected Services

For exploratory work, you can browse data assets by **collection** or by the **hierarchy of a registered data source**. Only assets you have read access to are visible.

![](https://dagster.io/webflow-assets/d1681a7d3fced8ffc13f.webp)

Once Purview is registered with tools like **Azure Data Factory** or **Azure Synapse Analytics**, you can run catalog searches directly from those services. This integration helps locate and use governed data while staying in your workflow.

![](https://dagster.io/webflow-assets/da494d69e58bf7456f90.webp)

### Microsoft Purview Unified Catalog Best Practices 

#### 1. Build Effective Governance Domains

Governance domains are the backbone of Purview’s federated governance model. They distribute ownership and give users a way to navigate the catalog without being overwhelmed by the full data estate. A well-designed domain structure makes discovery easier, aligns responsibilities with business expertise, and reduces governance bottlenecks.

When defining domains, avoid structuring them around platforms or technologies. IT systems often reflect how applications are built, not how data is used by the business. Instead, center domains on business use, such as subject areas (e.g., finance, sales), functions (e.g., risk, operations), regulatory categories, or major projects. A mixed model that blends these perspectives provides durability as organizations change and scales better than single-approach structures.

Start small, selecting a few domains with strong existing stewardship. Assign stewards and product owners, and let them develop the first glossary terms and data products. Keep domains in draft until those products are mature, then publish them and open access to a small user group. Feedback from those early users helps refine scope, structure, and content before broader rollout.

#### 2. Design Valuable Data Products

Data products turn raw assets into discoverable, governed, and reusable business-ready packages. Not every dataset warrants a product—focus first on high-value, trusted resources that already support decision-making. Examples include curated lake zones, high-quality SQL stores, enterprise warehouses, or reference datasets like customer master data. Reports and tables that business units regularly use to drive outcomes are also strong candidates.

Creating data products should be part of your intake process when onboarding new sources into the data map. Product owners need visibility into which assets are scanned and available, so they can quickly assess what should be grouped into products. Building from already-curated data accelerates adoption and demonstrates the benefits of the catalog early.

Each product should include clear descriptions, linked glossary terms, lineage, and data quality details. Publish only when the target audience is ready to use the product. This avoids “empty shelves” that erode trust and ensures early catalog users always find something meaningful.

#### 3. Define and Maintain Glossary Terms

Glossary terms give shared meaning to data across domains. They connect technical fields to business language and enable consistent interpretation of metrics, reports, and assets. Without them, users struggle to understand what data actually represents.

Begin with terms already in use by the business. Many departments maintain glossaries for onboarding new employees or supporting compliance efforts. Bring these into Purview and expand them over time. If a term is ambiguous, add it anyway with a basic definition—publishing early allows stewards and users to refine it as understanding improves.

Map glossary terms directly to data products and assets. This strengthens discoverability, ensures consistent use of language, and creates a feedback loop: as users search by term, stewards can see which products rely on it and adjust definitions where needed. Regular reviews of term mappings help keep the glossary relevant and aligned to real usage.

#### 4. Apply Custom Metadata Thoughtfully

Custom metadata extends Purview beyond its built-in attributes. It allows organizations to capture details that matter for their specific governance, compliance, or operational needs. Well-planned metadata structures improve navigation, filter precision, and governance automation.

Define attributes carefully. Overloading assets with unnecessary fields leads to inconsistency and wasted effort. Instead, focus on attributes that drive real business or governance outcomes—examples include regulatory classification, data lifecycle stage, sensitivity, or stewardship contact.

Standardize usage across domains. Ensure business concept types (e.g., governance domains, products) and data asset types share consistent attribute definitions. This helps stewards and consumers interpret metadata without confusion. Review attribute usage periodically and retire those that provide little value. Metadata should evolve alongside governance practices, not remain static.

#### 5. Elevate Understanding with Critical Data Elements

Critical data elements (CDEs) capture the most important fields within a dataset—the ones that drive compliance, reporting accuracy, or core business processes. Documenting and governing them in Purview ensures high-value data is consistently understood, protected, and trusted.

Don’t attempt to classify every field as critical. Instead, focus on a small subset of elements that matter most for decision-making or regulatory reporting. For example, customer identifiers, financial balances, or product codes may need strict governance, while ancillary fields do not.

For each CDE, define ownership, expected meaning, quality rules, and access controls. Aligning these elements with glossary terms ensures consistency of business definitions. Apply data quality checks to validate values across systems, and monitor lineage to confirm how critical elements flow into reports and models.

Cross-domain review of CDEs is essential. Often, a critical field in one domain (e.g., customer ID in sales) underpins use cases in another (e.g., customer service or finance). By aligning definitions and controls, organizations reduce duplication and avoid misaligned interpretations.

### Data Orchestration for Microsoft Purview Users with Dagster

Microsoft Purview provides governance, discovery, lineage, and access control for data across an organization. Dagster complements these capabilities by orchestrating the pipelines that create and update data assets. Together, they support a modern data architecture where high quality, well governed, and reliably produced data is available to consumers.

Dagster’s asset based development model aligns naturally with the way Purview organizes and catalogs information. Dagster assets represent tables, files, views, and other data outputs that Purview can register, scan, and classify. When Dagster materializes or updates these assets in systems such as Azure Data Lake Storage, Azure SQL Database, Azure Synapse, or Fabric Lakehouse, Purview can automatically discover them and capture metadata, lineage, and classifications.

Teams can also use Dagster to standardize transformations and enforce the operational quality that supports Purview governance. Examples include:

- Creating repeatable pipelines that feed trusted zones in a lake or warehouse
- Establishing clear ownership of upstream logic tied to assets Purview catalogs
- Surfacing data quality checks in Dagster that align with Purview classifications and domain expectations
- Coordinating schedules and dependencies that ensure cataloged assets stay fresh and reliable<br><br>

Some teams choose to enhance this relationship further by emitting metadata, descriptions, or lineage details from Dagster into Azure services that Purview scans. This helps reinforce consistent context between operational pipelines and governed assets.

By combining the strengths of Dagster’s orchestration with Purview’s governance and discovery capabilities, organizations can deliver data that is both operationally dependable and fully aligned with enterprise standards. The result is a unified ecosystem where pipelines, metadata, asset quality, and governance reinforce each other and accelerate the creation of data products.

---

## Best Data Engineering Services: Top 5 Providers in 2025

> Data engineering services encompass a range of capabilities focused on building and managing the infrastructure that supports data-driven decision-making. These services involve designing, building, and maintaining systems for collecting, storing, processing, and analyzing large volumes of data.

Source: https://dagster.io/learn/data-engineering-services

### What Are Data Engineering Services? 

[Data engineering](https://dagster.io/learn/data-engineering) services support organizations in building a modern data infrastructure. These services involve designing, building, and maintaining systems for collecting, storing, processing, and analyzing large volumes of data. Data engineers ensure that data is readily available, reliable, and usable for various purposes, including analytics, reporting, and machine learning.

Key aspects of data engineering services include:

- **Data strategy and architecture:** Developing a roadmap for data management, including identifying data sources, platforms, and key performance indicators (KPIs). 
- **Data pipeline development:** Building and maintaining pipelines for the extraction, transformation, and loading (ETL) of data, as well as for real-time data processing (streaming). 
- **Data storage and warehousing:** Designing and implementing data lakes, data warehouses, and delta tables for efficient storage and retrieval of both structured and unstructured data. 
- **Data governance and management:** Ensuring data quality, security, and compliance through the implementation of data governance frameworks and policies. 
- **DataOps and MLOps:** Implementing automated processes for data integration, quality assurance, and deployment, as well as enabling machine learning operations. 
- **Cloud data engineering:** Utilizing cloud platforms like AWS and Azure for data storage, processing, and analytics. 

Modern data engineering services may include building data lakes, warehouses, real-time pipelines, ETL (Extract, Transform, Load) processes, and supporting the implementation of data security and governance policies. Whether delivered through consulting firms, managed solutions, or specialized platforms, these services help organizations of all sizes turn raw data into actionable insights.

### Benefits of Using Data Engineering Services

Leveraging data engineering services offers organizations the foundation they need to manage data effectively and derive meaningful insights. By outsourcing or partnering for these capabilities, organizations can focus on outcomes rather than the complexities of infrastructure and pipeline management:

- **Faster time to insights**: Well-structured data pipelines and storage systems reduce the time needed to prepare and access data, enabling quicker decision-making.
- **Scalability and flexibility**: Data engineering services are designed to handle growing data volumes and evolving business needs without requiring a complete system overhaul.
- **Improved data quality and consistency**: Automated validation, transformation, and monitoring processes help ensure data is clean, accurate, and consistent across systems.
- **Operational efficiency**: Automation and optimized workflows reduce manual effort, minimize errors, and improve the overall efficiency of data operations.
- **Enhanced data security and compliance**: Services often include built-in mechanisms for access control, encryption, and auditability, supporting regulatory compliance and data protection.
- **Access to specialized expertise**: Teams benefit from the experience of data engineering professionals who stay up to date with evolving technologies, architectures, and best practices.
- **Cost optimization**: By using cloud-native and scalable technologies, data engineering services can help reduce infrastructure costs and eliminate redundant systems.

### Key Aspects of Data Engineering Services Providers 

#### Data Strategy and Architecture

A comprehensive data strategy defines how an organization will collect, manage, and use its data assets, aligning technical approaches with business objectives. Data engineering services providers assist in developing this strategy, identifying data needs, mapping out data flows, and recommending best-fit technologies. Architecture design involves structuring data systems to support performance, scalability, and reliability, considering factors such as data formats, integration patterns, cloud vs. on-premise deployment, and compliance requirements.

Effective data architecture also addresses future needs, making sure systems are extensible and easily adaptable as business priorities shift. Providers often conduct audits of existing infrastructure, propose modernization plans, and establish reference architectures that guide the implementation of platforms and pipelines. This ensures long-term sustainability and a solid foundation for analytics, AI, or other data-driven applications.

#### Data Pipeline Development

Data pipelines automate the movement and transformation of raw information into structured, usable data sets ready for analysis. Providers design and implement both batch and real-time pipelines that extract data from diverse sources, apply cleaning and transformation steps, and load results into target systems, like warehouses or lakes. This development process focuses on minimizing latency, ensuring accuracy, and making pipelines resilient to changes in source systems or data formats.

Building robust pipelines includes error monitoring, automated testing, and validation steps to reduce downstream data quality issues. Providers also employ workflow orchestration tools that schedule, monitor, and manage pipeline executions, improving operational efficiency. The result is a reliable flow of high-quality data that underpins analytics, machine learning models, and enterprise reporting.

#### Data Storage and Warehousing

The choice of data storage solutions, whether data lakes, data warehouses, or hybrid models, depends on the business's analytical needs, data variety, and performance requirements. Providers help organizations select and implement the best-fit storage options, designing schemas to optimize querying and ensure cost-effective scalability. Data warehouses support structured, high-speed analytics, while data lakes accommodate large volumes of raw, unstructured, or semi-structured information.

Providers also handle data migration, integrating legacy databases with modern platforms and ensuring minimal business disruption. Optimization and lifecycle management are crucial, as data storage costs and system performance can be major concerns. By organizing data logically and applying governance policies, providers make stored data more accessible and secure for business and technical users.

#### Data Governance and Management

Data governance encompasses the policies, standards, processes, and technologies that ensure data consistency, accuracy, privacy, and regulatory compliance. Providers help define governance frameworks, establish data catalogs, implement security measures, and monitor access and usage. This enables organizations to maintain trusted data assets, mitigate privacy risks, and meet requirements like GDPR, HIPAA, or CCPA.

Data management extends beyond policy creation to include metadata management, master data management, and data quality monitoring. Providers often deploy tools for automated lineage tracking, anomaly detection, and reporting. These practices prevent the proliferation of “shadow data,” support audit trails, and foster a culture of responsible, evidence-based decision-making throughout the organization.

#### DataOps and MLOps

DataOps applies DevOps principles to data pipeline development and management, aiming for rapid, reliable delivery of data products through automation, continuous integration, and continuous deployment. Providers implement DataOps practices to improve collaboration between engineering, analytics, and business teams, reducing manual bottlenecks and increasing deployment speed and reliability.

MLOps focuses on automating the deployment, monitoring, and management of machine learning models in production. Providers help build workflows for version control, model testing, retraining, and rollback, ensuring that machine learning delivers consistent value without unexpected failures. By institutionalizing these practices, organizations can scale AI initiatives with confidence and agility.

#### Cloud Data Engineering

Cloud data engineering services focus on designing, implementing, and managing data solutions in public, private, or hybrid cloud environments. Providers leverage cloud-native technologies such as serverless compute, containerized workflows, and managed databases to deliver highly scalable and cost-efficient data architectures. This allows organizations to respond quickly to changing data volumes and analytical demands.

Cloud services also enable data sharing, multi-region availability, and advanced analytics features out-of-the-box. Providers manage cloud resource optimization, security configuration, migration from on-premises systems, and ongoing operational monitoring. As a result, organizations benefit from faster innovation cycles, simplified infrastructure management, and reduced total cost of ownership.

### Notable Data Engineering Services

#### 1. Dagster Cloud

![](https://dagster.io/webflow-assets/bd38e0ce6114124fee73.avif)

Dagster is an open-source data orchestrator designed for developing, deploying, and observing data pipelines with a strong focus on software engineering best practices. Built to improve reliability, maintainability, and data quality in modern data platforms, Dagster introduces software-defined assets that let teams model their data ecosystem around the datasets they produce.

**Key features include:**

- **Software-defined assets:** Model data as first-class assets with explicit dependencies, enabling clear lineage, consistent updates, reproducible outputs, and a unified view of the data lifecycle.
- **Typing and validation:** Enforce schemas, runtime checks, and strong typing to ensure predictable pipelines, early error detection, and consistent data quality across ingestion and transformation steps.
- **Developer experience:** Provide local-first, testable, Pythonic workflows that support fast iteration and clear structure, helping teams build and maintain pipelines efficiently.
- **UI and observability:** Offer a modern interface showing lineage, runs, partitions, metadata, and asset health, giving teams deep visibility into pipeline operations and data states.
- **Flexible orchestration:** Support schedules, sensors, partitions, and backfills across multiple execution environments, enabling reliable orchestration of ingestion, transformation, and analytics pipelines.

**Integrations:** Connect to warehouses, lakes, analytics tools, and ML frameworks, integrating smoothly with dbt, Snowflake, BigQuery, Spark, and other data platform components.

![](https://dagster.io/webflow-assets/42b63941965f3662a688.avif)

#### 2. Databricks

![](https://dagster.io/webflow-assets/bf1e1f6a6026be2862e2.avif)

Databricks offers a suite of data engineering services through its Lakeflow platform, intended to simplify the development of reliable, scalable, and AI-ready data pipelines. By combining ingestion, transformation, orchestration, and governance, Databricks enables organizations to build batch and real-time workflows with reduced operational complexity. 

**Key features include:**

- **Unified tool stack:** Combines data ingestion, transformation, governance, and orchestration into a single platform, reducing integration overhead and tool sprawl.
- **Easier pipeline creation:** Supports no-code connectors, CDC (change data capture), and AI-assisted code generation.
- **Optimized processing engine:** Automatically tunes compute resources for efficient batch and real-time processing, improving performance and cost-efficiency.
- **Lakeflow jobs:** Supports high-reliability orchestration with observability and support for ETL, analytics, and AI tasks.

**Lakeflow Connect:** Provides native integration and prebuilt connectors for quick access to analytics and AI-ready data.

![](https://dagster.io/webflow-assets/26160d07ef8c436b4f37.avif)

#### 3. Snowflake

![](https://dagster.io/webflow-assets/97657961c7392036478c.avif)

Snowflake provides a data engineering solution that removes the complexity of managing infrastructure, enabling teams to focus on building scalable, AI-ready pipelines. Its platform supports the full lifecycle of data engineering, from ingestion and transformation to delivery, while offering native features and interoperability with open-source tools. 

**Key features include:**

- **ZeroOps data engineering:** Automates pipeline development and operations, removing the need for infrastructure tuning while meeting strict data SLAs
- **Open lakehouse architecture:** Supports integration with or creation of open lakehouse environments using open-source standards like Apache Iceberg™, reducing data silos and improving accessibility
- **AI-ready data pipelines:** Handles unstructured and semi-structured data for real-time AI applications, enabling seamless development of generative AI and intelligent agents
- **Openflow for ingestion:** Offers a managed, multi-modal ingestion service that supports structured, unstructured, batch, and streaming data, ensuring interoperability across any architecture

**Developer productivity tools:** Includes native dev environments, Git integration, observability dashboards, and Snowpark to support multi-language development and DevOps practices.

![](https://dagster.io/webflow-assets/bc6232d2abaa71219ebb.avif)

#### 4. Coalesce

![](https://dagster.io/webflow-assets/dd391298e0532ea571bb.avif)

Coalesce is a data engineering platform to accelerate data transformation, improve pipeline reliability, and simplify data discovery within a unified environment. It enables teams to build scalable ELT workflows with a balance of automation and control, while also embedding cataloging and governance throughout the development process. 

**Key features include:**

- **AI-powered data transformation:** Speeds up the transformation phase of ELT by allowing users to code when needed and automate the rest.
- **Standardized, scalable pipelines:** Uses structured SQL and a consistent framework to build maintainable pipelines, avoiding unmanageable code and reducing upkeep.
- **Column-level lineage and governance:** Tracks data changes at a granular level, improving auditability and control while supporting DataOps practices.
- **Intelligent Data catalog:** Integrates documentation, governance, and AI-driven search into daily workflows, enabling teams to discover and understand data more easily.

**Repeatable, trusted development:** Provides quality-tested templates and pre-built Packages from the Coalesce Marketplace to reduce manual work and development time.

![](https://dagster.io/webflow-assets/1b42f48eabfc11fc9de6.avif)

#### 5. Fivetran

![](https://dagster.io/webflow-assets/6b8d98879ec1cbdf4d39.avif)

Fivetran is a fully managed data movement platform that automates the extraction and loading of data from almost any source to any destination. With a focus on reliability, security, and performance, it eliminates the complexity of building and maintaining pipelines, enabling teams to centralize data quickly and consistently. 

**Key features include:**

- **Automated ELT pipelines:** Handles extraction and loading from hundreds of sources to destinations with minimal configuration and ongoing maintenance.
- **Connector library:** Offers a large catalog of pre-built, managed connectors for SaaS, databases, SAP systems, and files.
- **Custom connector support:** Provides SDKs, partner-built solutions, and a request program to support niche or proprietary data sources
- **Streaming and reverse ETL:** Supports real-time data flows and syncs transformed data back into operational systems via its reverse ETL capability (powered by Census).

**Hybrid deployment:** Enables secure deployments within the customer’s own environment while maintaining Fivetran’s automation benefits.

![](https://dagster.io/webflow-assets/cc6181f247f2190c2bcd.avif)

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data engineering tools</em></strong>](https://dagster.io/guides/data-engineering-tools-types-features-and-10-essential-tools-xsde6)<strong><em> </em></strong>

### Conclusion

Data engineering services provide organizations with the expertise and infrastructure needed to turn raw data into reliable, analytics-ready assets. By combining strategy, architecture, and operational execution, these services simplify complex data environments and enable businesses to extract value from data more effectively. They ensure scalability, governance, and automation across the stack, creating a foundation for real-time analytics, AI adoption, and long-term digital transformation.

---

## Best Data Engineering Software: Top 12 Solutions in 2026

> Data engineering software includes data orchestrators like Dagster, platforms such as BigQuery and Apache Spark for processing large datasets, data streaming tools such as Apache Kafka, and data transformation tools such as dbt (Data Build Tool).

Source: https://dagster.io/learn/data-engineering-software

### What Is Data Engineering Software? 

[Data engineering](https://dagster.io/learn/data-engineering) software refers to tools and platforms to manage the collection, transformation, storage, and delivery of data across an organization. These solutions enable businesses to gather data from various sources, process it efficiently, and store it in formats appropriate for downstream analytics, reporting, or machine learning tasks. The software aims to automate and simplify data flow, reduce human errors, and ensure scalability.

Data engineering software includes data orchestrators like Dagster, platforms such as BigQuery and Apache Spark for processing large datasets, data streaming tools such as Apache Kafka, and data transformation tools such as dbt (Data Build Tool).

Modern data engineering software supports complex data pipelines, integrates well with existing systems, and provides interfaces for monitoring and debugging workflows. It aids in making data accessible, consistent, and reliable.

### Data Engineering Software vs. Data Engineering Frameworks 

Data engineering software and frameworks serve different but related purposes. **Software platforms** are full-featured products designed to cover the end-to-end data lifecycle. They typically include graphical interfaces, built-in connectors, monitoring tools, and governance features, making them suitable for enterprise adoption. Examples include Apache NiFi or Prefect, which provide ready-to-use orchestration, security, and management capabilities.

**Frameworks** are lower-level libraries or toolkits that give engineers building blocks to implement custom solutions. They provide APIs, abstractions, and runtime environments rather than complete packaged workflows. Apache Spark and Hadoop are examples: they handle distributed data processing but require additional setup, integration, and orchestration to become production-ready.

Software platforms often embed or extend frameworks. For example, a data engineering tool may use Spark under the hood for transformations but expose higher-level controls for scheduling, error handling, and monitoring. Choosing between the two depends on whether an organization prioritizes flexibility and customization (frameworks) or operational simplicity and enterprise support (software).

### Core Functions of Data Engineering Software 

#### Data Ingestion and Integration

Data ingestion is the process of collecting and importing data from various sources, such as databases, APIs, files, and streaming services, into a centralized system. Data engineering software must handle a wide range of data formats and protocols to support structured, semi-structured, and unstructured data. Integration capabilities are essential to connect disparate systems, enabling flow between different platforms, cloud environments, and data warehouses.

Effective data integration ensures that data from multiple sources is made available in a unified format. This usually involves processes like cleaning, deduplication, and schema mapping. Without ingestion and integration, organizations risk inconsistencies and fragmented views of their data, which can hinder analysis and disrupt operations.

#### Transformation and Enrichment

Once data is ingested, it often requires transformation to fit analytical or business requirements. This can involve formatting, normalizing, aggregating, or joining datasets from different sources. Data engineering software provides transformation features through code, visual interfaces, or built-in functions, allowing organizations to customize the flow and structure of their data pipelines as needed.

Enrichment processes add context or value to raw data by merging external information, performing lookups, or deriving new attributes. This step is vital for delivering high-quality, ready-to-use datasets, improving the accuracy of analytics and downstream applications. Transformation and enrichment functions enable consistent, clean, and insightful data delivery.

#### Storage and Retrieval

Data engineering software manages persistent storage solutions that support efficient data retrieval and durability. These systems can range from cloud object storage and distributed file systems to NoSQL and relational databases. Choosing the right storage depends on access patterns, scalability needs, and the requirements of downstream consumers.

Efficient storage strategies ensure data is available, resilient, and structured for easy retrieval. The software supports indexing, partitioning, and caching mechanisms that optimize query performance and minimize latency. Storage and fast retrieval are critical for meeting service-level agreements and supporting diverse analytics workloads.

#### Orchestration and Workflow Management

Orchestration refers to the automated coordination of tasks and dependencies within data pipelines. Data engineering software provides workflow management tools that define task sequences, schedule executions, and monitor progress. These tools handle error recovery, retries, and dependency resolution, ensuring pipeline reliability without constant manual intervention.

Workflow management solutions offer visual interfaces or configuration-driven designs for building, configuring, and maintaining complex pipelines. They help teams track lineage, understand process bottlenecks, and debug failures efficiently. 

#### Monitoring and Governance

Monitoring capabilities in data engineering software provide real-time visibility into pipeline health, performance metrics, failure alerts, and resource utilization. This enables teams to identify issues proactively and ensure data flows operate within expected parameters. Granular logging and alerting are crucial for minimizing downtime and meeting compliance requirements.

Data governance features establish clear ownership, access controls, lineage tracking, and policy enforcement across datasets. These capabilities help organizations maintain data integrity, privacy, and auditability. Governance and monitoring reduce risks, support regulatory compliance, and foster trust in the organization’s data assets.

### Notable Data Engineering Software 

#### Data Orchestration

##### 1. Dagster

![Dagster logo](https://dagster.io/webflow-assets/a8ff22ef96140b51f593.webp)

Dagster is a modern data orchestration platform designed for building, testing, and operating reliable data pipelines. It takes a software engineering–first approach to data engineering, emphasizing declarative pipeline definitions, strong typing, observability, and asset-centric modeling. Dagster helps teams manage complex workflows across batch, streaming, analytics, and machine learning use cases while maintaining visibility into data quality and lineage.

Unlike traditional task-based orchestrators, Dagster centers pipelines around data assets, the tables, files, models, and datasets that teams ultimately care about, rather than just task execution order. This approach improves maintainability, testing, and cross-team collaboration.

**Key features include:**

- **Asset-centric orchestration:** Pipelines are defined as software-defined assets with explicit dependencies, enabling clear lineage and impact analysis.
- **Declarative data modeling:** Assets, schedules, and resources are defined in Python with strong typing and structured configuration.
- **Built-in observability:** The Dagster UI provides visibility into runs, logs, asset health, and automatically captured lineage.
- **Data quality checks:** Native asset checks and freshness policies help ensure reliable and trustworthy datasets.

**Dagster+ and Compass:** Dagster is available as open source and as Dagster+, a managed cloud offering with governance, alerting, and hybrid deployment support. Dagster Compass provides centralized asset discovery, ownership tracking, and health visibility across teams.

![Key features of Dagster](https://dagster.io/webflow-assets/04a3a39c523f3620a78b.avif)

##### 2. Prefect

![Prefect logo](https://dagster.io/webflow-assets/abaae7729d9f929006c6.webp)

Prefect is a workflow orchestration tool to simplify and scale data pipeline management with a focus on Python-native development. Unlike traditional orchestrators, Prefect emphasizes developer ergonomics and observability by allowing users to define workflows as code without the overhead of rigid DAG structures. 

**Key features include:**

- **Python-first workflow definition**: Pipelines are written in pure Python using decorators and flow functions, offering full flexibility and reusability without needing YAML or separate configuration files.
- **DAG-free execution model**: Prefect uses a task dependency graph, but allows dynamic branching, loops, and conditions that aren’t easily expressed in static DAGs.
- **Observability and logging**: Offers centralized logging, metrics, and real-time visibility into task states through both Prefect Cloud and open-source UI.
- **Fault tolerance and retries**: Supports automatic retries, caching, and state management, making workflows more resilient to failure.
- **Flexible deployment options**: Can run on local machines, in Kubernetes, on cloud VMs, or as a managed service via Prefect Cloud, depending on infrastructure needs.

##### 3. Apache Airflow

![Airflow logo](https://dagster.io/webflow-assets/988feb5b4a7d1fa14444.webp)

Apache Airflow is an open-source workflow orchestration tool to help engineers programmatically author, schedule, and monitor data pipelines. Built in Python, it allows developers to define workflows as code using directed acyclic graphs (DAGs). Each DAG represents a pipeline where tasks are nodes and dependencies are edges. 

**Key features include:**

- **Python-based configuration:** Workflows are defined using Python scripts, enabling dynamic pipeline creation and reuse of existing libraries and logic.
- **DAG-centric design:** Uses DAGs to represent workflows, allowing precise control over task execution order and dependency resolution.
- **Flexible scheduling:** Supports both time-based scheduling (e.g. hourly, daily) and event-driven triggers (e.g. file arrival).
- **Visual monitoring interface:** Offers a web UI to track task execution, view logs, and monitor pipeline health.

**Pluggable architecture:** Integrates with custom operators, sensors, and hooks for working with external systems and services.

![Key features of Airflow](https://dagster.io/webflow-assets/27421ece37fb25dba3fa.avif)

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data orchestration</em></strong>](https://dagster.io/guides/data-orchestration-tools-10-key-features-10-platforms-to-know)

#### Big Data Processing

##### 4. BigQuery

![Big Query logo](https://dagster.io/webflow-assets/e0922bac69bb8600cc40.webp)

BigQuery is a serverless data warehouse and analytics platform designed to support large-scale processing, machine learning, and real-time analytics within a unified environment. It enables SQL-based querying, integrates with open formats and frameworks like Spark and Apache Iceberg, and includes built-in support for AI and data governance.

**Key features:**

- **AI-integrated SQL workflows**: Run ML models, generate embeddings, and perform tasks like sentiment analysis using native SQL commands, without moving data.
- **Serverless Spark support**: Execute Spark jobs alongside SQL in a unified runtime, simplifying hybrid processing across batch and stream workloads.
- **Built-in governance**: Features like metadata harvesting, lineage tracking, and semantic search are integrated through Dataplex Universal Catalog.
- **Streaming and batch ingestion**: Supports data ingestion via batch loads, Pub/Sub streams, and change data capture with Datastream.

**Scalable and decoupled architecture**: Separates storage from compute, allowing cost-efficient, petabyte-scale analysis with auto-scaling and cross-region replication.

![Key features of Big Query](https://dagster.io/webflow-assets/5f541388b435bbf35971.avif)

##### 5. Apache Spark

![Spark logo](https://dagster.io/webflow-assets/3815383f309cdba6888b.webp)

Apache Spark is a distributed processing engine designed for large-scale data analytics, supporting batch, streaming, SQL, and machine learning workloads in a unified architecture. It works across clusters or single-node setups and is accessible via languages like Python, SQL, Scala, Java, and R.

**Key features:**

- **Unified batch and streaming processing**: Processes real-time and historical data using the same engine and APIs.
- **High-performance SQL**: Executes ANSI-compliant SQL queries with distributed processing for ad hoc and dashboard-style analytics.
- **Scalable machine learning**: Trains models on small datasets and scales seamlessly to cluster-based processing for larger workloads.
- **Adaptive query execution**: Dynamically adjusts execution plans for optimal performance at runtime, including join strategies and reducer settings.

**Multi-language support**: Offers native APIs in multiple languages, enabling flexible development and integration with various ecosystems.

![Key features of Apache Spark](https://dagster.io/webflow-assets/ede6ada669be256343e6.avif)

##### 6. Apache Flink

![Flink logo](https://dagster.io/webflow-assets/6a21441e846e5c0d6ac4.webp)

Apache Flink is a framework for building stateful stream and batch data processing applications. It supports low-latency, high-throughput operations and provides consistency guarantees, making it suitable for real-time analytics, ETL, and event-driven systems.

**Key features:**

- **Stateful stream processing**: Handles unbounded data streams with exactly-once consistency and event-time semantics.
- **Layered APIs**: Offers multiple abstraction levels including SQL, DataStream API, and ProcessFunctions for fine-grained time and state control.
- **Scalable architecture**: Supports large application state and scaling through features like incremental checkpoints and savepoints.
- **Operational flexibility**: Deploys on various cluster environments with high-availability setups and flexible configuration.

**In-memory performance**: Designed for low-latency and high-throughput workloads, using in-memory computing for faster execution.

![Key features of Flink](https://dagster.io/webflow-assets/6e206e8ca5691bea48f3.avif)

#### Data Streaming

##### 7. Apache Kafka

![Kafka logo](https://dagster.io/webflow-assets/7aa4aafdf093d3c06f38.webp)

Apache Kafka is a distributed event streaming platform designed for building high-throughput, scalable, and fault-tolerant data pipelines. It supports mission-critical applications by ingesting, storing, and processing massive volumes of event data across clusters. Kafka includes native stream processing capabilities and integrates with data systems through Kafka Connect.

**Key features:**

- **High throughput and low latency**: Delivers millions of messages per second with latencies as low as 2ms across large clusters.
- **Elastic scalability**: Supports scaling to thousands of brokers and petabytes of data, with dynamic expansion and contraction of resources.
- **Durable, fault-tolerant storage**: Ensures persistent storage of event streams using a distributed, replicated log.
- **Built-in stream processing**: Enables operations like filtering, joins, and aggregations with event-time semantics and exactly-once guarantees.
- **Extensive integrations**: Connects to a wide ecosystem of sources and sinks via Kafka Connect, including databases, cloud storage, and search engines.

##### 8. Apache Pulsar

![Pulsar logo](https://dagster.io/webflow-assets/41ab3edbbe240e085710.webp)

Apache Pulsar is a distributed messaging and streaming platform built for cloud-native environments. It offers high-performance event streaming with multi-tenancy, geo-replication, and fine-grained resource isolation. Pulsar separates compute from storage, enabling flexible scaling and topic management across large deployments.

**Key features:**

- **Low-latency messaging**: Supports both message queues and stream processing with sub-10ms latency across hundreds of nodes.
- **Geo-replication and failover**: Replicates data across regions and supports automatic client failover to ensure availability.
- **Multi-tenant architecture**: Provides isolation and access control per tenant, allowing shared clusters across organizations.
- **Dynamic scalability**: Automatically balances load and redistributes hot topics without data reshuffling.
- **Serverless processing**: Supports native Pulsar Functions for lightweight message handling in Java, Go, or Python.

##### 9. Amazon Kinesis

![Amazon Kinesis logo](https://dagster.io/webflow-assets/209028f6f7d056e03489.webp)

Amazon Kinesis is a fully managed streaming data service that enables real-time data ingestion, processing, and analysis at scale. It offers modular components for different workloads, including Kinesis Data Streams for ingesting high-volume data, Kinesis Data Firehose for delivery to destinations, and Managed Flink for real-time analytics.

**Key features:**

- **Multiple data stream types**: Includes services for video, data streams, and serverless delivery pipelines to data lakes and analytics platforms.
- **Real-time processing**: Processes and analyzes incoming data immediately without waiting for full batch completion.
- **Scalable and serverless**: Handles high-throughput workloads with no server provisioning, automatically scaling to match data volume.
- **AWS ecosystem integration**: Seamlessly connects with Amazon S3, Redshift, Lambda, and other AWS services for storage, analytics, and processing.

**Flink-based analytics**: Offers a managed Apache Flink service for building custom stream processing applications with low-latency results.

![Key features of Amazon Kinesis](https://dagster.io/webflow-assets/e03cf5af2cf608b26815.avif)

#### Data Transformation

##### 10. dbt

![dbt logo](https://dagster.io/webflow-assets/d9350151c3cb398d4a95.webp)

dbt is a data transformation platform that enables teams to turn raw data into reliable, analytics-ready datasets using just SQL. It brings software engineering practices like version control, CI/CD, and testing into analytics workflows. dbt acts as the control plane for structured data, centralizing business logic, automating workflows.

**Key features include:**

- **SQL-based transformation:** Allow analysts to build and maintain data models directly in SQL, simplifying the development process while maintaining full control over transformation logic.
- **Built-in orchestration:** Automate and schedule the execution of data pipelines, enabling smooth deployment cycles and reducing operational overhead.
- **Data observability:** Proactively monitor pipeline health with built-in testing, alerts, and metadata signals to catch issues before they impact downstream consumers.
- **Semantic layer:** Define and manage business metrics, making them accessible to different BI tools and large language models (LLMs) with consistent logic and definitions.

**Lineage and cataloging:** Visualize data lineage and explore metadata across the warehouse, improving context, documentation, and debugging workflows.

![Key features of dbt](https://dagster.io/webflow-assets/41eb0e203fdf585c52b1.avif)

##### 11. Matillion

![Matillion logo](https://dagster.io/webflow-assets/9ff0b55f3975d3310af7.webp)

Matillion is a cloud-native data transformation platform that helps teams build and manage pipelines with both code-optional and no-code workflows. It supports a range of data sources and cloud destinations, and offers tools to automate transformation, manage pipeline execution, and integrate AI capabilities. 

**Key features:**

- **Code-optional transformations**: Supports both no-code and high-code options for building ETL workflows, making it accessible to different skill levels.
- **Pipeline automation**: Automates pipeline execution, error handling, and task scheduling to reduce manual overhead and speed up delivery.
- **Wide data connectivity**: Provides pre-built connectors for systems like Salesforce, SAP, PostgreSQL, Amazon S3, and custom sources.
- **Centralized management**: Offers a single platform for visibility into all pipelines, along with monitoring and task automation.

**Security and compliance**: Built with enterprise-grade security to meet compliance needs across cloud environments.

![Key features of Matillion](https://dagster.io/webflow-assets/c140d64c5d5d91689095.webp)

##### 12. Talend

![Talend logo](https://dagster.io/webflow-assets/ab99895395552dec775d.webp)

Talend, now part of Qlik, offers a unified platform for data integration, transformation, and governance. It supports multi-modal data processing and flexible deployment across cloud, on-premises, or hybrid environments. Talend is intended to deliver trusted, analytics-ready data pipelines through visual tools, code-based options, and AI augmentation.

**Key features:**

- **Multi-modal integration**: Handles ETL, ELT, batch, real-time, and API-based data flows within a single platform.
- **Flexible transformation tooling**: Offers visual low-code interfaces and full-code support, enabling a wide range of user roles to transform data.
- **Built-in data quality and governance**: Includes features like lineage tracking, reusable data products, and policy enforcement for trust and transparency.
- **AI-augmented pipelines**: Uses AI to assist in building, optimizing, and maintaining pipelines that support analytics and machine learning workloads.

**Hybrid deployment support**: Runs in cloud, on-premises, or hybrid environments, providing flexibility for compliance and infrastructure constraints.

![Key features of Talend](https://dagster.io/webflow-assets/c90b1f16349d5779c8a6.avif)

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data engineering tools</em></strong>](https://dagster.io/guides/data-engineering-tools-types-features-and-10-essential-tools-xsde6)

### Considerations for Choosing Data Engineering Software 

Selecting the right data engineering software depends on a variety of organizational, technical, and operational factors. The following considerations can help teams evaluate tools that best fit their data architecture, use cases, and team capabilities:

- **Pipeline complexity and flexibility**: Choose tools that can handle the scale and complexity of workflows. If the pipelines involve branching logic, variable dependencies, or frequent changes, prioritize solutions with dynamic DAG support, parameterization, and modular configurations.
- **Integration ecosystem**: Ensure compatibility with existing systems: databases, cloud platforms, APIs, and third-party services. Strong integration support reduces implementation overhead and ensures data movement across tools.
- **Latency and throughput requirements**: Some use cases demand low-latency or near-real-time processing (e.g. stream analytics), while others are batch-oriented. Match the software to the performance characteristics of data workloads.
- **Observability and debugging tools**: Effective monitoring, alerting, and logging are essential for maintaining pipeline reliability. Choose tools with built-in support for visualizing data lineage, tracking task execution, and capturing failures.
- **Operational scalability**: Assess how well the software scales with growing data volumes and concurrent workloads. Look for features like horizontal scaling, distributed execution, and efficient resource management.
- **Team skill set and usability**: Consider who will be building and maintaining pipelines. Some tools are code-heavy and suitable for engineers, while others offer low-code or visual interfaces suited for broader data teams.
- **Governance and security**: Support for role-based access control, encryption, auditing, and data lineage is crucial, especially in regulated industries. Ensure the tool aligns with the organization’s security and compliance policies.
- **Cost and licensing model**: Evaluate the total cost of ownership, including licensing, cloud infrastructure, support, and maintenance. Open-source tools may reduce software costs but can increase the need for internal support.
- **Community and ecosystem support**: Tools with strong community backing, comprehensive documentation, and active development tend to evolve faster and provide better support through forums, plugins, and integrations.

### Conclusion

Data engineering software provides the building blocks to move, transform, and govern data reliably at scale. The right stack standardizes pipeline development, enforces quality controls, and automates operations, so teams can deliver trusted datasets to analytics and AI with predictable cost and performance. Choosing tools that match workload patterns, skills, and governance needs leads to simpler architectures and faster time to value.

---

## Best Data Engineering Solutions: 5 Essential Platforms in 2025

> Data engineering solutions encompass a range of services focused on building and managing the infrastructure that supports data-driven applications and analytics. These solutions involve designing, building, and maintaining systems for collecting, processing, storing, and accessing data.

Source: https://dagster.io/learn/data-engineering-solutions

### What Are Data Engineering Solutions?

[Data engineering](https://dagster.io/learn/data-engineering) solutions make it possible to build and manage the infrastructure that supports data-driven applications and analytics. These solutions involve designing, building, and maintaining systems for collecting, processing, storing, and accessing data. They are crucial for enabling businesses to leverage their data for insights, decision-making, and machine learning initiatives.

Benefits of data engineering solutions include:

- **Improved data accessibility:** Making data more readily available for various users and applications. 
- **Enhanced data quality:** Ensuring data accuracy, consistency, and reliability. 
- **Increased operational efficiency:** Automating data workflows and reducing manual effort. 
- **Faster insights and decision-making:** Enabling timely access to data and insights for informed decision-making. 
- **Scalability and flexibility:** Adapting to growing data volumes and changing business needs. 
- **Competitive advantage:** Leveraging data for innovation, improved products and services, and better customer experiences.

By integrating automation, scalability, and governance, data engineering solutions provide the backbone for building robust data pipelines. They offer standardized ways to ingest, process, and catalog data, reducing the complexity of managing disparate systems. These solutions abstract much of the operational overhead involved in maintaining data workflows.

### Core Capabilities of Modern Data Engineering Solutions 

#### Ingestion Patterns

Modern data engineering solutions support a range of ingestion patterns to accommodate diverse requirements. They enable both batch and streaming data ingestion, capturing data from transactional databases, APIs, files, and event streams. This flexibility makes it possible to process real-time events from sources like IoT devices while also handling high-volume, periodic data loads needed for traditional analytics workloads.

These solutions commonly include pre-built connectors and adapters, which simplify onboarding new data sources. Ingestion processes are designed to handle schema evolution, error handling, and backpressure scenarios, ensuring resilient pipelines. 

#### Storage Architectures

Effective data engineering relies on choosing and managing the right storage architectures. Modern solutions offer support for diverse storage paradigms, including data lakes, data warehouses, and lakehouse designs. These architectures enable organizations to separate compute and storage for cost efficiency, enable the handling of both structured and unstructured data, and ensure rapid data availability.

Storage solutions also address concerns around data durability, partitioning, and scalability. Features like versioning and snapshotting increase reliability, while automated tiering optimizes costs. Data cataloging and indexing improve discoverability, and native integrations with processing engines or BI tools further reduce friction across the analytics stack.

#### Transformation Paradigms

Transformation is at the core of data engineering, where raw data is shaped into analytics-ready formats. Modern solutions provide declarative frameworks for both batch and streaming transformations, supporting SQL-based, code-driven, or visual transformation paradigms. This flexibility allows teams to leverage familiar tools and programming models, reducing ramp-up time and promoting best practices in data modeling.

Advanced solutions also enable modular, reusable transformation steps, enabling extensive testing and lineage tracking. This modularity allows teams to orchestrate complex data flows efficiently and maintain clarity around how data is altered. Integration of quality checks and schema validation at this stage ensures downstream reliability for consumers.

#### Orchestration and Scheduling

Orchestration tools are integral to data engineering solutions, managing dependencies and scheduling data workflows across distributed environments. They coordinate jobs such as data ingestion, transformation, and data delivery, ensuring steps occur in the correct sequence and handling failure recovery scenarios. This orchestration is essential for reproducible, reliable, and efficient pipeline executions.

Scheduling capabilities enable automated, recurring pipeline runs aligned with business needs, such as hourly data refreshes or end-of-day reporting. Modern solutions include monitoring dashboards, alerting mechanisms, and logs, empowering engineers to diagnose failures, optimize performance, and manage SLAs. 

#### Metadata, Catalog, and Governance Layers Across the Stack

Metadata management and governance are critical for operating data platforms at scale. Modern data engineering solutions embed cataloging and governance layers, capturing details like dataset lineage, ownership, schema evolution, and data quality metrics. Rich metadata improves searchability, discoverability, and transparency, allowing users to quickly locate and understand available data assets.

Governance frameworks enforce data access controls, auditing, and compliance with regulations such as GDPR and HIPAA. They enable stewardship initiatives by providing automated classification, documentation, and change management features. These layers foster trust in the data, supporting broader adoption and collaboration across business and technical teams.

**Related content: Read our guide to** [**data engineering tools**](https://dagster.io/guides/data-engineering-tools-types-features-and-10-essential-tools-xsde6)

### Notable Solutions Supporting Data Engineering 

#### 1. Dagster

![](https://dagster.io/webflow-assets/bd38e0ce6114124fee73.avif)

Dagster is an open-source data orchestrator purpose-built for modern data engineering teams that need reliable, maintainable, and observable pipelines. Designed with software engineering principles at its core, Dagster helps organizations build resilient data platforms by modeling pipelines around the data they produce through software-defined assets. This asset-first approach improves clarity, quality, and long-term scalability across complex data systems.

**Key features include:**

- **Software-defined assets:** Represent data as assets with explicit upstream and downstream dependencies to enable accurate lineage, reproducible transformations, and clear visibility into how data flows across the platform.
- **Strong typing and validation:** Apply schemas, runtime checks, and typed interfaces to enforce data quality, prevent pipeline failures, and maintain consistent contracts across ingestion, processing, and analytics layers.
- **Developer-focused workflow:** Local-first development, testability, and Python-native APIs support fast iteration and robust engineering practices, making pipelines easier to build, debug, and evolve.
- **Comprehensive observability:** A modern UI provides detailed insights into lineage graphs, run histories, partitions, metadata, and asset health, enabling proactive monitoring and quicker troubleshooting.
- **Flexible orchestration model:** Support schedules, sensors, event-driven triggers, partitions, and large-scale backfills across multiple execution environments, including Kubernetes, serverless compute, and local development.

**Rich ecosystem integrations:** Seamlessly connects with warehouses, lakes, ETL and ELT tools, analytics frameworks, and ML platforms. Includes strong integrations with dbt, Snowflake, BigQuery, Spark, DuckDB, and similar tools used across enterprise data stacks.

![](https://dagster.io/webflow-assets/42b63941965f3662a688.avif)

#### 2. Databricks

![](https://dagster.io/webflow-assets/bf1e1f6a6026be2862e2.avif)

Databricks supports data engineering by providing a unified platform for building, deploying, and managing data pipelines. Its Lakeflow solution combines ingestion, transformation, orchestration, and governance into a single toolchain, helping teams simplify development and reduce operational overhead. 

**Key features include:**

- **Unified tool stack**: A single platform for ingestion, transformation, orchestration, and governance, reducing integration complexity and tool sprawl.
- **Declarative pipelines**: Simplifies ETL with built-in support for change data capture (CDC), data quality enforcement, and streaming workflows.
- **AI-assisted development**: Enables faster pipeline creation through no-code interfaces and AI-assisted code generation.
- **Optimized processing engine**: Automatically allocates compute resources for efficient execution across real-time and batch workloads.

**High observability**: Offers monitoring and logging capabilities to manage thousands of daily jobs with visibility and reliability.

![](https://dagster.io/webflow-assets/26160d07ef8c436b4f37.avif)

#### 3. Alation

![](https://dagster.io/webflow-assets/ef88388679a26c45d24c.avif)

Alation is a data intelligence platform that helps organizations make better use of metadata. It enables teams to discover, govern, and use data more effectively across the enterprise. Alation combines data cataloging, governance, and search capabilities with AI-powered agents that improve metadata accuracy and usability. 

**Key features include:**

- **Metadata-aware AI agents**: Boost data discovery and question answering accuracy by leveraging context from metadata.
- **Data cataloging**: Centralizes metadata in a searchable catalog, helping users find and understand trusted data assets.
- **Integrated data governance**: Embeds compliance and policy guidance directly into workflows, balancing governance with user productivity.
- **Self-service analytics**: Empowers users to explore data independently while maintaining control and consistency across teams.

**Trusted data products**: Enables organizations to create and maintain a marketplace of vetted, reliable data assets.

![](https://dagster.io/webflow-assets/f0f35ddd86f8764fee88.avif)

#### 4. Matillion

![](https://dagster.io/webflow-assets/3ff3147848e4c5e1a560.avif)

Matillion is a cloud-native data integration platform to accelerate the creation and management of data pipelines across the data team. It supports low-code and code-first development, enabling technical and non-technical users to collaborate effectively. 

**Key features include:**

- **Maia AI agents**: Use natural language to automate pipeline development and assist with complex data engineering tasks, scaling team productivity without adding headcount.
- **Low-code interface**: Build sophisticated pipelines visually with drag-and-drop components, reducing the time to deployment for non-developers.
- **Code-first flexibility**: Supports SQL, Python, and dbt for advanced users, with orchestration and Git integration to enable data ops practices.
- **Universal connectivity**: Ingest structured and unstructured data from virtually any source using pre-built or custom connectors.

**Cloud-native architecture**: Generates native SQL and runs workloads on platforms like Snowflake, AWS, and Databricks for better performance and scalability.

![](https://dagster.io/webflow-assets/520394067baa29bd2dce.avif)

#### 5. Apache Airflow

![](https://dagster.io/webflow-assets/b65240b55b2d614d91aa.avif)

Apache Airflow is an open-source workflow orchestration platform used to programmatically author, schedule, and monitor data engineering pipelines. Initially developed by Airbnb to manage complex internal workflows, Airflow has since become an Apache Software Foundation project. It uses Python to define workflows as code, allowing developers to build reusable and dynamic data pipelines. 

**Key features include:**

- **Python-based workflow definition**: All workflows are written in Python, enabling the use of standard programming constructs, libraries, and modular components.
- **DAG-centric orchestration**: Tasks and dependencies are managed through DAGs, providing a clear and auditable structure for complex pipelines.
- **Flexible scheduling**: Supports both time-based scheduling (e.g. hourly, daily) and event-driven execution, allowing for reactive data workflows.
- **Built-in UI and monitoring**: Offers a web interface to visualize DAGs, monitor task progress, and troubleshoot execution issues.

**Extensibility**: Supports plugins and custom operators to integrate with external systems such as databases, cloud platforms, and APIs.

![](https://dagster.io/webflow-assets/77dea9b3f9b590fc1f83.avif)

### Conclusion

Data engineering solutions form the foundation of modern data-driven organizations. They provide the capabilities needed to ingest, store, process, and govern data reliably at scale. By standardizing workflows and enabling automation, they reduce complexity and operational overhead while improving data quality and accessibility. The result is a more agile data infrastructure that supports real-time analytics, advanced modeling, and machine learning, ultimately allowing businesses to make faster, more confident decisions.

---

## Best Data Pipeline Tools: Top 6 Options in 2026

> Data pipeline tools automate the process of collecting, moving, transforming, and loading data from various sources into a single destination for analysis, including popular options like Prefect for orchestration, Fivetran and Airbyte for data integration, and Kafka Connect for real-time streaming.

Source: https://dagster.io/learn/data-pipeline-tools

### What Are Data Pipeline Tools? 

[Data pipeline](https://dagster.io/learn/data-pipeline) tools automate the process of collecting, moving, transforming, and loading data from various sources into a single destination for analysis. These tools maintain data consistency and integrity, especially as organizations scale and data ecosystems increase in complexity.

Efficient data pipeline tools help teams orchestrate workflows, schedule recurring jobs, and manage dependencies across multiple data processing steps. By abstracting away the complexities involved in building and maintaining custom integrations and transformation logic, data pipeline tools enable data engineers and analysts to focus on analytics and strategic initiatives rather than infrastructure concerns.

### Types of Data Pipeline Tools 

Data pipeline tools can be classified into the following types, although these often overlap.

#### Cloud-Native Solutions

Cloud-native data pipeline tools are services built to leverage the scalability, reliability, and elasticity of cloud infrastructure. These solutions are typically managed by cloud providers and offer integration with the provider’s ecosystem, such as storage, compute, and analytics services. They are built for high availability and automatic scaling, which lets organizations handle variable or growing data volumes without manual intervention.

These tools often offer a pay-as-you-go pricing model and minimal administrative overhead, making them a popular choice for organizations prioritizing flexibility and quick deployment. However, this convenience may come with a trade-off in customization and control compared to open-source or on-premises solutions. Data sovereignty considerations are also a factor, as organizations must be mindful of where and how data is processed and stored.

#### Open-Source Frameworks

Open-source data pipeline frameworks offer transparency, flexibility, and community-driven support. Common examples like Apache Airflow and Apache NiFi provide workflow orchestration, scheduling, and extensibility through plugins or custom code. These frameworks can be deployed on-premises or in the cloud, allowing organizations full control over configuration and security.

Adopting open-source tools can reduce licensing costs and avoid vendor lock-in. However, they typically require engineering expertise for setup, monitoring, and maintenance. Updates, debugging, and scaling are also the team’s responsibility, which can increase operational complexity compared to fully-managed solutions.

<strong><em>Learn more in our detailed guide to </em></strong>[<strong><em>data pipeline framework</em></strong>](https://dagster.io/guides/data-pipeline-frameworks-key-features-10-tools-to-know-in-2025)<strong><em> </em></strong>

#### Low-Code Solutions

Low-code data pipeline tools simplify data integration and processing by offering graphical interfaces, prebuilt connectors, and drag-and-drop workflow design. These platforms allow business analysts and non-technical users to configure and deploy pipelines without needing to write extensive code, accelerating development cycles and reducing dependency on specialized data engineering talent.

While low-code tools are accessible and easy to use, they might offer limited flexibility for highly customized or complex transformation logic. Organizations must evaluate whether the tool’s abstraction meets their data needs or if it imposes constraints on integration, scalability, or automation.

#### Enterprise-Grade Platforms

Enterprise-grade data pipeline platforms cater to large-scale, complex data environments with strict requirements for security, compliance, and integration. These solutions provide features for governance, auditing, role-based access control, and pipeline versioning. They often include support for hybrid or multi-cloud deployments, ensuring operational flexibility to meet varying regulatory and business needs.

Implementing enterprise platforms frequently involves higher upfront costs, vendor contracts, and sometimes bespoke integrations. In return, organizations benefit from mature support, reliability SLAs, advanced analytics, and integration with a broad range of enterprise systems. These platforms are favored by organizations that need assurances around uptime, compliance, and service continuity.

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data pipelines architecture</em></strong>](https://dagster.io/guides/data-pipeline-architecture-5-design-patterns-with-examples)

### Key Features to Look for in Data Pipeline Tools 

#### Scalability and Performance

Data pipelines must handle growing volumes, velocity, and variety of data as business needs evolve. Scalability means the tool can accommodate higher throughput by adding resources or balancing workloads across distributed systems. This includes the ability to process data in batches, real time, or both, depending on analytic requirements.

Performance is equally critical. Tools should efficiently process data with low latency, handle peak loads, and avoid bottlenecks that could delay insights. Evaluate whether the platform supports parallel processing, resource elasticity, and workload isolation, as these directly affect the reliability and speed of data operations under changing demands.

#### Monitoring, Alerting, and Logging

Robust monitoring capabilities are crucial to track pipeline health, data movement, and system resource utilization in real time. Effective alerting mechanisms notify teams of failures, slowdowns, or data quality issues before they impact downstream applications or decision-making.

Comprehensive logging provides actionable information for troubleshooting, auditing, and performance optimization. Structured logs allow engineers to analyze historical trends, debug errors, and demonstrate compliance for regulatory requirements. Together, these features help ensure consistent and uninterrupted data processing.

#### Data Quality and Reliability

Ensuring data quality requires built-in validation, cleaning, and enrichment capabilities at each stage of the pipeline. Tools should provide mechanisms to detect, report, and recover from missing or corrupt data, so analytics and reporting remain accurate and trustworthy.

Reliability involves designing for automatic retries, checkpointing, and idempotent operations to prevent data loss or duplication. Pipeline tools that support versioning and rollback of jobs or configurations further enhance the reliability of your data delivery processes. Look for tools with proven track records of uptime and consistency in production environments.

#### Security and Compliance

Data pipeline tools should incorporate granular security features such as encryption in transit and at rest, secure credential management, and fine-grained access controls. This is particularly important when handling personally identifiable information (PII), financial data, or other sensitive records.

Compliance is non-negotiable for organizations operating under regulations like GDPR, HIPAA, or SOX. Evaluate whether the platform supports auditing, logging, data retention policies, and region-specific deployment options to ensure both legal and industry-specific requirements are consistently met.

#### Cost Optimization

Cost optimization features help organizations control spending while scaling data infrastructure. Tools that provide usage-based billing, detailed cost reporting, and auto-scaling capabilities allow better budgeting and flexibility as workloads fluctuate over time.

Additionally, the ability to optimize resource allocation, such as shutting down idle services or leveraging spot instances, ensures you aren’t overpaying for underutilized capacity. Systems that offer tiered storage options and granular cost forecasts make it easier to plan for future growth without risk of bill shock.

### Notable Data Pipeline Tools

#### 1. Fivetran

![](https://dagster.io/webflow-assets/c376237a9b774d68ec82.webp)

Fivetran is a managed data movement platform that simplifies and automates the process of ingesting data from hundreds of sources into destinations like data warehouses and lakes. Fivetran eliminates the need for manual ETL development by offering prebuilt connectors, managed pipeline infrastructure, and automated schema handling. 

**Key features include:**

- **700+ prebuilt connectors**: Supports a wide range of data sources including SaaS apps, databases, ERPs, and cloud storage platforms.
- **Fully managed pipelines**: Automates schema mapping, error handling, and maintenance, reducing the need for manual intervention.
- **Data lake integration**: Offers a managed service to land standardized, governed data directly into lakes using query-ready open table formats.
- **Custom connector framework**: Enables organizations to build and deploy their own connectors when unique integration needs arise.

**Enterprise-grade security**: Certified for SOC 1 & 2, ISO 27001, GDPR, HIPAA, PCI DSS, and HITRUST compliance.

![](https://dagster.io/webflow-assets/8db18321241050a724d9.webp)

#### 2. Airbyte

![](https://dagster.io/webflow-assets/f53f99553c261a1c550f.webp)

Airbyte is an open-source ELT platform to simplify and accelerate the movement of data from any source to any destination. With over 600 prebuilt connectors and the ability to create custom ones using a no-code builder or code development kit, Airbyte offers a fast and flexible way to build data pipelines. 

**Key features include:**

- **600+ prebuilt connectors**: Access the largest open-source connector marketplace to extract data from SaaS apps, databases, APIs, and more.
- **No-code and code-based connector development**: Use the no-code builder or developer tools like CDK and PyAirbyte to build new connectors quickly.
- **Self-hosted and customizable**: Deploy on your own infrastructure for full control over security, compliance, and performance.
- **Integration with orchestration tools**: Seamlessly works with Airflow, Dagster, and Prefect to fit into existing pipeline orchestration stacks.

**Infrastructure-as-code support**: Manage and automate configuration through a robust API or Terraform provider using YAML files.

![](https://dagster.io/webflow-assets/c7f871d8208972123061.webp)

#### 3. Kafka Connect

![](https://dagster.io/webflow-assets/c7d898a18a869289d3f3.webp)

Kafka Connect is an open-source component of Apache Kafka for streaming data between Kafka and external systems. It defines connectors that ingest entire databases or collect metrics into Kafka topics with low latency, and can export topics to indexes or batch systems. Deployments run standalone for simple jobs or in distributed mode for scalability and fault tolerance. 

**Key features include:**

- **Source and sink connectors:** Stream data from databases, apps, and files into Kafka topics, or deliver Kafka data to indexes and batch systems.
- **Standalone and distributed deployment:** Run on a single node for development, or across workers with automatic scaling, coordination, rebalancing, and fault tolerance.
- **Tasks and workers model:** Connectors coordinate parallel tasks, rebalance work across workers, and store task configuration and status in Kafka-managed internal topics.
- **Converters and formats:** Pluggable converters translate between bytes and structured data, supporting Avro, Protobuf, JSON Schema, raw JSON, strings, and byte arrays.
- **Single message transforms:** Apply chained, lightweight per-record modifications and routing within connectors; use external stream processing for complex, multi-record operations.

**Dead letter queues:** Configure sink connectors to route invalid records to a DLQ topic, with optional context headers for diagnostics in secure environments.

![](https://dagster.io/webflow-assets/e22ee249564a3c53e1f0.webp)

#### 4. Prefect

![](https://dagster.io/webflow-assets/67e94308aa58d844e33f.avif)

Prefect is an open-source workflow orchestration tool that helps data teams build, run, and monitor reliable data pipelines using python. Prefect turns fragile ETL processes into version-controlled workflows with features like retries, scheduling, logging, and state tracking. It integrates natively with tools like dbt, data warehouses, and BI platforms.

**Key features include:**

- **Python-native orchestration**: Define workflows using python code with full access to version control, libraries, and custom logic.
- **Task-level control**: Supports retries, dependencies, mapping, and conditional logic for building modular, fault-tolerant pipelines.
- **dbt integration**: Run dbt transformations via native support for both dbt Cloud and CLI-based workflows.
- **Self-service deployment**: Enables quick development and deployment of data pipelines with minimal operational overhead.

**Data quality and SLAs**: Automates alerts, failure notifications, and SLA tracking to ensure data reliability and timely delivery.

![](https://dagster.io/webflow-assets/a7c2c2e7610d81527ac5.webp)

#### 5. Stitch

![](https://dagster.io/webflow-assets/f34b12fe95b2ef3d8260.webp)

Stitch is a cloud-first ELT platform that helps teams quickly and securely move data from over 130 sources into cloud data warehouses, lakes, or lakehouses. Now part of Qlik, Stitch’s capabilities are being integrated into the Qlik Talend Cloud, combining its ease of use and reliability with a broader set of enterprise features. 

**Key features include:**

- **130+ data source connectors**: Supports popular platforms like Salesforce, Google Analytics, HubSpot, Shopify, and TikTok for fast data extraction.
- **No-code setup**: Configure pipelines once through a user-friendly interface, then monitor and control without writing custom code.
- **Automated ELT pipelines**: Handles data extraction, loading, and optional transformations with minimal manual intervention.
- **Secure and compliant**: Ensures data integrity and regulatory compliance with secure pipeline infrastructure.

**Fast time to value**: Enables teams to start syncing data in minutes, reducing setup time and accelerating insight delivery.

![](https://dagster.io/webflow-assets/747836861f917d3ba109.webp)

#### 6. Hevo Data

![](https://dagster.io/webflow-assets/31c79312324d1cafac64.webp)

Hevo Data is a fully managed ELT platform designed to move data securely from a range of sources to cloud data warehouses. With built-in automation, high throughput, and intelligent error handling, Hevo enables engineering and analytics teams to deliver analytics-ready data at scale without writing complex code or managing infrastructure.

**Key features include:**

- **Automated ELT pipelines**: Orchestrates end-to-end data movement with no-code configuration, ensuring up-to-date, analysis-ready data.
- **High-throughput change data capture (CDC)**: Enables low-latency replication from operational databases without impacting source performance.
- **Schema drift handling**: Detects and adapts to schema changes automatically, adding new columns or tables without breaking the pipeline.
- **Custom connector builder**: Allows integration with internal systems and third-party tools using REST APIs or webhooks via an intuitive interface.

**Intelligent error recovery**: Features automatic retries, checkpointing, and error detection to ensure reliability even during transient failures.

![](https://dagster.io/webflow-assets/f92f4de7bab4916bc289.webp)

### Conclusion

Data pipeline tools are essential for modern data infrastructure, enabling efficient movement, transformation, and delivery of data across increasingly complex environments. By automating core ETL/ELT processes, these tools reduce manual workload, ensure data consistency, and improve pipeline reliability. They support diverse use cases, from real-time analytics to large-scale batch processing, while offering flexibility, scalability, and integration with broader data ecosystems.

---

## Big Data Pipelines: 6 Key Components, Challenges & Best Practices

> A big data pipeline is a system that automates the process of collecting, processing, and storing large volumes of data from various sources. It transforms raw data into a usable format for analysis and decision-making, ensuring scalability and efficient data flow.

Source: https://dagster.io/learn/big-data-pipelines

### What Is a Big Data Pipeline? 

A big [data pipeline](https://dagster.io/learn/data-pipeline) is a system that automates the process of collecting, processing, and storing large volumes of data from various sources. It transforms raw data into a usable format for analysis and decision-making, ensuring scalability and efficient data flow.

The components of a big data pipeline handle data ingestion, validation, transformation, storage, and eventual delivery for analytics or downstream consumption. By orchestrating the movement and processing of data, big data pipelines enable organizations to derive actionable insights from raw logs, transaction records, IoT sensor streams, or other data sources. 

Modern pipelines often operate continuously and at scale, supporting business-critical use cases like personalization, predictive analytics, fraud detection, and more. As data volumes and complexity grow, the reliability and scalability of these pipelines become crucial for organizations striving to become data-driven.

### Benefits of Big Data Pipeline 

Big data pipelines aid in enabling scalable data processing. By automating and optimizing how data is handled from ingestion to output, they provide foundational support for analytics and data-driven decision-making.

Key benefits include:

- **Scalability:** Handles growing volumes and velocities of data without performance bottlenecks.
- **Automation:** Reduces manual data handling through automated ingestion, transformation, and delivery.
- **Real-time processing:** Supports time-sensitive use cases by enabling near real-time analytics and decision-making.
- **Data quality enforcement:** Integrates validation and cleansing steps to ensure accuracy and consistency of data.
- **Flexibility:** Works across diverse data types and sources, including structured, semi-structured, and unstructured data.
- **Cost efficiency:** Optimizes resource usage by using distributed computing and storage technologies.
- **Resilience and fault tolerance:** Built with mechanisms to handle failures gracefully and recover without data loss.
- **Support for advanced analytics:** Enables machine learning, business intelligence, and complex event processing by delivering ready-to-use data.
- **Improved data governance:** Enables data lineage, auditing, and compliance through structured processing workflows.

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data pipelines architecture</em></strong>](https://dagster.io/guides/data-pipeline-architecture-5-design-patterns-with-examples)

### Core Components of a Big Data Pipeline 

#### 1. Data Ingestion

Data ingestion is the initial step in a big data pipeline, responsible for capturing data from a variety of sources. This process may involve batch loading of files from external databases, log files, or cloud environments, as well as real-time streaming from APIs, sensors, or messaging systems. Robust ingestion systems handle diverse data formats and protocols while ensuring high availability and minimal data loss during transfer. The goal is to reliably funnel raw data into the pipeline without bottlenecks, regardless of source or volume.

Effective data ingestion platforms must scale horizontally to match increases in incoming data while providing support for error handling and automatic retries. They must also support metadata capture and basic schema validation to ease downstream processing. Ingestion can be performed via open-source tools like Apache Kafka, Apache NiFi, or proprietary managed services on major cloud platforms.

#### 2. Data Validation

Data validation ensures that input data meets defined quality standards before moving further through the pipeline. This stage involves checking for completeness, accuracy, and conformity to expected formats or schemas. Automated validation rules can catch malformed records, missing values, and data type mismatches early in the process, reducing downstream errors and saving processing costs. 

Implementing a validation layer requires a balance between thoroughness and performance. Overly strict validation can introduce latency or reject useful records, while lenient validation may let errors propagate into analytics outputs. Validation frameworks can be configured to send alerts on critical issues or quarantine suspect records for review. 

#### 3. Data Processing and Transformation

Once data passes validation, it undergoes processing and transformation to become more usable for analysis or machine learning. This includes tasks like filtering irrelevant records, joining datasets, normalizing, aggregating, or enriching data with additional context. Processing can occur in batch mode, where large volumes are handled at set intervals, or in real-time as data streams in, depending on business needs. Frameworks like Apache Spark, Flink, or Google Dataflow enable complex, distributed processing pipelines that scale with data size.

Transformation is often tailored to target use cases, such as producing summary dashboards, building training datasets, or structuring information for downstream applications. Efficiency and reliability matter at this stage, as poorly designed transformations can slow down the pipeline or introduce errors. 

#### 4. Data Storage

After processing, data needs to be stored in systems optimized for its size, structure, and access patterns. Storage solutions range from data lakes capable of holding raw, semi-structured, and structured data at petabyte scale to high-performance data warehouses tailored for fast analytical queries. 

Technologies like Amazon S3, Google Cloud Storage, Hadoop HDFS, and Snowflake offer diverse options for storing processed or raw data. Effective storage design addresses data partitioning, compression, and indexing to optimize both cost and retrieval speed. Access controls and encryption help organizations maintain security and comply with data protection regulations. 

#### 5. Data Serving

Data serving delivers processed data to consumers: analysts, applications, or business intelligence (BI) platforms. This layer transforms query requests into fast, reliable responses by leveraging optimized storage backends and caching mechanisms. Serving could be as simple as providing an API endpoint for real-time results, or as robust as powering interactive dashboards and embedded analytics within applications. 

Technologies like Elasticsearch, Druid, and data warehouses are often used for high-performance data serving. Operations teams should design serving layers to meet specific latency and concurrency requirements. For example, supporting end-user dashboards with low-latency queries may require specialized storage formats or in-memory datastores. 

#### 6. Monitoring, Logging, and Alerting

Continuous monitoring, logging, and alerting are essential for maintaining the health and reliability of big data pipelines. Monitoring systems track key metrics like data volumes, processing times, error rates, and resource utilization, providing early visibility into potential problems. Properly instrumented pipelines generate detailed logs that can be analyzed for root cause analysis and auditing purposes. 

Automated alerts notify operators of failures, performance bottlenecks, or data quality issues, allowing for rapid intervention. Metrics dashboards and log aggregators using tools such as Grafana, Prometheus, or cloud-native solutions help visualize trends, set thresholds, and trigger escalations as needed. 

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data pipeline framework</em></strong>](https://dagster.io/guides/data-pipeline-frameworks-key-features-10-tools-to-know-in-2025)

### Big Data Pipelines vs. ETL 

While the traditional Extract, Transform, Load (ETL) process and big data pipelines share some conceptual similarities, they differ significantly in architecture, scalability, and use cases. 

**ETL** usually involves scheduled batch operations for periodic updates to centralized relational databases or data warehouses. The process expects well-structured data sources, predefined schemas, and relatively predictable workloads, making it less suitable for handling the velocity and variety of modern big data.

**Big data pipelines** are designed for much greater scale and flexibility. They can handle both structured and unstructured data, accommodate real-time streams alongside batch jobs, and adapt to evolving schema requirements. Modern pipelines also emphasize automation, modularity, and observability, providing the speed and reliability required for real-time analytics, machine learning, and operational AI workloads. As data environments become more complex, the need for robust, end-to-end pipelines outpaces the capabilities of traditional ETL processes.

### Big Data Pipelines Challenges

#### Dealing with High Data Volumes

Handling massive and ever-increasing data volumes is a foundational challenge for big data pipelines. Systems must scale horizontally and gracefully, accommodating spikes in traffic, emerging data sources, or growing user demands. This requires careful selection of scalable ingestion, processing, storage, and serving technologies, with the ability to add resources on demand without outages or performance drops.

Beyond infrastructure scaling, managing high data volumes means monitoring bandwidth, ensuring timely processing, and archiving or purging aged data to control costs. Teams must also consider data sharding, replication, and backup strategies to ensure redundancy and reliability. 

#### Latency vs Throughput Trade-Off

Every big data pipeline faces trade-offs between latency (the speed at which data is processed and made available) and throughput, the volume of data handled per unit of time. Low-latency systems are designed for real-time analytics and fast decision-making but often require specialized, more costly hardware and optimized code paths. 

Deciding which to prioritize typically depends on business requirements. For instance, fraud detection systems may value milli- or microsecond-level latencies, while traditional reporting can process overnight batches. Optimizing for both metrics without sacrificing reliability requires a thoughtful blend of stream processing frameworks, parallel computing, and queuing systems. 

#### Cost Management

Managing costs in big data pipelines involves controlling both infrastructure and operational outlays. Storing, moving, and processing large-scale data across distributed systems can incur significant expenses, especially as workloads scale. Cost efficiency requires teams to architect pipelines with right-sized compute instances, managed storage lifecycles, and autoscaling policies that prevent underutilization or overprovisioning of resources. 

Operational costs can also grow with pipeline complexity. Open-source tools can save on licensing but often demand greater in-house maintenance and expertise. Managed cloud services simplify operations but introduce ongoing subscription expenses, so choosing technologies requires weighing total cost of ownership. 

#### Data Quality, Consistency, Schema Drift

Maintaining high data quality and consistency is another core challenge in big data pipelines. As data sources diversify and evolve, the risk of schema drift, when datasets change structure or semantics over time, grows, often breaking downstream processes or analytical models. 

Pipelines must include robust validation, schema enforcement, and migration frameworks to catch and adapt to changes quickly. Without these controls, unreliable data erodes confidence and impacts business outcomes. Consistency issues arise from distributed processing, out-of-order arrival, or temporary failures, which can introduce duplicates, missing values, or contradictory records. 

### Best Practices for Building Robust Big Data Pipelines 

Here are some of the ways that organizations can improve the effectiveness of their big data pipelines.

#### 1. Keep Pipelines Modular, Testable, Maintainable

Designing pipelines as collections of modular, loosely-coupled components simplifies both development and long-term maintenance. Each module, responsible for tasks like ingestion, validation, transformation, or serving, should have clear inputs, outputs, and error handling boundaries. 

This approach makes it easier to update, scale, or replace individual parts without disrupting the entire system. Modularity also encourages code reuse and sharing of best practices across teams or projects. Teams should implement automated unit and integration tests within each module, as well as end-to-end pipeline validation, to catch defects before they impact production. 

#### 2. Track and Maintain Data Lineage

Maintaining comprehensive data lineage tracks the origin, movement, and transformation of every dataset within a pipeline. This enables teams to diagnose errors, perform audits, and demonstrate compliance with data regulations. Data lineage tools automatically capture metadata as records move through ingestion, processing, and storage stages, providing a map of how data evolves and which systems or processes impact it.

Effective lineage tracking helps organizations recover from failures or rollbacks by revealing which downstream outputs may require attention. It also builds confidence among users and auditors, as clear lineage documentation demonstrates that data integrity and security requirements are met. 

#### 3. Implement Strong Data Quality Controls

Strong data quality controls are essential for preventing corrupt or incomplete data from polluting analytics, dashboards, or machine learning models. Pipelines should incorporate robust validation, deduplication, and anomaly detection mechanisms at both ingestion and processing stages. Automated quality checks identify errors early, quarantining suspect records and raising alerts for teams to act on.

Organizations should define clear quality standards and continuously measure pipeline outputs for compliance with those benchmarks. This may involve setting thresholds for missing value rates, implementation of automatic schema updates, or monitoring statistics for outliers and unexpected trends. Regular review and refinement of quality controls help address new sources of risk as data sources and business requirements evolve.

#### 4. Optimize for Cost and Resource Efficiency

Optimizing pipelines for cost and resource efficiency includes right-sizing compute, storage, and throughput to match workload patterns. Teams should monitor resource usage at every stage and employ autoscaling or serverless options when appropriate to minimize over-provisioning. Data lifecycle management, like automated purging or archiving, reduces unnecessary storage costs.

Compression and partitioning accelerate queries and lower transfer expenses. It is also important to evaluate technology choices periodically to ensure ongoing alignment with business needs and budgets. Cloud pricing models, open-source licensing changes, and new architectures can all impact long-term cost. 

#### 5. Ensure Security and Regulatory Compliance

Security and compliance must be integrated throughout the design and operation of big data pipelines. Encryption of data at rest and in transit prevents unauthorized access, while fine-grained access controls restrict data visibility to only those requiring it. Auditing and monitoring systems record access attempts and modifications to enable incident investigation or compliance reporting.

Pipelines must support compliance requirements such as GDPR, HIPAA, or industry-specific mandates. This often means implementing data retention, masking, or pseudonymization practices and ensuring that all data movement is documented and auditable. Collaboration with legal and compliance teams ensures controls are current.

#### 6. Enable Self-Service for Data Teams

Enabling self-service across big data pipelines empowers data scientists, analysts, and developers to access, process, and analyze data without excessive dependencies on engineering or IT. This involves providing friendly interfaces, reusable pipeline components, and cataloged datasets that simplify experimentation and innovation. 

Modern data platforms may include notebook integrations, REST APIs, or low-code interfaces to support self-service workflows. A strong self-service approach also features robust access governance, user training, and clear documentation. By reducing bottlenecks and handover steps, organizations accelerate time-to-insight and foster data-driven experimentation. 

### Powering Big Data Pipelines with Dagster

Dagster is a modern data orchestration platform that helps teams build and operate reliable big data pipelines. It provides a unified framework for managing ingestion, processing, quality checks, and delivery, making complex workflows easier to develop and maintain.

With Dagster’s software defined assets model, teams express data dependencies as code, which creates clear lineage and improves governance. This visibility helps engineers and analysts understand how data is produced, how it changes, and which downstream outputs are affected by any given update.

Dagster includes strong data quality features. Users can define expectations that validate datasets during processing, allowing issues to surface early. Rich metadata and historical context support debugging, auditing, and continuous improvement.

The orchestration engine is built for scale and supports batch and event driven workloads across many execution environments, including Kubernetes and cloud services. Features for scheduling, retries, and backfills allow teams to run pipelines with confidence in high volume settings.

Dagster also promotes efficient development through local testing, structured configuration, and an intuitive UI that provides lineage views, monitoring, and clear logs. The asset catalog enables self service access to datasets, helping teams collaborate and move faster.

By connecting tools across the data ecosystem, Dagster enables organizations to operate robust, scalable, and well governed big data pipelines.

---

## Big Data Platforms: Key Components & 7 Platforms to Know in 2025

> A big data platform is an integrated set of hardware and software tools designed to collect, store, process, and analyze massive volumes of structured, semi-structured, and unstructured data that traditional systems cannot handle efficiently.

Source: https://dagster.io/learn/big-data-platforms

### What Is a Big Data Platform? 

A big [data platform](https://dagster.io/learn/data-platform) is an integrated set of hardware and software tools designed to collect, store, process, and analyze massive volumes of structured, semi-structured, and unstructured data that traditional systems cannot handle efficiently. 

These platforms use distributed computing and often cloud infrastructure to manage the volume, velocity, and variety of big data, enabling organizations to extract actionable insights for decision-making, fraud detection, predictive analytics, and more.

Big data platforms typically integrate some or all of the following components:

- **Data storage:** Uses distributed systems like Hadoop Distributed File System (HDFS) or cloud-based options (e.g., Amazon S3, Google Cloud Storage) to store large datasets.
- **Data ingestion:** The entry point for information into a big data platform, ensuring that raw data from multiple sources is collected, transported, and made available for storage and processing
- **Processing engines:** Employs frameworks like Apache Spark to process data at high speeds, often in real-time or batch modes. 
- **Data wrangling and management:** Tools that help organize, cleanse, and transform raw data into a format that is understandable and useful for extracting insights. 
- **Analytics tools:** Includes software for performing complex queries and various types of analytics, such as business intelligence and machine learning. 
- **User interfaces:** Provide ways for users to access, visualize, and interact with the data and analytical results.

As organizations continue to generate and collect more data, these platforms remain central to uncovering actionable insights, driving innovation, and maintaining competitive advantage.

### How Big Data Platforms Work: Key Components and Functions 

#### Data Storage

Modern data solutions commonly employ distributed file systems like HDFS or object stores such as Amazon S3 to store both structured and unstructured data. These systems are designed to scale horizontally, meaning storage capacity can be increased simply by adding more hardware or virtual resources. 

Efficient storage architecture also includes mechanisms for data replication, compression, and backup, which enhance fault tolerance and optimize disk usage. Data is typically organized to support fast access patterns for downstream processing, including partitioning and indexing strategies. 

#### Data Ingestion

Data ingestion is the entry point for information into a big data platform, ensuring that raw data from multiple sources is collected, transported, and made available for storage and processing. Sources can include transactional databases, IoT devices, application logs, or third-party APIs. 

Ingestion tools are designed to handle high-volume and high-velocity streams, capturing both batch uploads and continuous real-time feeds without data loss. Ingestion pipelines typically support connectors and adapters that standardize heterogeneous input formats, transforming them into consistent schemas before passing them downstream. Features like buffering, checkpointing, and replay ensure that data is not lost during failures or network interruptions. 

#### Processing Engines

Processing engines are responsible for executing data transformations, aggregations, and analysis at scale. Tools like Apache Spark and Hadoop MapReduce distribute computational tasks across a cluster, breaking down large workloads into smaller jobs that run in parallel. This approach dramatically accelerates processing times for tasks such as ETL, ad-hoc querying, and advanced analytics compared to single-node solutions. 

These engines often support both batch and real-time data processing modes. Batch processing suits large, periodic data transformations, while real-time or streaming engines address scenarios such as fraud detection and instant data feeds. Support for multiple programming languages and APIs enables organizations to develop pipelines in Python, Java, SQL, or Scala.

#### Data Wrangling and Management

Data wrangling and management are critical for preparing raw, messy inputs into reliable datasets ready for analytics. This stage often involves data profiling, deduplication, validation, and enrichment. Automated pipelines apply rules and transformations to clean records, align schema inconsistencies, and normalize formats such as dates, currencies, and categorical fields. Effective wrangling ensures downstream processes operate on accurate, consistent data, reducing errors in reporting or modeling.

Management extends beyond transformation to include cataloging, versioning, and metadata tracking. Platforms typically provide centralized data catalogs, lineage tracing, and policy enforcement to help teams understand where data originated, how it has been transformed, and who has access. This governance layer makes it easier to maintain compliance, support schema evolution, and enable reproducible workflows while ensuring that high-quality data remains available to analytics and machine learning applications.

#### Analytics Tools

Analytics tools within big data platforms provide the capabilities required to transform raw data into actionable insights. These tools range from interactive SQL query engines to advanced statistical modeling, data visualization, and machine learning libraries. Their integration within big data platforms means analyses can be performed directly on data at scale, eliminating bottlenecks caused by data movement between systems. T

Beyond querying and visualization, analytics components often support complex event processing and predictive analytics workflows tailored for specific industries. Built-in support for leading data science frameworks allows practitioners to develop and test models using real-world datasets within the platform, reducing time-to-value. 

#### User Interfaces

User interfaces (UIs) in big data platforms serve as the bridge between complex backend systems and users with varying technical skill levels. They can range from graphical dashboards and SQL workbenches to code-based notebooks. Intuitive UIs empower data scientists and analysts to interact with data, define workflows, monitor jobs, visualize results, and manage datasets without writing low-level infrastructure code. 

Besides day-to-day data operations, UIs enable collaboration, audit trails, and documentation, making it easier for teams to share insights and maintain reproducibility. Advanced permissioning and customizable dashboards allow organizations to tailor access and views according to users’ roles or business needs. 

### Big Data Platforms vs. Data Lakes vs. Data Warehouses 

**A data lake** is primarily a central repository for storing raw, unstructured, and semi-structured data at scale. Data lakes focus on ingesting a wide range of formats without enforcing strict schema, making them suitable for exploration and data science experimentation. 

**Data warehouses** are optimized for structured data, supporting fast SQL analytics on cleaned, modeled datasets. Traditional data warehouses enforced schema-on-write, offering high performance for business intelligence workloads but less flexibility for handling raw or unstructured data. Modern data warehouses also make it possible to store unstructured data, providing improved flexibility, a pattern known as data lakehouse.

**Big data platforms** extend beyond both concepts by integrating storage, processing, analytics, management, and user interface layers into a cohesive ecosystem. Unlike standalone data lakes or data warehouses, a big data platform combines real-time and batch processing, governance tools, and analytics engines, allowing organizations to handle varied data workloads end-to-end. 

Some platforms blur the lines, offering **“lakehouse”** architectures that merge the flexible storage of data lakes with the performance of data warehouses, catering to both data engineering and analytical use cases within a single solution.

<strong><em>Learn more in our detailed guide to:</em></strong>

- *data warehouse platform (coming soon)*
- *data lake platform (coming soon)*

### Notable Big Data Platforms 

#### 1. Apache Hadoop

![](https://dagster.io/webflow-assets/70c01f2201566d3e2a41.avif)

Apache Hadoop is an open-source framework for distributed storage and large-scale data processing across clusters of commodity hardware. It addresses the challenge of managing big data by dividing datasets into blocks, distributing them across nodes, and processing them in parallel using the MapReduce model. 

##### **Key features include:**

- **HDFS (Hadoop Distributed File System):** A scalable, fault-tolerant storage system that replicates data across multiple nodes and supports high throughput access to large files.
- **MapReduce engine:** Distributes processing tasks across nodes, enabling parallel execution of data transformation and analysis jobs.
- **YARN (Yet Another Resource Negotiator):** Manages cluster resources and schedules jobs dynamically, supporting multiple application types beyond MapReduce.
- **Modular architecture:** Includes Hadoop Common (shared utilities), and allows integration with tools like Hive, HBase, Pig, and Spark.

**Data locality optimization:** Minimizes network traffic by processing data on or near the node where it is stored.

![](https://dagster.io/webflow-assets/47dd7b08d5bbc2ea397c.avif)

#### 2. Apache Spark

![](https://dagster.io/webflow-assets/18b5961c539f46e37243.avif)

Apache Spark is an open-source, unified analytics engine built for large-scale data processing across distributed clusters. It was designed to overcome the performance limitations of Hadoop MapReduce by supporting in-memory processing, iterative computations, and a rich set of APIs for various programming languages. 

##### **Key features include:**

- **In-memory computing:** Uses memory caching to speed up iterative and interactive data processing, often outperforming disk-based engines like Hadoop MapReduce.
- **Resilient distributed datasets (RDDs):** Core abstraction for fault-tolerant, parallel processing with functional programming constructs like map, filter, and reduce.
- **Dataset and DataFrame APIs:** High-level APIs for working with structured and semi-structured data, with support for compile-time type safety in some languages.
- **Spark SQL:** Enables querying data using SQL or the DataFrame API; integrates with JDBC/ODBC and supports multiple languages (Python, Scala, Java, R).

**Structured streaming:** Supports scalable, fault-tolerant stream processing using the same APIs as batch jobs, simplifying the lambda architecture.

![](https://dagster.io/webflow-assets/114cbdaefbf428a928ed.avif)

#### 3. Snowflake

![](https://dagster.io/webflow-assets/97657961c7392036478c.avif)

Snowflake is a fully managed, cloud-native data platform for analytics, artificial intelligence, data engineering, and application development across a unified architecture. Operating as a multi-cloud solution, Snowflake simplifies data workflows by eliminating the need for infrastructure management, while enabling collaboration, elastic scalability, and high-performance processing. 

##### **Key features include:**

- **Fully managed platform:** No infrastructure setup, tuning, or maintenance; users focus on building data products, not managing servers.
- **Multi-cloud & cross-region:** Runs natively on AWS, Azure, and Google Cloud with the ability to share and operate data across regions and providers.
- **Separation of storage and compute:** Enables independent scaling of workloads, optimizing cost and performance for analytics, ETL, and machine learning tasks.
- **AI & ML capabilities:** Integrates native support for developing, fine-tuning, and deploying machine learning models and large language models using Snowflake Cortex AI.

**Data sharing & collaboration:** Securely share live data with partners, customers, or across business units without data duplication or complex integration.

![](https://dagster.io/webflow-assets/f3d90391bf2e0257e5f4.avif)

#### 4. Google BigQuery

![](https://dagster.io/webflow-assets/21145843b0aa0556ba78.avif)

Google BigQuery is a managed, serverless, and AI-ready data platform to handle large-scale analytics across structured and unstructured data. It separates compute and storage, allowing independent scaling of resources without the need for manual provisioning or maintenance. It supports standard SQL, Python, and other languages.

##### **Key features include:**

- **Serverless architecture:** Fully managed platform with no infrastructure to manage, compute and storage scale independently to meet demand.
- **High-speed performance:** Query terabytes in seconds and petabytes in minutes with Google’s petabit-scale network and distributed compute engine.
- **Multi-format & multi-source support:** Analyze structured, semi-structured, and unstructured data across BigQuery, Cloud Storage, Bigtable, Spanner, and Google Sheets using federated queries.
- **Machine learning (BigQuery ML):** Train and deploy models using SQL without moving data; supports classification, regression, clustering, and time series forecasting.

**Geospatial and search analytics:** Natively supports geographic data types and functions, as well as semantic search to explore metadata and lineage.

![](https://dagster.io/webflow-assets/4ffa4bd35a0362e311c9.avif)

#### 5. Amazon Redshift

![](https://dagster.io/webflow-assets/d6db80650cd42b1dfdc5.avif)

Amazon Redshift is a fully managed data warehouse service for fast analytics on large volumes of structured data. Built on a massively parallel processing (MPP) architecture, Redshift distributes workloads across multiple nodes, delivering high performance through columnar storage, data compression, and advanced caching mechanisms. 

##### **Key features include:**

- **Massively parallel processing (MPP):** Distributes queries and data across multiple nodes for fast performance at scale.
- **Columnar storage & compression:** Optimizes I/O performance and reduces storage footprint by storing data in columnar format.
- **Serverless & elastic scaling:** Automatically scales compute resources or allows manual cluster resizing with minimal downtime.
- **Redshift spectrum:** Query exabytes of data directly in Amazon S3 without moving or transforming it first.

**Federated queries:** Combine and analyze live data across Redshift, S3, and operational databases like Aurora and RDS.

![](https://dagster.io/webflow-assets/76cf7025eced52a87c52.avif)

#### 6. Microsoft Azure Synapse

![](https://dagster.io/webflow-assets/9e46d6d98dad914659eb.avif)

Azure Synapse is a unified analytics platform that integrates data warehousing, big data, data exploration, and data integration into a single environment. It combines enterprise-grade SQL analytics with Apache Spark, Data Explorer for log analytics, and Azure Data Factory for ETL pipelines, all integrated with services like Power BI, Azure Machine Learning, and Cosmos DB. 

##### **Key features include:**

- **Unified SQL engine:** Synapse SQL supports both serverless and provisioned (dedicated) models, enabling scalable, distributed T-SQL queries across structured and semi-structured data.
- **Apache Spark integration:** Native Spark support with fast startup, autoscaling, and ML capabilities via SparkML and Azure Machine Learning integration; supports Delta Lake and .NET for Spark.
- **Lakehouse access:** Use SQL or Spark to directly query Parquet, CSV, JSON, and TSV files stored in Azure Data Lake; define shared tables accessible by both engines.
- **Data integration:** Full integration with Azure Data Factory to ingest from over 90 sources, build no-code ETL/ELT pipelines, and orchestrate complex workflows.

**Data explorer runtime (preview):** Optimized engine for log and time series analytics with interactive KQL queries and automatic indexing of semi-structured data.

![](https://dagster.io/webflow-assets/7317d1071b490567afae.avif)

#### 7. Cloudera

![](https://dagster.io/webflow-assets/8035f072e25336254e76.avif)

Cloudera is a hybrid data and AI platform for enterprises that need unified analytics and governance across cloud, data center, and edge environments. It enables organizations to manage and analyze data anywhere it resides (public cloud, private cloud, or on-premises) with consistent security, governance, and control. 

##### **Key features include:**

- **Hybrid and multi-cloud architecture:** Run data workloads seamlessly across AWS, Azure, GCP, private clouds, and on-premises infrastructure.
- **Unified data fabric:** Connect, govern, and analyze structured and unstructured data from multiple sources through a single integrated platform.
- **Open data lakehouse:** Built on Apache Iceberg, Cloudera delivers an open, high-performance lakehouse that supports both analytical and AI workloads at scale.
- **Scalable data mesh support:** Manage decentralized, domain-oriented data architectures with enterprise-grade governance and interoperability.

**Portable data analytics:** Easily move users, applications, and data between environments without rewriting code or compromising security.

![](https://dagster.io/webflow-assets/158f383d58dc15ba110d.avif)

### Evaluation Criteria for Choosing a Big Data Platform 

#### Scalability and Performance

Platforms supporting horizontal scaling allow organizations to add resources as needed, reducing the likelihood of bottlenecks or downtime. Performance hinges not only on raw compute power and throughput, but also on architectural factors like data partitioning, memory management, and parallel processing capabilities.

Evaluation should include real-world benchmarking on representative data workloads, considering metrics like query latency, ingestion speed, and job completion time. Effective support for both batch and streaming scenarios is also significant, especially for organizations with hybrid analytics requirements. 

#### Ease of Integration with Enterprise Systems

Big data platforms need to fit into existing enterprise IT landscapes, which typically include various databases, business applications, workflow engines, and analytic tools. Effective platforms offer connectors and APIs for seamless data ingestion, export, and bidirectional communication with core enterprise systems. Strong support for industry standards and out-of-the-box integrations reduces the effort and cost of implementation.

Beyond technical integration, platforms must align with enterprise data governance, compliance, and security workflows. They should support identity management systems, role-based access controls, and provide audit trails for all data interactions. 

#### Cost Models and Pricing Transparency

The economics of big data platforms can significantly impact their suitability for an organization. Most modern platforms offer pay-as-you-go, subscription, or hybrid pricing models that scale with compute, storage, and user needs. Transparent cost structures are essential for avoiding unexpected expenses, especially as data volumes grow.

Cost assessment should consider not just immediate expenses, but also total cost of ownership, including maintenance, support, training, and any required infrastructure upgrades. Cloud-native offerings may reduce capital expenses but can introduce unpredictable charges for data egress, API calls, or unused resources. 

#### Security, Compliance, and Governance

Big data platforms are often entrusted with sensitive or regulated data, making robust security a non-negotiable requirement. Core features should include encryption at rest and in transit, fine-grained access controls, audit logs, and real-time threat detection. Platforms must also support compliance with regional and industry-specific regulations such as GDPR, HIPAA, or SOC 2, offering features like data masking, retention management, and automated reporting.

Effective data governance extends beyond technical controls, including data lineage tracking, metadata management, and support for classification and policy enforcement. Centralized and automated governance tools help organizations manage risk, maintain data quality, and demonstrate accountability. 

#### Vendor Support and Community Strength

The level of support, whether from a commercial vendor or the open-source community, can determine how successfully an organization operates and evolves its big data platform over time. Responsive vendor support means faster resolution of incidents, access to updates, and guidance on best practices, all of which are critical in dynamic production environments.

Long-term viability should be assessed by examining the platform’s release cadence, contribution trends, documentation quality, and activity on forums or repositories. Organizations should favor solutions with vibrant user and partner networks, as these increase resilience to vendor lock-in or product discontinuation. 

### Ingesting and Managing Big Data with Dagster

Dagster provides a modern data orchestration framework that simplifies how teams ingest, transform, and manage large-scale datasets across complex environments. It offers strong abstractions for building reliable data pipelines and ensures that every step of the data lifecycle is observable, testable, and maintainable.

Dagster treats data ingestion as a first-class concern by allowing engineers to define clear asset boundaries and ingestion logic using a declarative model. These assets represent meaningful units of data and make it easier to track upstream sources, dependencies, and transformations. Dagster’s scheduling and sensor features allow pipelines to react to events, perform incremental ingestion, or run at regular intervals, which supports both batch and streaming workloads.

For data management, Dagster provides built-in tools for tracking metadata, schema evolution, and lineage across the entire platform. The asset graph gives teams a complete view of how data flows through the system, helping them identify bottlenecks, diagnose failures, and understand the impact of changes before they occur. Integration with storage systems and compute engines is supported through a flexible I/O manager interface, which allows teams to connect to data lakes, warehouses, object stores, or custom data platforms.

Observability is central to Dagster’s design. Each execution produces structured logs, metadata events, and materialization records that give operators insight into performance, data quality, and resource usage. These features help organizations enforce governance policies, validate data expectations, and maintain trust in the accuracy of downstream analytics.

Dagster’s approach enables engineering and analytics teams to collaborate effectively. By unifying ingestion, transformation, governance, and monitoring in a single platform, Dagster reduces operational complexity and provides a strong foundation for managing big data workloads at scale.

---

## Data Build Tool: Pros/Cons, Editions, and Quick Tutorial

> dbt, or Data Build Tool, is an open-source command-line tool that enables data analysts and engineers to transform data in a data warehouse.

Source: https://dagster.io/learn/data-build-tool

### What Is dbt (Data Build Tool)?

dbt, or Data Build Tool, is an open-source data transformation framework that enables data analysts and engineers to transform data in a data warehouse. It focuses on the transformation (T) step of ETL, allowing users to write data transformation workflows in SQL.

Users define transformation models within dbt, which then automatically handles their compilation, orchestration, and execution. This capability allows teams to transform raw data into a usable form without maintaining complex orchestration logic, ensuring that data processing becomes more accessible to those familiar with SQL.

The dbt workflow starts by connecting to a data warehouse, creating source models, and then building downstream models. It applies modular techniques, allowing users to reuse code and improve efficiency. This reduces duplicate work, improves collaboration among data professionals, and provides scalable, maintainable, and transparent data models.

### Key Features of dbt 

#### SQL-Based Transformation with Modular Code

dbt utilizes SQL for its data transformations, enabling a familiar and declarative approach to processing data. It encourages a straightforward method for data manipulation, making it accessible to data professionals who are already proficient in SQL. Users organize transformation logic into models, which helps reduce complexities. 

These models maintain readability and modularity, which simplifies tracking changes and refining data pipelines. Modular design in dbt allows for easy code reuse, leading to cleaner and more maintainable code bases. It promotes reusability by abstracting common transformations into macros or functions. Users can then apply these macros across multiple models.

#### Version Control and Collaboration

Version control is integrated into dbt, utilizing Git's capabilities to maintain a history of changes, manage branching, and resolve conflicts. This integration supports collaboration among teams, as it allows multiple users to work simultaneously on the same project while tracking all alterations. dbt enables team members to review, test, and validate each other's work.

Users can structure dbt projects to align with their team's workflow, making it simple to involve multiple contributors and distribute tasks. 

#### Testing and Documentation

dbt includes functionality for testing data to ensure accuracy and reliability throughout the transformation process. Users can define tests within their models, checking for anomalies or mismatches in data expectations. These tests capture errors early, leading to fewer data quality issues downstream. Regular testing ensures data remains reliable and maintains integrity, supporting consistent analytics and reporting. 

The documentation is automatically generated and linked with the respective code, simplifying the tracking and auditing of data transformations. Users can annotate their models with markdown, providing context and improving understanding. 

#### Easy Integration with Data Warehouses

dbt is compatible with various modern data warehouses such as Snowflake, BigQuery, Redshift, and others. It operates directly within the data warehouse, leveraging their processing power to execute SQL transformations. This eliminates the need for external processing environments, improving speed and reliability by utilizing the warehouse's capabilities directly.

Integration with data warehouses involves a simple configuration step within dbt, allowing users to quickly quickly to their data source. This means dbt can easily work alongside existing data infrastructure without significant changes, enabling efficient data transformations where data resides. 

### dbt Product Editions 

#### dbt Core

dbt Core is the open-source version of dbt, for teams that prefer a self-managed approach to data transformation. It provides the core functionality of dbt, including SQL-based transformations, testing, and documentation. Users execute dbt commands via the command line, integrating it with version control tools like Git to manage model changes and collaboration.

Since dbt Core runs locally or within a custom environment, it requires users to set up their own infrastructure, including scheduling and orchestration. It is well-suited for organizations with existing deployment processes or teams comfortable managing their own workflows. Despite being free and open-source, dbt Core is capable of handling complex transformations.

#### dbt Cloud

dbt Cloud is a fully managed, web-based version of dbt that simplifies deployment and collaboration. It includes all the capabilities of dbt Core while adding features like a graphical user interface (GUI), job scheduling, integrated development environments (IDEs), and automated documentation. These improvements make it easier for teams to build, manage, and monitor their dbt workflows without needing to manage infrastructure manually.

dbt Cloud’s built-in scheduling and orchestration allow users to automate transformation workflows without relying on third-party tools. It also provides a more user-friendly experience, making dbt accessible to a wider range of users. Additionally, dbt Cloud offers enterprise features such as role-based access control (RBAC) and single sign-on (SSO).

### Quick Tutorial: Running dbt Core with a Local DuckDB Database 

This tutorial will guide you through setting up a GitHub Codespace to run dbt Core. By the end, you will have a fully functional dbt environment, connected to a DuckDB database, loaded with sample data from a fictional café called Jaffle Shop. These instructions are adapted from the [dbt documentation](https://docs.getdbt.com/guides/duckdb?step=4). 

**Prerequisites**

Before you begin, ensure that you:

- Have a **GitHub account**
- Are familiar with basic **command-line navigation** (commands like cd, ls, and pwd)

#### Step 1: Create a Codespace

1. Log into your **GitHub account** and navigate to the [jaffle\_shop\_duckdb repository](https://docs.google.com/document/d/13o8Rk-_k_4k22Rwf7ldfF6TAADGKHRcRFupWfKenQpE/edit?tab=t.0).
2. Click **Use this template** and choose **Create new repository**.
3. Once the repository is created, click **Code**, then go to the **Codespaces** tab and select **Create codespace on main**.
4. Wait for the **postCreateCommand** to complete, which will set up the development environment. Once done, a new terminal window will open, ready for commands.

#### Step 2: Run dbt Commands

Once your codespace is ready, you can execute dbt commands from the terminal.

To **build your dbt project**, run:

```javascript theme=classic
dbt build
```

This command:

- **Compiles** the dbt models into executable SQL
- **Runs** the transformations
- **Tests** the results to ensure data quality

Other useful dbt commands include:

```javascript theme=classic
dbt compile  # Generates executable SQL from project files
dbt seed     # Create the source tables using the CSV seed files  
dbt run      # Compiles and runs the project  
dbt test     # Runs tests on the transformed data  
```

#### Step 3: Query Data Using duckcli

To explore your data warehouse directly from the terminal, use duckcli:

```javascript theme=classic
duckcli jaffle_shop.duckdb
```

This allows you to write SQL queries against the DuckDB database and explore the models created by DBT.

With this setup, you now have a fully functional dbt environment running in GitHub Codespaces. You can start modifying models, writing transformations, and analyzing data directly from your browser.

### Key Limitations of dbt 

While dbt is useful for data transformation, it has certain limitations that users should be aware of before adopting it. These limitations were reported by users on the [G2 platform](https://www.g2.com/products/dbt/reviews):

- **Steep learning curve**: dbt requires a shift in mindset, particularly for those coming from traditional data modeling tools. Users need to understand dbt’s structured approach to modeling and modularizing SQL with CTEs. Beginners may find it difficult to get started without prior SQL knowledge.
- **Limited Python support**: While dbt allows data modeling in Python, its functionality depends on the data platform being used. This means some warehouses have better Python support than others, limiting flexibility for certain workflows.
- **No built-in scheduler**: dbt does not have an integrated scheduling system for automating workflows. Users must rely on external tools like Dagster, Airflow, or Prefect to schedule dbt runs.
- **Batch processing only**: dbt operates in batch mode, meaning it cannot be used for real-time data transformations. This makes it unsuitable for streaming or event-driven use cases.
- **Development environment limitations**: The dbt Cloud IDE lacks some features found in other SQL development environments, which can make development less efficient. However, new features are being rolled out with each release.
- **Difficult log navigation**: The Job Runs tab in dbt Cloud is not intuitive for debugging. Finding and interpreting logs to troubleshoot issues can be cumbersome.
- **High data warehouse load**: Since dbt runs transformations directly in the data warehouse, it can lead to increased processing costs and slower query performance if models are not optimized.
- **Lack of visual data modeling**: dbt is heavily code-focused and does not provide strong visual tools for data modeling. Users who prefer graphical representations may find this limiting. However, new features like dbt Canvas are enabling visual approaches to data modeling.
- **Limited integration with external tools**: While many data tools (like Fivetran and Monte Carlo) work well with dbt, dbt itself does not always integrate seamlessly with these tools. More built-in support for external integrations could improve its usability.
- **Inconsistent documentation and support**: The documentation process could be improved to make onboarding easier. Some users have also reported slow customer support response times.

<strong><em>Related content: Read our guide to dbt cloud alternatives (coming soon)</em></strong>

### 5 Best Practices for Effectively Using dbt 

Here are some useful practices to keep in mind when using dbt.

#### 1. Organizing the Project Structure

A well-organized project structure is critical for scaling and maintaining dbt projects. Start by grouping models logically: such as by subject area or data domain: to improve clarity and manageability. Consistent naming conventions for directories and model files ensure that all team members can identify components quickly and understand the structure.

Documentation and README files should accompany project structures to offer guidance on navigating and understanding the project setup. Clear documentation aids teams in maintaining consistency in modeling practices and helps new team members integrate smoothly into ongoing projects.

#### 2. Implementing Tests and Documentation

Implementing tests within dbt models is vital to ensure data reliability throughout the pipeline. Tests should check for expected outcomes, such as row counts or specified constraints, validating model logic. Regular testing helps quickly spot and rectify issues, maintaining high confidence levels in data quality.

Consistent documentation improves the comprehension and tracking of dbt models. By documenting transformation logic, rationale, and associated tests, teams ensure transparency and accountability. dbt's automated documentation aids these efforts, generating records of model lineage and logic.

#### 3. Using Macros and Jinja for Code Reuse

Macros allow users to abstract repetitive SQL logic into reusable functions, which can be invoked across different models. This approach minimizes code duplication, enables standardization, and accelerates development by allowing teams to focus on unique transformation logic rather than repetitive coding tasks.

Jinja templating complements macros by enabling dynamic SQL generation, catering to complex transformation scenarios. It allows injecting variables and conditional logic into SQL statements, increasing flexibility and adaptability of models. By using Jinja, organizations improve their ability to dynamically scale transformation logic and adapt to new requirements.

#### 4. Managing Environments with Version Control

Version control is important for managing environments, especially in multi-developer setups. Git enables commit logging, branching, and merging that reflect changes in model logic, allowing team members to work independently and integrate their changes. It ensures that transformations are tracked over time, maintaining clarity over the evolution and rationale for alterations.

With segregated environments, such as development, staging, and production, version control ensures consistency across different models and transformations. Branch-specific modeling and testing preserve existing resources while allowing iterative development. dbt's integration with version control leads to a disciplined approach in managing environments.

#### 5. Automating Deployments and CI/CD

Automating deployments with continuous integration and continuous deployment (CI/CD) frameworks improves the reliability and efficiency of dbt projects. Incorporating CI/CD introduces automated testing and validations as part of the deployment pipeline, ensuring models adhere to predefined standards before entering production environments. 

This methodology reduces manual intervention, lowering the risk of errors and increasing deployment speed. CI/CD enables seamless transitions from development to production, ensuring tested and validated transformations are consistently deployed. Automated deployments can be configured to trigger on specified events, such as code commits or merge requests.

### Setting Up Effective dbt Workflows with Dagster

Dagster is an open-source data orchestration platform with first-class support for orchestrating dbt pipelines. As a general-purpose orchestrator, Dagster allows you to go beyond just SQL transformations and seamlessly connect your DBT project with your wider data platform.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and ML workflows. With a Python-native approach, it unifies SQL, Python, and more into a single, testable, and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## Data Discovery Platforms: 8 Solutions to Know in 2026

> A data discovery platform is a software solution that enables organizations to find, understand, and use their data, regardless of its location. These platforms automate data exploration and analysis to uncover patterns, anomalies, and insights, serving as a foundation for data-driven initiatives.

Source: https://dagster.io/learn/data-discovery-platform

### What Is a Data Discovery Platform? 

A data discovery platform is a software solution that enables organizations to find, understand, and use their data, regardless of its location. These platforms automate data exploration and analysis to uncover patterns, anomalies, and insights, serving as a foundation for data-driven initiatives like governance, compliance, and AI development.

Data discovery platforms make it easier to:

- **Locate data:** They connect to various data sources (databases, cloud storage, data lakes, and applications) to identify where data resides. 
- **Understand data:** They provide tools to profile data, assess quality, and understand relationships between different data elements. 
- **Catalog data:** Platforms often create a [data catalog](https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices), a centralized repository with metadata that describes the available data assets. 
- **Analyze & visualize:** They offer user-friendly interfaces and visualization tools to explore datasets, spot trends, and identify anomalies. 
- **Govern data:** Platforms assist in defining access controls, enforce rules, and manage data throughout its lifecycle, supporting privacy and security efforts. 
- **Enable AI:** By making data discoverable and trustworthy, these platforms are critical for building and scaling AI-powered applications.

Key components and processes of these platforms include:

- **Data source identification:** Recognizing and cataloging all available data assets. 
- **Integration with governance frameworks:** Applying established frameworks to ensure compliance with data requirements.
- **Data cataloging:** Creating a searchable, organized inventory of data assets. 
- **Data quality management:** Assessing and improving data accuracy and consistency. 
- **Security & governance:** Implementing controls and policies to protect sensitive information and ensure compliance.

This is part of a series of articles about [data platform](https://dagster.io/learn/data-platform).

### What Data Discovery Platforms Do 

#### Locate Data

Data discovery platforms excel at helping organizations locate data wherever it resides in the enterprise. They use automated scanning and connectors to search databases, cloud storage, data warehouses, and other sources without manual intervention. By continuously scanning the environment, they ensure their inventory stays up to date even as new data assets are introduced or old ones are retired. 

This ability to locate data quickly is crucial for overcoming data silos, making it easier for technical and non-technical users to find relevant information. By providing a central catalogue, these tools eliminate the need for time-consuming manual searches, allowing teams to respond rapidly to business needs and regulatory inquiries.

#### Understand Data

Once data is located, a platform must help users understand the context, structure, and meaning of that information. Data discovery solutions use features like metadata extraction, profiling, classification, and automated tagging to illuminate what data exists and what it describes. 

This intelligence is surfaced through search, recommendations, and visual summaries, making the data accessible to users with varying technical backgrounds. Understanding data is not just about identifying columns or formats. It involves assessing [data quality](https://dagster.io/learn/data-quality), highlighting sensitive information, and tracing relationships between datasets. 

#### Catalog Data

Cataloging is a core function of data discovery platforms. By constructing a centralized repository of all available data assets, these tools enable organizations to manage and retrieve data efficiently. The catalog includes metadata such as source, owner, schema, freshness, and usage history, giving users confidence in what they’re accessing. 

A mature catalog doesn’t only list data; it also supports collaboration by allowing users to annotate data sets, flag issues, or recommend trusted sources. This central catalog becomes the backbone for data governance, improving consistency, accountability, and reuse throughout the data lifecycle.

#### Analyze and Visualize

Modern data discovery platforms often include analysis and visualization tools so users can explore datasets without switching to other solutions. These platforms enable users to create dashboards, generate insights, and visualize trends directly from discovered data, often using natural language queries or drag-and-drop interfaces. 

The ability to analyze and visualize within the discovery platform accelerates the journey from data to insight. It reduces the friction in moving data between tools, lets users validate their findings on the fly, and supports better, faster decision-making across the organization.

#### Govern Data

Data governance is integrated deeply within data discovery platforms. They enforce access controls, track data usage, and underpin compliance efforts with audit logs and policy enforcement. By embedding governance into the discovery process, these platforms help organizations manage who can access what data, ensuring privacy and regulatory requirements are met. 

Users benefit from transparency: they know where data comes from, how it can be used, and its quality status. For compliance teams, tooling for governance reduces manual monitoring burdens, simplifies policy application, and centralizes incident response, strengthening an organization’s overall data posture.

#### Enable AI

Data discovery platforms play a significant role in enabling AI initiatives. They provide curated, well-documented, and high-quality datasets that are essential for building trustworthy machine learning models. Discovery platforms can also surface overlooked datasets and relationships that unlock new training data or features. 

Automation within these platforms helps ensure the data used for AI is relevant, up to date, and compliant with internal policies or external regulations. As AI adoption increases, robust discovery tools have become essential in controlling risk and accelerating experimentation and deployment for enterprise AI projects.

### Key Capabilities of Data Discovery Platforms 

#### Automated Data Cataloging and Metadata Management

Automated cataloging is central to a data discovery platform’s value proposition. These systems connect to disparate data sources and automatically extract metadata, mapping datasets, schemas, and lineage without heavy manual effort. This automation not only accelerates the creation of a living data inventory but also significantly reduces human error in documentation.

Effective metadata management goes beyond collection: it involves classifying, enriching, and maintaining metadata over time. Powerful search, tagging, and relationship mapping features enable users to derive more context and make informed decisions. This automated approach ensures data remains discoverable and relevant as environments evolve, supporting continuous data-driven operations.

#### Data Source Identification 

Understanding data lineage (the movement and transformation of data over time) is essential for transparency, trust, and compliance. Data discovery platforms offer dynamic lineage mapping that tracks how data flows from its original sources through pipelines, transformations, and reporting layers. This way, users can easily visualize dependencies and understand the ripple effects of changes.

Impact analysis builds on lineage to answer questions such as, “If I change or remove this field, what downstream dashboards, models, or processes will break?” By illuminating these connections, platforms prevent costly errors, support safe governance, and enable data migration or modernization projects with minimal disruption.

#### Data Quality Management

Continuous data quality monitoring is crucial for maintaining trustworthy analytics and AI. Discovery platforms embed automated profiling and validation mechanisms to detect anomalies, missing values, or inconsistent formats within datasets. These quality checks are often visualized through dashboards and alerts, allowing teams to triage and respond before issues reach end users.

Observability doesn’t stop at static checks. More advanced platforms detect trends, monitor data freshness, and spot drifts over time. This ensures organizations can take corrective action quickly and maintain a high standard for their production data environments, thereby improving the accuracy and reliability of business decisions.

#### Integration with Data Governance Frameworks

Integration with governance frameworks is a defining strength of leading data discovery platforms. They enable the application and enforcement of business rules, privacy policies, and data stewardship practices at scale. Integration points may include interoperability with identity management, policy engines, and compliance reporting tools, which ease the work of governance teams.

These integrations support automation of common governance tasks such as access provisioning, data masking, or retention enforcement. By making policy enforcement native to the discovery process, organizations can reduce the risk of ungoverned data usage and set clear guidelines for all data interactions, driving both safety and productivity.

#### Security and Governance 

Security and compliance are non-negotiables for handling enterprise data. Discovery platforms incorporate strong access controls, encryption, and auditing to protect sensitive assets. They also provide automated detection of personally identifiable information (PII) and other regulated data, mapping it to compliance frameworks like GDPR, HIPAA, or CCPA.

These capabilities make it straightforward for organizations to demonstrate compliance during audits and mitigate the risk of data breaches. Centralizing controls within the discovery platform helps enforce consistent security practices and enables organizations to adapt quickly to evolving regulatory landscapes, without increasing operational burden.

### Notable Data Discovery Platforms

#### 1. Dagster

![Dagster logo](https://dagster.io/webflow-assets/29e9468525d8dc4fad51.png)

Dagster is a data discovery–adjacent platform that approaches discovery through data assets, metadata, and operational context, rather than through standalone cataloging alone. It helps organizations understand what data exists, how it is produced, and whether it can be trusted, making it a strong foundation for discovery, governance, and AI readiness.

Dagster Compass extends these capabilities by serving as a lightweight, developer-first data catalog that surfaces asset metadata, lineage, ownership, and health in a centralized interface. Compass focuses on making data discoverable and understandable directly from how it is produced, without requiring heavy manual curation.

**Key features include:**

- **Software-defined data assets**: Treats datasets as first-class entities with explicit dependencies, ownership, and metadata
- **Built-in lineage tracking**: Automatically captures upstream and downstream relationships across pipelines, warehouses, and transformation tools
- **Operational metadata capture**: Surfaces freshness, run history, failures, and quality checks alongside datasets
- **Dagster Compass catalog**: Provides a centralized, searchable view of data assets, lineage, and metadata without complex catalog setup
- **Data quality and validation**: Integrates checks and expectations directly into data production workflows

![Key features of Dagster](https://dagster.io/webflow-assets/7d58825356440553d391.webp)

#### 2. Alation

![Alation logo](https://dagster.io/webflow-assets/8802bf8624567de41f8f.webp)

Alation is a data discovery platform to help organizations find, understand, and trust their data, even in highly decentralized environments. AI-driven simplifies how users navigate enterprise data. Alation's platform enables both technical and business users to quickly locate relevant data assets through natural language queries, semantic search, and customizable filters. 

**Key features include:**

- **Universal search**: AI-powered search bar queries across connected data sources, including databases, lakehouses, and file systems
- **Natural language understanding**: Interprets human-readable questions to return contextually relevant results
- **Semantic and keyword search**: Combines search by meaning and keywords for precise discovery
- **Advanced filtering**: Filters results by object type, custom tags, or data quality status (e.g., endorsed, deprecated)

**Saved searches**: Allows users to save and reuse complex queries for consistent access to trusted data

![Key features of Alation](https://dagster.io/webflow-assets/4ce529af1242ec750a76.webp)

#### 3. Atlan

![Atlan logo](https://dagster.io/webflow-assets/201afad5ec80de476478.webp)

Atlan is a data discovery and governance platform to help data teams find, understand, and collaborate on data across the enterprise. Built with a focus on active metadata, Atlan transforms metadata into a dynamic layer that supports search, lineage, access control, and context. 

**Key features include:**

- **Active metadata platform**: Continuously ingests and activates metadata from tools like Snowflake, dbt, Looker, and Tableau to keep discovery and governance up to date 
- **Search and discovery**: Use natural language search to find datasets, dashboards, metrics, and lineage, with rich context and usage metadata
- **Data lineage**: Visualize upstream and downstream lineage across modern data stacks to understand dependencies, assess impact, and troubleshoot faster
- **Embedded Collaboration**: Bring context to data with embedded Slack/Teams integrations, user annotations, conversations, and ownership assignments

**Role-based access control (RBAC)**: Govern data access and enforce policies with role-aware permissions, audit trails, and automated workflows

![Key features of Atlan](https://dagster.io/webflow-assets/110ff0ffc201aef4e621.webp)

#### 4. Collibra Data Catalog

![Collibra logo](https://dagster.io/webflow-assets/9867b6986e0930d787b3.webp)

Collibra Data Catalog helps simplify data discovery, understanding, and access across large ecosystems. It connects to over 100 data sources, automatically extracting and enriching metadata to create a unified, searchable inventory of enterprise data assets.

**Key features include:**

- **Automated discovery and profiling**: Uses over 100 native connectors to scan structured and unstructured data, automate profiling, and reduce manual stewardship work
- **AI-assisted metadata curation**: Uses AI to generate descriptions and classify assets, simplifying catalog creation and maintenance
- **Contextual enrichment**: Connects data to glossary terms, policies, data contracts, stakeholders, and quality metrics to improve understanding
- **Integrated data marketplace**: Offers a curated, self-service portal for discovering and accessing trusted data products

**End-to-end lineage mapping**: Visualizes technical and business lineage to show where data comes from, how it’s used, and its downstream impact

![Key features of Collibra](https://dagster.io/webflow-assets/8b845eed062010692ea8.webp)

#### 5. AWS Glue Data Catalog

![AWS logo](https://dagster.io/webflow-assets/2af8aa2ec3b673efee00.webp)

The AWS Glue Data Catalog is a centralized metadata store for managing and discovering data across AWS and external sources. It uses crawlers to automatically scan data repositories, infer schemas, and populate metadata tables. This enables a consistent, queryable view of data assets across services like Amazon Athena, Amazon EMR, and AWS Lake Formation. 

**Key features include:**

- **Centralized metadata repository**: Stores schema, location, and operational metadata in databases and tables for unified access
- **Automatic data discovery**: Crawlers detect and register new or updated data sources, reducing manual cataloging
- **Schema management and evolution**: Tracks schema changes and partitioning for supported formats and services
- **Materialized views for Iceberg tables**: Supports precomputed views that automatically refresh for faster query performance

**Column-level statistics**: Computes metrics like min/max, null counts, and distinct values to help profile data and optimize queries

![Key features of AWS](https://dagster.io/webflow-assets/8c02a47baf05a4a5dafd.webp)

#### 6. Google Cloud Data Catalog

![Google Cloud Data Catalog logo](https://dagster.io/webflow-assets/c7c2480388c3500739c8.webp)

Google Cloud Data Catalog is a fully managed metadata management service that helps organizations discover and understand data assets across Google Cloud. It provides a unified search interface and supports tagging, classification, and metadata enrichment. It is now deprecated and scheduled for discontinuation in January 2026. Users are encouraged to transition to Dataplex Universal Catalog for continued support.

**Key features include:**

- **Centralized metadata management**: Stores technical and business metadata for Google Cloud data assets
- **Search-driven discovery**: Uses a search interface built on Google's internal search engine for fast, relevant results
- **Custom tagging system**: Supports user-defined tags and tag templates for consistent metadata enrichment
- **Sensitive data detection**: Integrates with the DLP API to automatically identify and tag sensitive information

**Third-party metadata integration**: Supports ingestion of metadata from tools like Looker, Tableau, and on-premises RDBMS systems

![Key features of Google Cloud Data Catalog](https://dagster.io/webflow-assets/daaa63843a37909e3d7b.webp)

#### 7. Informatica Enterprise Data Catalog

![Informatica logo](https://dagster.io/webflow-assets/813b75786dd7b0fb4e66.webp)

Informatica Enterprise Data Catalog is a scalable metadata management solution to scan, index, and organize data assets across cloud and on-premises environments. It provides capabilities for data discovery, profiling, and lineage to help users understand their data. 

**Key features include:**

- **Automated data discovery**: Scans and catalogs metadata from enterprise data sources, both cloud and on-premises
- **AI-assisted curation**: Uses machine learning for domain discovery, similarity detection, and linking business terms to technical assets
- **Data lineage**: Tracks end-to-end lineage at system and column levels for transparency and impact analysis
- **Contextual data profiling**: Surfaces data quality and profiling information to support informed data use

**Collaborative insights**: Enables teams to share knowledge and maximize data reuse through a unified platform

![Key features of Informatica](https://dagster.io/webflow-assets/0e527d1d223140cd2e78.webp)

#### 8. Ataccama Data Catalog

![Ataccama logo](https://dagster.io/webflow-assets/654be1eb6f890662fd95.webp)

Ataccama Data Catalog is part of the Ataccama ONE platform and integrates data discovery, quality, and governance into a single environment. It uses AI-driven automation to catalog data from cloud and on-premises sources, classify it, and maintain quality through continuous monitoring. 

**Key features include:**

- **Automated data discovery and classification**: Scans connected systems to identify data assets and apply AI-based tagging and business term suggestions
- **Data quality monitoring**: Uses prebuilt and custom rules to detect anomalies, profile data, and ensure reliability over time
- **Augmented data lineage**: Captures end-to-end lineage and overlays it with business terms and quality metrics for better traceability
- **Business glossary integration**: Manages business terms, rules, and policies to align metadata with organizational context
- **Collaborative workflows**: Supports task assignment, comments, and feedback within the catalog for improved team coordination

**Report and metadata cataloging**: Indexes reports, dashboards, ML models, APIs, and other assets in a customizable metadata system

![Key features of Ataccama](https://dagster.io/webflow-assets/e99a899411d377fe5bcc.webp)

#### 9. OvalEdge

![OvalEdge logo](https://dagster.io/webflow-assets/1bff18ee7a28bf3cfcc1.webp)

OvalEdge is a modular data governance platform that includes a data catalog designed for fast deployment and broad organizational adoption. It connects to over 150 data sources and uses AI and automation to simplify metadata management, lineage tracking, and quality monitoring. 

**Key features include:**

- **Automated metadata crawling**: Uses over 150 built-in connectors to scan and catalog data without manual mapping
- **AI-powered natural language search**: Supports business-friendly, ChatGPT-style queries to retrieve insights from enterprise data
- **Integrated data quality tools**: Monitors and ensures data consistency and accuracy across sources
- **Lineage and glossary**: Tracks data flow automatically and manages business terms for clarity and traceability

**Embedded collaboration features**: Designed for cross-team collaboration and integration with commonly used enterprise tools

![Key features of OvalEdge](https://dagster.io/webflow-assets/8cce4290cd20d7e4944a.webp)

#### 10. data.world

![data.world logo](https://dagster.io/webflow-assets/b1c98d7254c00669c351.webp)

data.world is an enterprise data catalog built around a knowledge graph architecture that captures relationships, context, and meaning across data assets. It enables teams to discover, govern, and connect data through a unified platform. The catalog supports explainable AI outcomes while delivering search, automated governance, and scalability.

**Key features include:**

- **Knowledge graph foundation**: Models data relationships and business logic to enable contextual understanding across the data ecosystem
- **AI context engine**: Supports accurate, governed AI applications by delivering explainable, precise responses from large language models
- **Enterprise search**: Provides Google-like search that understands both technical metadata and business semantics
- **Automated governance workflows**: Adapts to team processes with smart workflows for scalable policy enforcement

**Unified data landscape**: Breaks down silos by integrating data from multiple sources into a single, connected view

![Key features of data.world](https://dagster.io/webflow-assets/6bd78dc751fd31134f63.webp)

### Conclusion

Data discovery platforms provide a foundational layer for modern data ecosystems, enabling organizations to gain visibility, control, and value from their data assets. By automating the processes of locating, cataloging, understanding, and securing data, these platforms help bridge the gap between data availability and usability. They support collaboration across technical and business teams, simplify governance, and ensure that data is both accessible and trustworthy, crucial for analytics, compliance, and AI initiatives alike.

---

## Data Engineering on AWS: Key Services & 6 Critical Best Practices

> Amazon Web Services (AWS) provides extensive support for data engineering tasks, such as designing, building, and managing scalable data processing systems.

Source: https://dagster.io/learn/data-engineering-on-aws

Amazon Web Services (AWS) provides extensive support for data engineering tasks, such as designing, building, and managing scalable data processing systems. This includes setting up pipelines, cleaning data, and ensuring it is ready for analysis. 

AWS offers a range of tools and services, like storage solutions, data processing capabilities, and big data services, to execute and manage data workflows efficiently. Data engineers use these capabilities to handle both structured and unstructured data.

AWS provides a cloud infrastructure that allows for flexible scaling of data operations, reducing the need for on-premises data management. The shift to cloud computing supports easier data sharing and integration across various services.

### Essential AWS Services for Data Engineering Workflows 

#### Deploy Data Storage (Data Lake, Data Warehouse, or Object Storage)

Effective data storage is essential for data engineering workflows. AWS offers specialized services for different storage needs, including data lakes for unstructured data, data warehouses for structured data, and blob storage for binary data. 

**Key AWS services for data storage**:

1. **Amazon S3**: Suitable for creating data lakes, S3 offers virtually unlimited storage, robust durability, and multiple storage classes for cost optimization. Its integration with services like AWS Glue and Amazon Athena enables seamless data discovery and querying.
2. **Amazon Redshift**: Designed for data warehousing, Redshift provides high-performance analytics on structured and semi-structured data. Features like columnar storage, data compression, and parallel query execution make it suitable for processing large datasets.
3. **Amazon EFS and Amazon FSx**: Provide scalable, high-performance file storage for use cases requiring file-based workloads. These solutions are especially useful for applications requiring shared file systems or POSIX-compliant access.

#### Develop Data Ingestion Patterns

Data ingestion involves collecting data from multiple sources, such as databases, APIs, and file systems, and moving it into a centralized location for processing and storage. Automation and orchestration of ingestion workflows are essential for ensuring reliable and efficient data flow.

**Key AWS services for data ingestion:**

- **Database Migration Service (DMS):** Supports real-time or batch migration of databases to AWS with minimal downtime. Can replicate ongoing changes, making it suitable for zero-downtime migrations and synchronization between source and target databases.
- **AWS DataSync**: Simplifies data transfer between on-premises systems and AWS storage services, ensuring efficient and secure migration of large datasets.
- **Amazon Kinesis:** A platform for real-time data streaming and ingestion. includes Kinesis Data Streams for building custom stream processing applications, Kinesis Data Firehose for direct delivery to destinations, and Kinesis Data Analytics for real-time SQL processing on streaming data.

#### Data Processing

Data processing involves transforming raw data into structured formats suitable for analysis. This process includes data cleaning, normalization, and aggregation. AWS offers managed services that simplify extract, transform, and load (ETL) tasks while ensuring scalability and automation.

**Key AWS services for data processing:**

- **AWS Glue**: A managed ETL service that automates schema discovery, data profiling, and data transformation tasks. Suitable for cleaning and preparing data for analysis.
- **Amazon EMR**: Managed big data service for running distributed processing frameworks like Apache Spark and Hadoop. Suitable for large-scale data transformations and batch processing.
- **AWS Lambda**: Serverless compute service for real-time data transformation. Executes code in response to events like data uploads or API requests.

#### Data Analysis and Visualization

Data visualization involves presenting processed data in a way that is easy to interpret. Visualization tools support interactive dashboards, graphs, and reports, enabling stakeholders to identify trends and insights quickly.

**Key AWS services for data visualization:**

- **Amazon QuickSight**: A serverless business intelligence tool for creating interactive dashboards and reports. Supports querying multiple data sources, including S3, Redshift, and RDS.
- **AWS Data Exchange**: Enables data sharing and collaboration through curated datasets. Simplifies access to third-party data for integration into visualizations.
- **Amazon Athena**: A serverless query engine that allows SQL-based querying of data stored in S3. Suitable for ad hoc analysis and visualization preparation.

### Data Orchestration

Data orchestration coordinates the movement and transformation of data across various AWS services. It defines the sequence, dependencies, and scheduling of tasks to ensure data pipelines run reliably and efficiently.

**Key AWS services for data orchestration:**

- **Amazon Managed Workflows for Apache Airflow (MWAA)**: Fully managed Apache Airflow service for authoring, scheduling, and monitoring workflows using Python. Supports features like task dependencies, retries, and error handling.
- **AWS Step Functions**: A low-code orchestration service that helps automate workflows with state machines. Provides built-in error handling, retry logic, and support for complex task dependencies.

### 6 Best Practices for Data Engineering on AWS 

Organizations should consider the following best practices to ensure successful data engineering in AWS environments.

#### 1. Adopt a Serverless or Managed Services Approach

With serverless services like AWS Lambda, developers can run code without provisioning or managing servers, automatically scaling based on demand. Similarly, managed services like AWS Glue, Amazon Relational Database Service (RDS), and Amazon Redshift take care of infrastructure management tasks such as scaling, patching, and backup.

This approach allows data engineers to focus on creating and optimizing data pipelines rather than dealing with operational complexities. For example, AWS Glue simplifies extract, transform, and load (ETL) tasks with built-in automation, while RDS manages databases while abstracting most administrative tasks. 

#### 2. Follow AWS Well-Architected Framework

The AWS Well-Architected Framework provides guidelines and best practices to help organizations design secure, high-performing, resilient, and cost-effective applications. For data engineering, adhering to the framework’s five pillars—operational excellence, security, reliability, performance efficiency, and cost optimization—is critical.

For example, the **performance efficiency** pillar recommends selecting the right instance types, using caching mechanisms, and leveraging scalable storage solutions like Amazon S3. The **reliability** pillar emphasizes designing for fault tolerance, such as using multiple Availability Zones (AZs) and automatic failover mechanisms. Regular reviews using the Well-Architected Tool can help identify bottlenecks, vulnerabilities, and inefficiencies in data workflows.

#### 3. Implement Tiered Storage

Tiered storage helps optimize storage costs by aligning data placement with its access frequency and retention requirements. AWS offers multiple storage classes within Amazon S3. A tiered storage strategy involves categorizing datasets by access patterns, implementing lifecycle policies to automate data transfers between tiers, and compressing data to save space.

For data that is frequently accessed, **S3 Standard** provides high performance and low latency. For less frequently accessed data, **S3 Standard-IA** (Infrequent Access) or **S3 Intelligent-Tiering** automatically moves data between tiers based on usage patterns. For long-term archival, **S3 Glacier** or **S3 Glacier Deep Archive** significantly reduce storage costs.

#### 4. Implement Schema Evolution

Data schemas often evolve over time due to changing business requirements, new data sources, or updated processes. Managing schema changes helps maintain the integrity of data pipelines. Best practices for schema evolution include using forward-compatible schemas to ensure new fields can be added without breaking downstream processes, implementing automated tests to validate schema changes, and communicating updates to all stakeholders. 

AWS Glue Schema Registry is a tool for managing and enforcing schema consistency in streaming and batch workflows. It enables schema versioning, validation, and compatibility checks, helping prevent data ingestion errors caused by mismatched formats.

#### 5. Leverage Parallelism and Scalability

Data engineering workflows often involve processing large datasets that can strain system resources if not designed for scalability. AWS services like Amazon EMR, AWS Glue, and AWS Lambda are built for parallel processing and horizontal scaling, making them appropriate for handling growing data volumes.

Amazon EMR, for example, supports distributed frameworks like Apache Spark and Hadoop, allowing data to be processed across multiple nodes in parallel. AWS Glue automatically partitions datasets to process them concurrently, while Lambda scales to handle bursts of data ingestion or processing tasks. 

To fully leverage parallelism, design workflows to minimize dependencies between tasks, enabling concurrent execution. Additionally, enable auto-scaling for services like EMR clusters to dynamically adjust capacity based on workload demands.

#### 6. Encrypt Data in Transit and at Rest

AWS provides tools to encrypt data in transit and at rest, protecting it against unauthorized access and tampering. For data at rest, AWS offers built-in encryption options for services like S3, RDS, Redshift, and DynamoDB. These services integrate with AWS Key Management Service (KMS), enabling centralized control over encryption keys.

For data in transit, using Transport Layer Security (TLS) ensures secure communication between clients and servers, protecting data from interception during transfer. AWS services like Amazon CloudFront and API Gateway also support encryption protocols to secure data exchange. To further improve security, implement policies that enforce encryption as a default setting, regularly rotate encryption keys, and use audit logs to monitor access to sensitive data. 

<strong><em>Related content: Read our guide to data engineering tools (coming soon)</em></strong>

### Automate Data Engineering on AWS with Dagster

Dagster is an open-source data orchestration platform designed to help teams build reliable, testable, and maintainable data pipelines. When paired with AWS services, Dagster provides a unified control layer for your entire data platform, making it easier to design workflows, manage dependencies, and ensure end-to-end observability across your data ecosystem.

#### **First-Class Orchestration for AWS Services**

Dagster integrates seamlessly with key AWS data engineering components, including Amazon S3, Redshift, EMR, Lambda, Glue, and Athena. Engineers can build declarative, Python-based assets and jobs that interact directly with AWS services using Dagster’s AWS integrations and community-maintained libraries.

This allows teams to:

- Trigger transformations when new files land in S3
- Orchestrate multi-step workflows that combine services such as EMR, Lambda, and Glue
- Manage ingestion and transformation logic with clear lineage and typed inputs and outputs
- Build event-driven pipelines without writing custom glue code<br>

#### **Declarative, Asset-Based Pipelines**

Dagster’s software-defined assets model encourages treating data as a product. Instead of manually stitching together tasks, you define assets that represent tables, datasets, or machine learning features. Dagster automatically manages their dependencies, materialization logic, and metadata.

This approach is especially powerful on AWS data stacks:

- S3 objects can be modeled as assets with clear freshness policies
- Redshift tables can be updated only when upstream datasets change
- Downstream transformations can be triggered incrementally and intelligently<br>

By modeling pipelines around data artifacts rather than jobs, teams reduce unnecessary recomputation, simplify debugging, and improve reliability.

#### **Built-In Observability and Logging**

Dagster offers a centralized UI for monitoring pipeline execution, understanding lineage, viewing logs, and tracking the health of your system. When orchestrating AWS resources, Dagster acts as a single pane of glass, so there is no need to jump between multiple AWS consoles.

You can easily visualize:

- Which assets were updated
- Trigger paths and upstream and downstream dependencies
- Logs and events tied to specific AWS actions
- Failed runs, retries, and alerts

Dagster Cloud further enhances observability with hosted infrastructure, event-driven execution, and production-grade monitoring.

#### **Infrastructure Flexibility for Any Environment**

Dagster aligns well with the serverless philosophy emphasized in AWS best practices. You can deploy Dagster in several modes depending on your architecture:

- **Dagster Cloud Serverless** which provides a fully managed orchestrator
- **Dagster Cloud Hybrid** which keeps compute inside your VPC while the Dagster control plane is hosted
- **Self-hosted Dagster** running on ECS, EKS, EC2, or Kubernetes

This flexibility allows teams to adopt the architecture that fits best, whether they are orchestrating Glue jobs, Spark clusters on EMR, or container-based transformations.

#### **Ideal for Teams, CI/CD, and Governance**

Dagster treats data pipelines as software, which enables:

- Code reviews for pipeline changes
- Automated testing of transformations
- Versioned assets with lineage tracking
- Policy enforcement and governance at the data layer<br>

This fits naturally into AWS-native CI/CD workflows using CodeBuild, GitHub Actions, or other automation tools.

---

## Data Engineering: Components, Skills & Best Practices [2025 Guide]

> Data engineering focuses on the practical application of data collection and processing techniques.

Source: https://dagster.io/learn/data-engineering

### What Is Data Engineering?

Data engineering focuses on the practical application of data collection and processing techniques. The primary goal is to design systems that consolidate, clean, and transform data into usable forms for analytical and operational use cases. This discipline ensures the flow of data from source to destination, accommodating complex and varying data sources. Through automation and efficient data handling, it removes bottlenecks in data pipelines, allowing integration and consistency across organizational data assets.

Data engineering involves designing an architecture to support analytics and AI use cases, ensuring that storage mechanisms (e.g., data lakes and data warehouses) comply with the specific requirements of business units. It also involves utilizing tools and technologies that permit scalability and adaptability in data processes. Data engineering lays the foundation for data analytics and machine learning by creating data sets that are clean, structured, and ready for further interrogation and utilization.

### What Do Data Engineers Do?

Data engineers are responsible for developing, constructing, testing, and maintaining data architectures and their components, such as databases, data streaming engines, and data processing systems. Their primary task involves designing, implementing, and managing the pipeline architecture to ensure efficient and reliable data flows. They work on optimizing data delivery, enabling more efficient and accurate data analysis to support organizational goals.

In addition, data engineers collaborate with data scientists and analysts to improve data quality and develop datasets for deeper analysis. They are proficient in using a range of tools and technologies to build data systems. These engineers also focus on infrastructure building, ensuring data orchestration, transition, and integration. Their operational support includes troubleshooting issues, ensuring that data systems operate smoothly without unplanned downtime.

### Types of Data Engineers

#### Generalists

Generalist data engineers handle a broad spectrum of tasks across the data pipeline. They thrive in small teams or startups where resources are limited, working across data collection, processing, and analysis phases. Their flexibility allows them to implement full-stack data solutions, integrating various technologies to meet company-specific challenges.

With a generalist approach, engineers can adapt quickly to changing business needs, addressing multiple aspects of data engineering without specialization. This adaptability makes them valuable assets in environments where versatility and cost-effective resource usage are essential. Their role often bridges gaps between IT and business domains.

#### Pipeline-Centrists

Pipeline-centric data engineers focus primarily on developing and maintaining data pipelines, ensuring efficient data flow within an organization. They specialize in automating data movement from source systems to data warehouses or lakes. Their role involves selecting and optimizing tools like Dagster and Apache Airflow to facilitate and orchestrate pipeline operations.

These engineers emphasize process reliability, managing dependencies and ensuring fault tolerance in data flows. By concentrating on pipeline architecture, they enable consistent and timely data availability for analytics and reporting functions. Their expertise supports organizations in scaling data operations while minimizing friction and maintaining high performance.

#### Database-Centrists

Database-centric engineers focus on database architecture and optimization, ensuring effective data storage and retrieval systems. They excel in schema design, indexing, and query performance tuning, allowing databases to handle large data volumes efficiently. Their proficiency extends to managing SQL and NoSQL databases, data warehouses, and data lakes to accommodate structured and unstructured data needs.

This specialization encompasses maintaining data integrity, consistency, and security within database systems. Engineers collaborate closely with developers and analysts to provide reliable, scalable storage solutions. Their work is critical for organizations requiring high data availability and swift access, underpinning real-time analytics and transaction processing systems.

### Data Engineering vs. Data Analysis vs. Data Science

**Data engineering** lays the groundwork for data analysis and data science by preparing and structuring data for analysis and making it accessible to stakeholders.

**Data analysts** focus on interpreting and visualizing this information to derive actionable insights. Their goal is to transform data into a story that highlights trends and patterns useful for strategic decisions.

**Data scientists** delve deeper into data to develop predictive models and algorithms that assist in forecasting future trends. They use structured data provided by data engineers to train machine learning models and perform statistical analysis.

While all three roles overlap in their use of data, each has its specific focus: engineering on efficient data processing; analysis on insight extraction; and science on outcome prediction and automation.

### Key Data Engineering Use Cases

#### Real-Time Analytics

Real-time analytics is a prominent use case where data engineering plays a crucial role. It involves continuous analysis and processing of data as it comes in, enabling quick decision-making. Businesses leverage this capability to monitor live transactions, customer interactions, and resource utilization. Data engineering supports real-time analytics by enabling high-speed data collection, processing, and analysis infrastructure.

To achieve this, data engineers implement architectures that handle large data volumes at high velocity, using tools like Apache Kafka and Amazon Kinesis. These systems prioritize data stream integrity and timeliness, ensuring that data is available instantly. This form of analytics empowers companies to generate immediate business value, optimize operations, and enhance customer experiences by acting on up-to-the-minute insights.

#### Data Migration

Data migration encompasses the process of transferring data between storage types, formats, or systems. It involves planning and executing the movement of large data sets without disrupting business operations. Data engineers facilitate this transition by ensuring data integrity and consistency across platforms. They use tools like Apache NiFi for orchestrating and automating data flow between different environments.

Effective data migration allows organizations to leverage more scalable, cost-efficient platforms, enhancing their data capabilities. Data engineers address compatibility issues and resolve potential conflicts during migration. They implement error handling and rollback strategies to preserve data quality, ensuring that migrated data is accurate and reliable.

#### Artificial Intelligence

Artificial intelligence (AI) relies heavily on data engineering to provide models with high-quality data sets necessary for training. Data engineers facilitate the collection, cleaning, and preparation of these data sets, allowing AI systems to function optimally. They ensure that data pipelines feeding machine learning models maintain reliability and scalability, handling large-scale data efficiently.

Proper data preparation minimizes biases and enhances the performance of AI models. Data engineers employ feature engineering techniques to extract meaningful variables from raw data, significantly impacting model accuracy. By enabling robust data workflows, engineers allow businesses to deploy AI solutions that drive innovation, automate processes, and enhance decision-making capabilities across various domains.

#### Fraud Detection

Fraud detection is a special case of a mission-critical business function that relies on data engineering. In organizations like payment card providers, data engineers create solutions for identifying anomalies in transactional data. By constructing systems that detect irregular patterns, data engineers play a pivotal role in securing financial transactions. The focus is on deploying real-time detection algorithms that identify and flag suspicious activities promptly.

For effective fraud detection, data engineers enable integration of disparate data sources into cohesive models that enhance detection accuracy. By leveraging machine learning algorithms, they ensure adaptive mechanisms that learn from past fraud patterns, continuously improving detection efficacy. This proactive approach minimizes fraud-related losses and enhances trust in digital financial environments.

### Key Components of Data Engineering

#### Data Extraction/Collection

Data extraction involves retrieving data from various sources, transforming it into a suitable format for processing. Data engineers use techniques and tools such as web scraping and API integration to automate the collection process, ensuring they gather comprehensive data sets. Proper extraction strategies help maintain data accuracy, validity, and consistency, which are essential for downstream applications.

The process also encompasses dealing with unstructured data, turning it into a structured format ready for analysis. This task requires data engineers to employ regular expressions and pattern recognition to identify relevant data points. By mastering data extraction, engineers can guarantee that the foundational data is sound, supporting reliable analytics and modeling efforts.

#### Data Ingestion

Data ingestion refers to the movement of data from various sources into a storage medium conducive for further processing. Data engineers create pipelines to systematically transport, schedule, and handle data in real-time or batch formats. The goal is timely and scalable data integration using tools like Apache Kafka and AWS Kinesis.

This process ensures that data remains current and reflective of the organization’s operational landscape. By optimizing the ingestion process, data engineers minimize latency and enhance the immediacy of data availability. Efficient ingestion supports business strategies that rely on precise, real-time data insights to drive decision-making.

#### Data Storage

Data storage involves choosing appropriate database solutions to store collected data efficiently and securely. This key element requires data engineers to evaluate and implement storage systems based on the type and volume of data, such as data warehouses or SQL databases for structured data and data lakes or NoSQL databases for unstructured data. Storage solutions must also ensure scalability and data retrieval speed.

Data engineers optimize their storage strategies, balancing cost-efficiency with data accessibility. They consider factors such as data redundancy and replication to safeguard data integrity and availability. Effective solutions promote the structured organization of data, supporting a foundation for analytics, reporting, and machine learning deployment within organizations.

#### Data Modeling, Scaling, and Performance

Data modeling involves designing data structures that meet the requirements of various applications. Data engineers create schemas that define relationships within data, supporting efficient querying and retrieval. Modeling decisions impact data scalability and performance, influencing how data systems grow with increasing organizational demands.

Engineers focus on optimizing performance by employing indexing, partitioning, and replication strategies. These techniques enhance data processing efficiency, ensuring systems remain responsive under heavy traffic. Scalable models allow businesses to manage larger data volumes without sacrificing processing speed or accuracy.

#### Data Transformation

Data transformation is the process of converting raw data into a structure fit for analysis. This involves cleaning, filtering, and aggregating data based on business requirements. Data engineers use ETL (extract, transform, load) tools like Apache Spark to streamline processes, ensuring that transformed data meets analytical needs.

Engineers design transformation workflows that automate consistency checks, data type conversions, and enrichment with additional contextual information. Properly transformed data is pivotal for accurate analyses, ensuring that outputs are relevant and actionable.

#### Data Quality and Governance

Data quality and governance are critical for ensuring that data remains accurate, complete, and secure throughout its lifecycle. Data engineers establish quality frameworks that include validation, error detection, and correction procedures to maintain high standards for data integrity. Enforcing governance policies involves setting rules, roles, and responsibilities associated with data usage.

Engineers also facilitate compliance with industry regulations through audits and monitoring, mitigating risks associated with data misuse. By ensuring consistent, governed data, organizations are better equipped to perform accurate analytics and maintain trust with stakeholders.

#### Security and Compliance

Data security is a top priority, involving the protection of sensitive data against unauthorized access and corruption. Data engineers implement encryption, access controls, and secure data handling practices to protect data assets. These security measures align with compliance requirements like GDPR and HIPAA, ensuring organizational adherence to legal standards.

By integrating data security strategies, engineers safeguard against data breaches and potential financial penalties. Compliance efforts foster trust in data systems, enhancing stakeholder confidence in organizational data handling practices.

### What Skills Do Data Engineers Need?

Data engineers require a mix of technical skills and domain knowledge to build reliable, scalable data systems. Key skills include:

1. **Programming and scripting**: Data engineers need proficiency in languages like Python, SQL, and Scala. Python is widely used for scripting and data manipulation, while SQL is essential for querying and transforming datasets. Scala is often used in big data tools like Apache Spark, improving data processing performance.
2. **Data warehousing**: Understanding data warehousing concepts is crucial. Data engineers should know how to structure data in warehouses like Snowflake, Amazon Redshift, or Google BigQuery, ensuring data is easily accessible and efficiently stored.
3. **Data modeling**: Strong data modeling skills help data engineers design schemas that support efficient storage and retrieval. Knowledge of both normalized and denormalized models ensures that data systems can handle application requirements.
4. **ETL and data pipeline management**: Engineers must master ETL (extract, transform, load) processes to transfer data from various sources into a usable format. Familiarity with tools like Dagster, Apache Airflow, Informatica, and Talend enables engineers to automate and optimize pipeline workflows.
5. **Big data technologies**: Big data frameworks like Hadoop and Spark allow engineers to handle massive datasets. Hadoop provides distributed storage and processing, while Spark offers a faster alternative for in-memory data operations. Familiarity with these tools is essential for building scalable data solutions.
6. **Cloud platforms**: With many data engineering workflows moving to the cloud, engineers must know services from AWS, Google Cloud, or Azure. Skills in cloud data storage (like AWS S3 or Google Cloud Storage), database services, and managed data pipeline solutions are in high demand.
7. **Data governance and security**: Data engineers should be knowledgeable in data governance frameworks to ensure data quality, compliance, and security. Skills in encryption, role-based access control, and familiarity with compliance standards (such as GDPR or HIPAA) are increasingly necessary.
8. **Version control and CI/CD**: Version control tools like Git and CI/CD practices simplify collaboration and ensure consistency in code and schema management. Engineers use these tools to track changes and integrate updates smoothly, minimizing disruptions in data systems.

### Best Practices for Successful Data Engineering

#### 1. Automate Data Pipelines

Automating data pipelines enhances processing efficiency and reduces manual intervention. Data engineers focus on building workflows that streamline data extraction, transformation, and loading processes, often using tools like Dagster to orchestrate elements across the pipeline. Automation minimizes errors, lowers operational costs, and accelerates data availability for analytics and machine learning tasks.

By implementing automation, engineers ensure pipelines adapt to changeable data requirements, managing dependencies and failures proficiently. This consistency boosts the reliability and robustness of data systems. It allows engineers to focus on optimizing processes rather than addressing repetitive tasks, facilitating scalable infrastructure.

#### 2. Implement Data Quality Checks

Ensuring high data quality is paramount for reliable analytics and decision-making. Data engineers implement automated quality checks within pipelines to validate data accuracy, completeness, and consistency. Techniques include schema validation, anomaly detection, and error reporting to identify and rectify data issues promptly.

Proactive quality management prevents downstream data corruption and inaccurate analyses. It supports organizational confidence in data-driven strategies, leading to more informed business decisions. High data quality fosters efficient use of data resources, maximizing the potential for insightful analysis and prediction across various business domains.

#### 3. Use Version Control for Data Schemas

Version control for data schemas ensures consistency and traceability of changes over time. Data engineers apply practices similar to software development, using tools like Git for tracking schema modifications. This approach facilitates collaboration among teams, maintaining a clear history of adjustments to the structural framework.

Implementing version control reduces the risk of errors in data models and enhances the ability to manage schema evolution in dynamic environments. It allows for rollback and experimental testing without disrupting production systems. Clear schema versioning supports meticulous quality assurance and smooth development processes, safeguarding data integrity.

#### 4. Ensure Fault Tolerance and Data Recovery

Fault tolerance and data recovery are essential for robust data systems. Data engineers design architectures that withstand failures, utilizing techniques like data replication, redundancy, and automated backups to safeguard data integrity. Tools like Apache Hadoop offer built-in fault tolerance to handle node failures without data loss.

Engineers establish recovery strategies to restore data quickly and accurately from disruptions. These systems secure business continuity, protecting against potential data loss or operational downtime. By prioritizing fault tolerance and recovery, engineers enhance system reliability, ensuring data systems serve organizational needs consistently.

#### 5. Maintain Clear Documentation

Clear documentation is critical for understanding and maintaining data systems. Data engineers develop comprehensive documentation covering architecture, data flow, pipeline processes, and troubleshooting procedures. Effective documentation facilitates knowledge transfer, enabling teams to quickly onboard and manage data systems efficiently.

Well-maintained documentation helps identify, address, and prevent issues, ensuring smooth operation and easier system audits. It promotes collaboration, providing a reference that aligns engineering efforts with organizational goals.

<strong><em>Related content: Read our guide to data engineering tools (coming soon)</em></strong>

### Making Data Engineering Easier with Dagster

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle.

It boasts an intuitive programming model, representing data pipelines in terms of the data assets that they generate, yielding an intuitive, declarative mechanism for defining complex data processes and their dependencies.

Thanks to its modular design and rich feature set, Dagster serves as the unified control plane for data processes across the organization, with native support for lineage and observability, an asset catalog, data validation checks, and best-in-class testability.

With composable integrations with popular data tools, native support for executing non-Python workflows in remote environments, declarative automation, and first-class support for partitioning, Dagster lets you seamlessly centralize and scale your data processes.

[**Learn more about the Dagster platform here**](https://dagster.io/platform-overview)**.**

‍

---

## Data Ingestion: How It Works, Challenges & Best Practices

> Data ingestion is the initial process of collecting and transferring data from diverse sources to a centralized destination like a data warehouse or data lake, preparing it for storage, processing, and analysis. This step pipeline helps organize raw data from various inputs into a consistent format.

Source: https://dagster.io/learn/data-ingestion

### What Is Data Ingestion? 

Data ingestion is the initial process of collecting and transferring data from diverse sources to a centralized destination like a data warehouse or data lake, preparing it for storage, processing, and analysis. 

This critical step in the [data pipeline](https://dagster.io/learn/data-pipeline) helps organize raw data from various inputs, such as databases, IoT devices, or SaaS applications, into a consistent format, enabling organizations to gain insights and make data-driven decisions.  

Data ingestion sources data from a range of origins, including: 

- Databases (on-premises and cloud)
- SaaS applications 
- IoT devices
- File systems
- APIs
- Social media platforms
- Third-party data providers

The collected data is moved to a target storage system, such as: 

- A data warehouse
- A data lake
- A data mart
- A centralized database

### Why Is Data Ingestion Important? 

Data ingestion is a critical step in enabling data-driven decision-making across modern organizations. Without a consistent and scalable way to move data from source systems to destinations, analytics, reporting, and machine learning workflows cannot function effectively.

- **Foundation for analytics and BI:** Data ingestion provides the raw input that powers dashboards, reports, and analytics tools. Timely and accurate ingestion is essential for insights that reflect current business conditions.
- **Enables real-time decision making:** Ingesting data in real time or near real time supports applications that require immediate insights, such as fraud detection, monitoring systems, or recommendation engines.
- **Supports data consolidation:** Organizations often rely on data from multiple systems (e.g., CRM, ERP, sensors). Ingestion pipelines unify this data into a single repository or data lake.
- **Feeds data lakes and warehouses:** Most data lakes and warehouses rely on automated ingestion processes to stay updated.
- **Maintains data consistency across systems:** Ingestion ensures that different parts of an organization are working with the same data, reducing the risk of conflicting information.
- **Scales with business growth:** As data volume and variety increase, ingestion frameworks help scale data operations without sacrificing performance or reliability.

### Key Aspects of Data Ingestion

#### Source Identification and Discovery

The initial step in any data ingestion pipeline is to identify and discover data sources. This involves detecting where the relevant data resides, such as relational databases, files in cloud storage, sensor streams, or external APIs. Tools often automate this discovery process, providing connectivity options that support a wide range of endpoints.

Automated source discovery features typically include connection templates, connectors, or agent-based integrations that catalog available data locations and their respective structures. These capabilities help organizations cut down the time required for onboarding new data assets.

#### Data Extraction / Collection

Data extraction or collection refers to the process of pulling data from identified sources. Depending on the data source, extraction might mean querying a database, listening to an event stream, scraping files, or invoking web APIs. The collection process requires connectors or adapters that can interface with diverse source systems and protocols, handling structured formats like SQL tables, as well as semi-structured or unstructured data such as JSON, XML, or plain text logs.

Efficient extraction handles complexities like source throttling, incremental pulls (e.g., only new or changed data), and error management. Good extraction practices also account for changes in source schemas, downtime recovery, and source system performance.

#### Staging / Buffering

Staging or buffering is a critical phase in the data ingestion process, acting as an intermediate step before the data is loaded into the target environment. In this phase, ingested data is temporarily stored in a buffer or staging area to decouple the data fetching speed from the data loading speed. This mechanism enables smoother data flows, helps with rate-limiting challenges, and provides a cushion for handling bursts in incoming data or transient failures in downstream systems.

Buffering ensures that even if sources or targets experience contention or brief outages, no data is lost and pipeline integrity is maintained. Importantly, the staging area can support bulk loading, batch operations, and enable retries on failed loads without re-querying the source.

#### Data Validation

Data validation involves checking the ingested data against defined rules to ensure accuracy, consistency, and completeness before the data proceeds downstream. This might include verifying formats (such as date strings), checking for null values or missing records, and confirming referential integrity. Automated validators and rule engines are often embedded in ingestion tools, allowing for real-time detection and flagging of anomalies or corrupt records.

Effective validation serves as a safeguard against ingesting faulty or malicious data that could compromise downstream analytics or cause system errors. In high-volume environments, scalable validation mechanisms become crucial to maintain [data quality](https://dagster.io/learn/data-quality) without introducing significant latency.

#### Transformation

Transformation refers to modifying, cleaning, or normalizing data as it moves through the ingestion pipeline so it can be effectively used by downstream systems. Common transformations include schema mapping, data type conversion, deduplication, enrichment with reference data, or standardizing units and string formats. Some ingestion tools offer transformation capabilities as tightly integrated features, while others may hand off this responsibility to dedicated ETL or processing platforms.

Early-phase transformation ensures that only relevant, usable, and high-quality data enters the organization’s analytics ecosystem. It helps align disparate sources with target schemas and business requirements, reducing the need for repeated reprocessing.

#### Metadata Management

Metadata management involves capturing information about the ingested data, such as its origin, schema details, timestamps, lineage, and quality metrics. Robust metadata management capabilities enable teams to trace data provenance, understand data context, and support audit and compliance needs. Ingestion tools often automate the gathering and cataloging of metadata, integrating with broader data governance systems.

By maintaining comprehensive metadata, organizations enable operational transparency and support regulatory requirements. Metadata also improves discoverability for data analysts and machine learning workflows, ensuring efficient data usage and reducing duplication.

**Tips from the expert:**

In my experience, here are tips that can help you better optimize and harden your data ingestion pipelines:

1. **Use change data capture (CDC) to minimize load:** Instead of repeatedly extracting full datasets, leverage CDC to ingest only what has changed. This significantly reduces bandwidth, source impact, and downstream reprocessing, especially in high-volume transactional systems.<br><br>
2. **Abstract ingestion logic from pipeline infrastructure:** Decouple source-specific extraction logic from orchestration and transformation layers using plugin-based architectures or wrappers. This allows teams to swap sources, version connectors, and evolve ingestion logic without disrupting the broader pipeline.<br><br>
3. **Establish a centralized error lake:** Don’t discard or ignore failed records. Route all ingestion failures, including parse errors, validation issues, or schema mismatches, into a centralized "error lake" with metadata tags. This preserves data for reprocessing, debugging, or audit while maintaining pipeline continuity.<br><br>
4. **Incorporate lineage at the ingestion level:** Embed lineage tracking from the very first byte ingested: tag every record with origin source, timestamp, ingestion version, and transformation status. This provides forensic-level traceability, critical for compliance, rollback, and advanced debugging.<br><br>
5. **Use schema fingerprinting for early anomaly detection:** Generate hash-based fingerprints of incoming schema structures and compare against previous versions. Unexpected drift, even without field renames, can then trigger alerts or quarantine processes *before* failures occur downstream.

### Data Ingestion vs. Data Integration 

Data ingestion and data integration are often used interchangeably, but they serve distinct roles within the data ecosystem. **Data ingestion** is primarily concerned with getting data from one or more sources into a central location, like a data lake or warehouse, regardless of its state or structure. The process emphasizes the timeliness and reliability of data movement, ensuring that disparate datasets are captured as-is for subsequent processing or exploration.

**Data integration** focuses on harmonizing, reconciling, and unifying data from multiple sources to provide a consistent view for analytics or operational purposes. This phase involves more complex transformations, joining datasets, resolving data conflicts, and aligning to business rules or models.

### Types and Models of Data Ingestion 

#### Batch Ingestion

Batch ingestion consolidates data in sizeable chunks and processes it at scheduled intervals. This approach is well-suited for scenarios where immediate data availability isn't critical, such as nightly updates from transaction databases or warehouse reloads. Batch processes support high-volume workloads by grouping data into logical units, minimizing the overhead associated with frequent reads or writes and reducing the strain on source systems.

Another advantage of batch ingestion is its operational simplicity and reliability, as entire datasets can be validated before loading, and failure recovery is often more straightforward. However, it introduces latency, making it unsuitable for use cases requiring up-to-the-minute data.

#### Real-Time Ingestion

Real-time ingestion delivers data as it’s generated, minimizing lag between data arrival and availability for use. Common in use cases like fraud detection, live dashboarding, and sensor data processing, real-time pipelines can process events individually or in very small batches. Achieving real-time performance requires robust architectures that handle in-flight data without bottlenecks or excessive latency, often leveraging streaming platforms and event-driven technologies.

While real-time ingestion maximizes data freshness and responsiveness, it introduces higher complexity in terms of scaling and error handling. Ensuring that frequent updates or high-velocity events don’t overwhelm downstream systems is a key challenge.

#### Microbatching

Microbatching offers a trade-off between the extremes of batch and real-time ingestion. Instead of processing massive daily or hourly loads or each event individually, microbatching collects small sets of records over brief intervals (such as every few seconds or minutes) and ingests them together. This approach takes advantage of batching efficiencies while keeping latency low enough for near-real-time applications.

Microbatching is often used with stream processing frameworks to smooth traffic spikes and manage throughput, balancing resource usage more effectively than pure real-time systems. It’s useful when data must be available quickly but absolute event-level immediacy isn’t necessary.

#### Stream Processing

Stream processing is a continuous, unbroken flow of data through the ingestion pipeline, enabling on-the-fly analytics, transformation, and alerting. Stream processing systems process each inbound event or message in real time, delivering data to applications and storage with very low latency. This model supports event-driven architectures and scenarios where prompt response to incoming data is both expected and critical.

Implementing stream processing requires specialized systems capable of handling large, fast-moving input, such as Apache Kafka, Apache Flink, or AWS Kinesis. The architecture must guarantee message order, fault tolerance, and state management.

#### Hybrid Approaches

Hybrid data ingestion combines two or more methods, like batch and real-time, to address complex requirements in dynamic environments. For example, an organization may process critical events in real time while handling less urgent, historical data through scheduled batch jobs. 

This approach allows teams to tailor the pipeline to different data sources, use cases, and SLAs, extracting the benefits of each ingestion model. Building hybrid ingestion pipelines requires careful coordination and orchestration, ensuring that the resulting data remains consistent and coherent.

### Key Data Ingestion Use Cases and Applications 

#### Cloud Data Lake Ingestion

Cloud data lakes are central repositories that store structured and unstructured data at scale, supporting analytics and machine learning. Data ingestion tools enable the efficient movement of data from legacy systems, databases, file stores, and external sources into these cloud environments. 

The ingestion process must handle a wide variety of data formats, volumes, and sources, ensuring integration and timely availability for analytic workflows. Modern data lake ingestion pipelines often incorporate transformation, validation, and metadata management directly into the loading process to maintain data quality and governance.

#### IoT and Streaming Data

IoT (Internet of Things) and streaming data scenarios involve massive volumes of small, high-frequency data points generated by connected devices, sensors, or telemetry systems. Data ingestion platforms must be able to handle the unique scale, variability, and intermittent connectivity of these environments.

Robust ingestion solutions connect to edge devices, normalize input formats, and buffer or pre-process data in transit before delivering it to central storage or analytic platforms. Managing streaming ingestion for IoT also requires strong error handling, security, and fine-grained metadata tagging to support device management and traceability.

#### Cloud Modernization

Cloud modernization efforts often involve migrating data workloads from on-premises systems to cloud platforms, requiring seamless, efficient, and reliable data ingestion processes. The migration typically includes bulk transfers (batch ingestion) for historical data and real-time or near-real-time synchronization to minimize downtime and ensure data consistency during cutover. 

Automated tools provide connectors for diverse source systems, manage schema mapping, and orchestrate loads to the cloud. Successful data ingestion for modernization addresses issues like scaling transfer rates, monitoring process health, and handling schema or data mismatches.

### Common Challenges in Data Ingestion 

Here are some of the main factors that can complicate the data ingestion process.

#### High Volume and Velocity Handling

Handling massive volumes and high-velocity data streams is a fundamental challenge, particularly for industries like finance, e-commerce, and telecommunications. Ingestion tools must be built to scale horizontally, distributing workloads across multiple nodes and optimizing throughput to avoid bottlenecks. Techniques such as partitioning, sharding, and parallel processing become essential to ensure that spikes in incoming data don’t overwhelm the system.

#### Latency and Reliability

Low latency is critical for use cases like real-time analytics or instant alerting, where any processing delay can have business impacts. Minimizing latency calls for simplifying extraction, validation, and delivery processes, while also ensuring each step is reliable and fault-tolerant. Failure in any stage of the pipeline must not result in lost or corrupted data, prompting the need for strong retry and recovery mechanisms.

#### Heterogeneous Data Formats

Modern pipelines ingest data from a mix of structured, semi-structured, and unstructured sources, each with unique schemas, encodings, and quality attributes. Tools must support seamless parsing, transformation, and normalization across these formats, accepting SQL tables, CSV files, JSON, XML, log files, and images.

#### Monitoring and Observability

Effective monitoring and observability are essential to ensure that data ingestion pipelines operate as intended and can quickly surface issues before they impact business-critical workflows. Organizations need visibility into pipeline health, throughput, error rates, and latency metrics, as well as detailed logs and traces for debugging and auditing. Modern ingestion platforms often provide real-time dashboards and alerts that enable proactive issue remediation.

#### Upstream Changes

Changes in upstream systems, such as databases, APIs, or applications, pose a significant risk to data ingestion pipelines. These changes can include schema modifications, altered field names, deprecated endpoints, or even shifts in update frequency. If not handled properly, such changes can cause ingestion failures, data corruption, or silent data quality issues that propagate downstream.

### Best Practices for Building Data Ingestion Pipelines 

Organizations can improve the efficiency of their data ingestion pipelines by incorporating these practices.

#### 1. Design for Scalability

A scalable ingestion pipeline can seamlessly handle both steady growth and sudden surges in data volume and velocity. This requires a modular, distributed architecture that supports horizontal scaling, adding more nodes or resources as demand increases. Leveraging cloud-native services, partitioned message queues, and auto-scaling infrastructure enables dynamic adjustment to workload fluctuations without service interruption.

Planning for scalability also involves efficient resource management, such as optimizing network usage and balancing loads across ingestion and transformation steps. A loosely coupled, microservices-based design makes it easier to isolate and expand bottlenecked components.

#### 2. Ensure Data Quality at the Source

Embedding data quality checks at the source minimizes the risk of bad data propagating through downstream systems. This can be accomplished by applying validation rules, format checks, and enrichment during the initial extraction phase, well before the data reaches critical analytics or operational processes. Early detection of anomalies, corrupt records, or out-of-range values reduces troubleshooting workload and prevents data-driven errors.

Automated quality gatekeeping should extend to schema consistency, referential integrity, and completeness verification. Integrating quality monitoring within the ingestion process, rather than treating it as a post-processing task, also simplifies compliance, audit efforts, and business intelligence accuracy.

#### 3. Implement Robust Monitoring and Logging

Logging every stage of data ingestion, from extraction through validation and loading, creates transparency and aids in root-cause analysis for failures or inconsistencies. Real-time monitoring dashboards provide visualization for pipeline health, throughput, and errors, enabling rapid issue identification and response.

Automated alerting, combined with detailed historical logs, makes it easier for teams to detect unexpected patterns or degradation over time. Integrating observability into the ingestion pipeline is also important for supporting SLAs, auditing, and governance.

#### 4. Optimize for Latency and Throughput

Balancing low latency and high throughput is essential for pipelines that support real-time analytics or deal with high-velocity data streams. Optimizing for latency involves reducing unnecessary intermediate steps, fine-tuning buffer capacities, and selecting protocols and serialization formats that minimize transmission overhead. For throughput, proper partitioning, parallel processing, and efficient batch sizing are crucial.

Continuous performance profiling allows ingestion teams to pinpoint slow-downs, whether in network transmission, transformation logic, or target system writes. Optimizations should be an ongoing process, tied to specific SLAs and end-user requirements.

#### 5. Secure Data in Motion and at Rest

Securing data is a primary responsibility for ingestion pipelines, both during transit and once stored. Encryption protocols like TLS/SSL should be enforced on all data transmissions, while storage encryption protects against breaches or unauthorized access once data is landed. Access controls, authentication, and integration with enterprise IAM (Identity and Access Management) systems further protect sensitive content.

Security best practices include rigorous monitoring for unauthorized access attempts, regular reviews of audit logs, and prompt patching of vulnerabilities in all pipeline components. Data masking and tokenization techniques may also be employed for particularly sensitive or regulated data. Effective security at every stage helps maintain customer trust and meet legal or industry compliance mandates.

#### 6. Automate Schema Evolution Handling

Data sources and business requirements inevitably change, leading to evolving schemas over time. Manual management of these changes is impractical, especially in high-velocity or distributed environments. Automation tools that detect schema changes, reconcile differences, and trigger necessary updates to downstream systems ensure ingestion pipelines can adapt with minimal disruption.

Intelligent schema evolution mechanisms can include dynamic mapping, backward and forward compatibility strategies, and automated documentation updates. By proactively handling schema drift, organizations maintain pipeline stability and data integrity even as underlying source systems and data models evolve, reducing operational risk and technical debt.

### Supporting Data Ingestion with Dagster

Dagster provides a modern orchestration platform purpose-built to manage the complexity of today’s data ingestion workloads. By combining strong abstractions, integrated observability, and a software-engineering-centric approach to pipeline development, Dagster enables teams to build ingestion systems that are reliable, testable, scalable, and easy to evolve.

#### Unified Orchestration for Batch, Streaming, and Hybrid Ingestion

Data ingestion pipelines often involve a mix of schedules, triggers, event-driven logic, and service integrations. Dagster offers a single orchestration layer that supports all these execution patterns while keeping implementation details cleanly separated from business logic.

With Dagster, teams can orchestrate:

- **Batch ingestion workflows** on fixed schedules
- **Event-based pipelines** triggered by file drops, API events, or sensor activity

Dagster’s declarative definitions and software-defined assets make it easy to express how data flows across ingestion boundaries, regardless of the underlying technologies involved.

#### Built-In Observability and Data-Aware Monitoring

Observability is often one of the most challenging aspects of ingestion pipelines. Dagster provides detailed, real-time visibility out of the box. Each ingestion run produces structured metadata, event logs, asset materializations, and lineage information that allow teams to quickly diagnose failures or trace upstream data issues.

Key capabilities include:

- **Asset lineage graphs** to trace ingestion inputs through downstream transformations
- **Automated metadata capture**, including row counts, schema details, and data quality markers
- **Run-level and asset-level monitoring**, enabling alerts on freshness, failures, or SLA breaches
- **Type checking and built-in validation hooks**, ensuring data quality at ingestion time

This level of transparency gives engineering and analytics teams confidence that pipelines are operating correctly and provides quick insight into the root cause when they aren’t.

#### Managing Schema Evolution and Upstream Changes

Dagster’s asset-based model tracks dependencies explicitly, enabling pipelines to adapt gracefully to upstream drift. Using asset checks, metadata comparisons, and custom logic, teams can detect schema changes early, automatically trigger required transformations, or quarantine affected ingestions before failures propagate downstream.

Because Dagster treats assets as first-class entities, schema drift becomes an event the system can react to, not a hidden failure mode.

#### Reliability, Idempotency, and Recovery Made Simple

Ingestion pipelines must be resilient to transient failures, rate limits, and partial data delivery. Dagster offers first-class tools for:

- **Retries with configurable backoff**
- **Idempotent asset materializations**
- **Checkpoints and handoffs** that prevent re-ingestion of already-processed data
- **Error isolation**, ensuring a failed ingestion step doesn’t break unrelated assets

The result is robust ingestion pipelines that remain stable even as data volumes, velocities, or upstream systems fluctuate.

#### Bringing It All Together

Whether ingesting data from SaaS APIs, streaming systems, operational databases, or cloud storage, Dagster provides the orchestration, observability, reliability, and developer workflow modern ingestion pipelines require. By making assets, dependencies, and metadata explicit, Dagster transforms ingestion from a fragile, opaque process into a well-governed, maintainable part of the data platform.

---

## Data Integration in 2025: 4 Pillars & 5 Technical Approaches

> Data integration is the process of combining data from various sources into a unified and consistent view. It involves extracting, transforming, and loading (ETL) or extracting, loading, and transforming (ELT) data from diverse systems, ensuring data quality and consistency.

Source: https://dagster.io/learn/data-integration

### What Is Data Integration 

Data integration is the process of combining data from various sources into a unified and consistent view. It involves extracting, transforming, and loading (ETL) or extracting, loading, and transforming (ELT) data from diverse systems, ensuring data quality and consistency. This unified view enables better analysis, decision-making, and overall business intelligence.

Key aspects of data integration include:

- **Consolidation:** Bringing together data from multiple sources into a single repository or system. 
- **Transformation:** Converting data into a consistent format and structure suitable for analysis and reporting. 
- [**Data quality**](https://dagster.io/learn/data-quality)**:** Ensuring accuracy, completeness, and reliability of the integrated data.<strong> </strong>
- **Accessibility:** Providing users with a unified view of data for various purposes like reporting, analytics, and application integration.

Data integration is important for:

- **Improved decision-making:** Provides a holistic view of data, leading to more informed and accurate decisions. 
- **Enhanced business intelligence:** Enables comprehensive analysis and insights across different business functions. 
- **Increased operational efficiency:** Simplifies data access and reduces manual data handling. 
- **Support for digital transformation:** Crucial for leveraging data to drive innovation and adapt to a digital landscape, according to Google Cloud. 
- **Reduced redundancy and errors:** Eliminates data silos and inconsistencies, minimizing errors and improving data quality.

This is part of a series of articles about [data pipeline](https://dagster.io/learn/data-pipeline)

### Pillars of Data Integration 

Here are a few essential aspects of most data integration projects.

#### 1. Consolidation

Consolidation is the process of collecting and combining data from multiple sources into a single repository. This could include merging databases, aggregating logs, or centralizing files from different branches or departments. By bringing all relevant data together, organizations can reduce redundancy, streamline management, and lay the groundwork for analytics or machine learning initiatives.

Proper consolidation requires careful planning around schema mapping, data formats, and deduplication. If handled incorrectly, consolidation can result in lost information, duplicated records, or conflicting data structures. Successful projects minimize these risks while enabling access to a comprehensive data view.

#### 2. Transformation

Transformation involves converting data from its original format or structure into one suitable for analysis, storage, or further processing. This can include standardizing field types, calculating new metrics, or normalizing values. Transformation ensures that inconsistencies or mismatches across data sources do not hinder downstream operations or analytics.

A major challenge in transformation is preserving the original data’s meaning and accuracy while adapting it to meet integration requirements. Rules for transformation should be well-documented and automated wherever possible to reduce manual intervention and errors. Good transformation practices enable faster decision-making and more reliable reporting across the organization.

#### 3. Data Quality

Maintaining high data quality is essential in any integration project. Data quality checks involve detecting and fixing issues like incomplete, duplicate, or inconsistent entries. The integration process should enforce rules for validation, cleansing, and enrichment to ensure that only reliable data enters the unified system.

Poor data quality can have downstream effects, from incorrect analytics to operational disruptions. Continuous monitoring and remediation are necessary to uphold standards as new data sources are added. By prioritizing data quality, organizations build trust in integrated data assets and reduce the risk of costly errors in both strategic and operational contexts.

#### 4. Accessibility

Accessibility ensures that integrated data can be easily and securely accessed by users and applications that need it. Well-designed integration platforms provide role-based access, support various querying methods, and enable interoperability with analytics or operational tools. This accessibility accelerates time-to-insight and empowers teams with self-service capabilities.

However, accessibility should not come at the cost of security or compliance. Organizations must address permission controls, audit trails, and encryption as part of their integration strategy. When accessibility and governance are balanced, the integrated data environment supports agility, innovation, and strong risk management.

### Technical Approaches to Enterprise Data Integration 

Here are some of the technical methods for implementing data integration in an enterprise. Many integration projects combine two or more of these techniques.

#### 1. ETL (Extract, Transform, Load)

ETL is a traditional batch-based integration approach where data is first extracted from original sources, then transformed to ensure quality and consistency, and finally loaded into a target data warehouse or database. ETL workflows are especially effective when dealing with large volumes of structured data that can be processed in scheduled batches, such as nightly database synchronizations or historical data migrations.

A challenge with ETL is latency between extraction and availability due to the batch model, which makes it less suited for real-time use cases. However, it excels where data quality requirements demand complex transformations or where compliance necessitates data cleansing before loading. Many organizations leverage ETL for strategic reporting, business intelligence, and regulatory compliance workloads.

#### 2. ELT (Extract, Load, Transform)

ELT reverses the traditional ETL process by loading raw data into a storage system, such as a cloud data lake or modern data warehouse, before performing transformations in place. This approach leverages the scalable compute and storage capabilities of new platforms, allowing organizations to ingest varied and high-volume data without bottlenecks during transformation.

This method supports more flexible and agile analytics, enabling data scientists or analysts to define and apply transformations as needed. It is especially advantageous for big data environments or unstructured data. However, organizations must monitor storage and compute costs, as well as maintain good governance to avoid data sprawl or increasing complexity over time.

#### 3. Data Virtualization

Data virtualization provides a unified access layer over multiple data sources without actually moving or duplicating the data. This logical integration model enables users or applications to query and consume data as if it resided in a single location, simplifying both operations and governance.

This approach reduces storage costs and eliminates data silos while maintaining source-system autonomy and minimizing data movement. However, virtualization often faces performance limitations for large or complex queries, and real-time data consistency depends on source system availability. It is best suited for federated analytics or use cases where frequent integration of new sources is required.

#### 4. Data Streaming and Real-Time Integration

Data streaming and real-time integration enable organizations to ingest, process, and integrate data as events happen. Technologies like Apache Kafka, AWS Kinesis, or cloud-native event streaming platforms allow businesses to keep data continuously updated across operational systems and analytics platforms.

Real-time integration is critical for use cases such as fraud detection, IoT telemetry, or personalized digital services, where delayed data can lead to missed opportunities or operational failures. Configuring low-latency pipelines and ensuring message integrity are key challenges, but the payoff is an always-current view of the business and its environment.

#### 5. API-Driven Integration

API-driven integration connects disparate systems by exposing, consuming, and orchestrating data through standardized or custom APIs. This method is highly flexible, supporting both point-to-point integration and complex workflows across SaaS platforms, legacy systems, and mobile applications.

APIs enable organizations to react quickly to new integration needs, automate data movement, and enforce security or validation at the interface level. However, successful API integration requires strong version management, documentation, and monitoring to ensure reliability as dependencies evolve. It is especially powerful in microservices architectures, digital ecosystems, or multi-cloud environments.

### Data Integration Use Cases 

Organizations can derive significant value from data integration projects. Here are a few of the common use cases.

#### Business Intelligence and Analytics

Data integration directly supports business intelligence by ensuring that reporting platforms and analytics tools have timely, consistent access to all relevant data sources. By bringing together sales, financial, operational, and customer data, organizations can create dashboards and insights based on comprehensive information, driving smarter strategy and tactical decisions.

Integration reduces time to insight by eliminating data silos and manual data preparation tasks. It also helps analytics teams correlate trends or outliers across multiple business domains, leading to more precise forecasting, performance tracking, and opportunity identification.

#### Customer 360 and Personalization

A unified customer view, commonly referred to as "customer 360", depends on integrating data from CRM systems, support platforms, marketing channels, and even social media. Integration enables companies to understand customer behavior, preferences, and interactions across all touchpoints, allowing for highly-personalized marketing and service.

Personalization initiatives powered by integrated data can drive higher engagement, retention, and revenue through tailored offers or recommendations. Reliable integration frameworks are critical to maintaining up-to-date and accurate profiles, supporting both B2C and B2B strategies as customer journeys become more complex.

#### Data Migration and Modernization

As organizations adopt new platforms, migrate to the cloud, or retire legacy applications, data integration becomes central to successful migration. Integration technologies can manage extract, transformation, and loading tasks during migrations, ensuring that business operations are not interrupted and no data is lost or corrupted.

Modernization initiatives often require harmonizing data formats and schemas between old and new environments. Integration minimizes downtime, simplifies compliance with regulatory standards, and accelerates overall migration. These benefits allow organizations to transition to modern platforms with better scalability, security, and performance.

#### Real-Time Operational Intelligence

Real-time integration provides the foundation for operational intelligence systems, which monitor transactions, processes, and events to optimize performance or preempt issues. Examples include logistics tracking, supply chain monitoring, and predictive maintenance powered by IoT sensor data.

By exposing up-to-the-second data to dashboards and alerting platforms, operational teams can enact rapid responses and improve service levels. Real-time integration is crucial for industries where seconds matter, such as finance, energy, or transportation, enabling data-driven action at the point of need.

### Challenges in Data Integration 

While data integration is a strategic initiative, it raises major challenges for organizations, especially considering the high volume, velocity, and variety of data in a modern IT environment.

#### Managing Disparate Data Sources and Formats

Integrating data from diverse sources presents technical and organizational challenges. Data comes in various formats, structured, semi-structured, and unstructured, and might use different schemas, encodings, or standards. Mapping relationships and values between heterogeneous systems requires careful planning and sometimes custom connectors or middleware.

Additionally, legacy systems often lack robust integration interfaces, making it harder to access or export data. This disparity increases integration complexity and the risk of errors during extraction or loading. Effective solutions depend on thorough source analysis and adoption of tools that can normalize diverse data types on the fly.

#### Handling Massive Data Volumes

Modern organizations generate and collect data at enormous scales, from transactional logs and IoT devices to social media feeds and video streams. Integrating such high-volume data sets requires infrastructure that can ingest, process, and store large quantities efficiently. Traditional systems often struggle with throughput limitations, making it essential to adopt distributed architectures, parallel processing, and stream-based ingestion to maintain performance and responsiveness.

Another challenge is ensuring timely access and transformation of this data without introducing bottlenecks. As volumes grow, so do the risks of latency, data loss, and incomplete loads. Solutions must include scalable storage layers (like cloud object stores or columnar databases), efficient transformation pipelines, and monitoring tools that can flag issues early. Capacity planning, load testing, and cost optimization are ongoing needs in environments where data volumes can grow unpredictably.

#### Handling Low-Quality or Outdated Data

When integrating data, existing quality issues, such as missing values, duplicates, or outdated records, can spread across systems and undermine trust. Ensuring incoming data meets business rules and quality thresholds is essential to avoid compounding problems and misleading end users.

Continuous profiling and cleaning processes help catch discrepancies before data is stored or exposed to users. Integration frameworks should also automate validation and enrichment wherever possible to reduce manual workloads and maintain data reliability as source systems evolve or expand.

#### Scaling for Big Data Environments

The exponential growth of data volume, velocity, and variety demands scalable integration architectures. Traditional workflows can become bottlenecks when handling petabytes of data or terabytes per hour streaming from IoT, web logs, or transactional sources. Organizations need architectures that can parallelize processing, optimize storage, and manage spikes in data flow.

Modern integration solutions often tap into cloud-native features such as serverless compute, elastic storage, and distributed processing. Scalability comes with its own set of monitoring and cost-control challenges, requiring ongoing architectural reviews as data estates grow and business requirements evolve.

#### Ensuring Security and Compliance

Integrating data increases the surface area for security risks and regulatory scrutiny. Sensitive data may need to be masked, encrypted, or segregated at every stage of the integration pipeline. Roles, policies, and access controls must be enforced in both source and destination environments.

Compliance adds another layer of complexity—data residency, retention, and audit-trails become critical for regulations like GDPR, HIPAA, or industry-specific mandates. Ongoing review of integration pipelines, automated alerting, and regular penetration testing are key to maintaining a secure and compliant data integration landscape.

### Best Practices for Implementing Data Integration Tools

Here are a few practices that can help make an enterprise data integration effort a success.

#### 1. Establish a Common Data Model

A common data model creates a standard structure, vocabulary, and relationship map for integrated data, reducing ambiguity and simplifying downstream usage. By defining how data from different sources is unified, organizations ensure consistency and reusability across integration projects, analytics, and reporting.

This approach accelerates onboarding of new data sources and increases the quality of integrated data. Communicating the common model throughout the organization also fosters better cross-team collaboration and faster troubleshooting when issues arise in integration pipelines.

#### 2. Prioritize Data Governance and Metadata Management

Strong data governance ensures that data integration complies with organizational policies, security standards, and regulatory requirements. It defines roles, responsibilities, and access controls for managing data across its lifecycle. Without governance, integrated environments can become fragmented, leading to inconsistencies, unauthorized access, or audit failures.

Metadata management provides context and traceability by cataloging where data comes from, how it has been transformed, and where it is used. This transparency improves trust and makes troubleshooting easier. Effective metadata strategies also support data discovery, lineage analysis, and documentation across integration workflows.

#### 3. Automate Integration Pipelines

Automation accelerates and stabilizes integration workflows, reducing reliance on manual tasks that are error-prone and slow. Automated scheduling, pipeline management, and transformation logic enable teams to scale their integration landscape efficiently, supporting both batch and real-time requirements.

Emerging tools allow declarative or “pipeline-as-code” solutions, enhancing traceability and reducing drift between development and production. Automation also ensures repeatable, auditable integration routines, crucial for compliance and operational confidence.

#### 4. Monitor and Test Continuously

Integrated data environments are dynamic, and continuous monitoring is required to promptly detect failures or quality issues. Implementing real-time metrics, alerts, and logging helps resolve problems before they affect users or downstream systems. Synthetic tests or end-to-end validation routines catch integration drift or unexpected schema changes.

Regular regression testing and ongoing validation of data quality, volume, and performance help maintain trust in integrated outputs. Proactive monitoring is key to reliable data operations, supporting business users who rely on timely, accurate information for critical decisions.

#### 5. Design for Flexibility and Future Growth

Data integration environments should be built to adapt to evolving business needs and technology changes. This includes modular architecture, support for new data sources, and the ability to scale workloads across cloud and on-prem systems. Flexibility allows teams to integrate new platforms or services without reengineering the entire pipeline.

Planning for growth also involves setting up infrastructure that can handle increasing data volume, velocity, and variety. Leveraging containerized services, cloud-native storage, or distributed processing frameworks ensures the integration stack can expand seamlessly. A future-ready design reduces technical debt and extends the lifespan of integration investments.

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data ingestion tools</em></strong>](https://dagster.io/guides/10-data-ingestion-tools-to-know-in-2025-open-source-paid)

### Powering Data Integration Pipelines with Dagster

Dagster is a modern data orchestration platform designed to help teams build, run, and maintain reliable data integration pipelines at scale. Rather than focusing only on task scheduling, Dagster treats data assets as first-class concepts, making it easier to reason about how data is produced, transformed, and consumed across an organization. This asset-oriented approach aligns naturally with data integration work, where clarity around inputs, outputs, and dependencies is critical.

Dagster supports a wide range of integration patterns, including ETL, ELT, batch processing, and event-driven workflows. Teams can orchestrate pipelines that span data warehouses, lakes, APIs, and streaming systems using Python-based definitions that are version-controlled and testable. Built-in features like type checks, data quality validations, and software-defined assets help catch issues early and enforce consistency across pipelines as they grow more complex.

Operationally, Dagster provides strong observability through detailed logs, lineage tracking, and rich metadata. This visibility allows data engineers and analytics teams to understand pipeline health, debug failures quickly, and trace downstream impacts of upstream changes. With flexible deployment options across cloud, hybrid, and on-prem environments, Dagster fits into modern data stacks while supporting security, governance, and scalability requirements.

By combining orchestration, observability, and software engineering best practices, Dagster helps organizations move from fragile, ad hoc integrations to robust, maintainable data integration platforms. This foundation enables teams to scale their data pipelines with confidence and deliver trusted data for analytics, operations, and machine learning.

---

## Data Intelligence Platform: Key Features & Top 5 Solutions in 2026

> A Data Intelligence Platform is an integrated system using Artificial Intelligence (AI) and Machine Learning (ML) to collect, process, and derive actionable insights from an organization's diverse data assets. These platforms consolidate data from various sources into a unified foundation.

Source: https://dagster.io/learn/data-intelligence-platform

### What Is a Data Intelligence Platform? 

A data intelligence platform is an integrated system, typically powered by artificial intelligence and machine learning algorithms to collect, process, and derive actionable insights from an organization's diverse data assets. These platforms consolidate data from various sources into a unified foundation, breaking down silos to improve data accessibility and foster a data-driven culture. 

Core functions and capabilities of data intelligence platforms include:

- **Data integration and management:** Unifies data from disparate sources (databases, data lakes, etc.) into a single platform for easier management and access. 
- **AI/ML capabilities:** Leverages AI and ML models to analyze data, identify patterns, and provide refined insights and recommendations. 
- **Data governance:** Enforces data quality, security, and compliance across the organization's data assets. 
- **Metadata and cataloging:** Builds a comprehensive data catalog to manage metadata, understand data lineage, and improve data discovery. 
- **Generative AI features:** Incorporates generative AI to simplify tasks like code generation and enable natural language-based querying and analysis. 
- **Data visualization:** Enables creation of dashboards, charts and other visual outputs that give stakeholders direct access to data.
- **Data democratization:** Makes data and AI insights accessible to a broader range of users, not just data experts.

This is part of a series of articles about [data platform](https://dagster.io/learn/data-platform)

### Core Functions and Capabilities of Data Intelligence Platform 

#### Data Integration and Management

Data intelligence platforms unify data from structured and unstructured sources, such as cloud applications, on-prem systems, and streaming platforms, into a centralized environment. This consolidation removes duplication and inconsistencies while ensuring data is readily accessible across the organization. Platforms often include prebuilt connectors, real-time ingestion, and change data capture (CDC) to keep datasets current and synchronized.

These platforms also manage the end-to-end data lifecycle, including data transformation, enrichment, and storage optimization. With built-in automation, they simplify pipeline configuration and reduce manual maintenance. 

#### AI and ML Capabilities

Modern data intelligence platforms embed artificial intelligence (AI) and machine learning (ML) to automate complex tasks and unlock deeper insights. These capabilities may include automated data classification, anomaly detection, predictive analytics, and natural language processing. 

Platforms use AI/ML to improve [data quality](https://dagster.io/learn/data-quality), identify patterns, and recommend actions, reducing the burden on data teams and accelerating the time from raw data to actionable intelligence. The integration of AI and ML into the [data pipeline](https://dagster.io/learn/data-pipeline) allows organizations to analyze vast datasets that would be impossible to evaluate manually. This supports use cases like forecasting, churn prediction, or personalized recommendations with minimal human intervention. 

#### Data Governance

Data governance in a data intelligence platform encompasses the policies, processes, and controls needed to manage data availability, usability, integrity, and security. This includes roles and responsibilities, data stewardship, data lineage, access management, and compliance with regulatory standards like GDPR or CCPA. 

Proper governance frameworks help organizations reduce risk, enforce data privacy, and ensure that data is fit for purpose across different business units. Automated governance features in data intelligence platforms provide rule-based access controls, policy enforcement, and audit trails. They offer data masking, encryption, and detailed activity logs to protect sensitive information and demonstrate regulatory compliance. 

#### Metadata and Cataloging

Metadata and cataloging are essential for discovering, organizing, and understanding an organization’s data assets. A data intelligence platform collects metadata (information about the data such as source, format, lineage, and usage statistics) and organizes it in a searchable catalog. 

This catalog enables users to quickly find relevant datasets, understand data context, and trace data origins, which is critical for analysis, compliance, and troubleshooting. Beyond basic search, advanced metadata management features include data profiling, relationship mapping, and impact analysis. This helps users see how data flows across systems, who uses it, and how changes might affect downstream processes. 

<strong><em>Learn more in our detailed guide to </em></strong>[<strong><em>data catalog</em></strong>](https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices)

#### Data Democratization

Data democratization refers to making data available and understandable to all stakeholders, not just IT or data experts. A core function of modern data intelligence platforms is eliminating technical barriers by providing self-serve analytics, intuitive interfaces, and natural language search capabilities. 

By simplifying access, these platforms empower employees across business units to explore data, run queries, and extract insights independently. Enabling data democratization also involves providing training resources, documentation, and built-in data literacy features. When paired with robust governance and cataloging, democratization ensures that even non-technical users can safely leverage data without compromising compliance or quality. 

### Key Benefits of Data Intelligence Platforms for Organizations 

A data intelligence platform creates a scalable foundation for managing data as a strategic asset. By consolidating fragmented data ecosystems and enabling smarter data usage, it helps organizations achieve efficiency, compliance, and innovation.

Key benefits include:

- **Improved decision-making:** Enables real-time, data-driven decisions across the organization by ensuring that data is accurate, accessible, and contextual.

* **Operational efficiency:** Reduces manual data handling and simplifies workflows through automation of integration, governance, and analytics processes.

- **Regulatory compliance:** Helps maintain compliance with data privacy and security regulations (like GDPR, CCPA) through built-in controls, auditing, and policy enforcement.

* **Data trust and transparency:** Enhances confidence in data by offering lineage tracking, version control, and detailed metadata, allowing users to understand where data comes from and how it’s used.

- **Faster time-to-insight:** Speeds up analytics cycles by providing self-service tools, centralized catalogs, and AI/ML-powered recommendations.

* **Cross-team collaboration:** Breaks down silos by making data assets discoverable and usable across departments, fostering collaboration between technical and business users.

- **Scalability and flexibility:** Adapts to growing data volumes and evolving data sources, supporting both structured and unstructured data across cloud and on-prem environments.

* **Enhanced data literacy:** Promotes a data-first culture by making data more approachable and useful to non-technical stakeholders through user-friendly interfaces and embedded guidance.

### Notable Data Intelligence Platforms 

#### 1. Qlik

![](https://dagster.io/webflow-assets/a47ea41d41f18347498c.avif)

Qlik provides analytics and AI capabilities centered on its associative engine, enabling flexible exploration across combined data. It includes generative, predictive, and agentic features, plus deployment options across cloud and on-premises. The platform connects to many enterprise systems and supports embedded and mobile analytics to deliver insights where decisions occur.

**Key features include:**

- **Associative analytics engine:** Indexes and freely associates data across sources, supporting fast exploration beyond traditional, strictly query-based filtering and joins.
- **Generative, predictive, and agentic AI:** Adds natural language answers, no-code predictions, and event-driven monitoring with actions integrated into analytics workflows.
- **Flexible deployment options:** Runs analytics in the cloud or on premises, allowing teams to decide where to store, transform, and analyze data.
- **Broad connectivity:** Provides connectors to major applications and platforms, combining data from systems like ERP, CRM, cloud warehouses, and open formats.

**Embedded and mobile analytics:** Delivers context-aware insight in applications and on devices, extending analytics access to operational environments and field users.

![](https://dagster.io/webflow-assets/290f7c4b77e4f33ef302.avif)

#### 2. Mode

![](https://dagster.io/webflow-assets/0fbd41fc7255e1592657.avif)

Mode is a business intelligence platform combining ad hoc analysis with self-serve reporting in one environment. It offers a SQL-first workflow with connected Python and R notebooks, interactive dashboards, and lightweight data apps. Governance and scale features include granular access controls, scheduling, and embedded analytics.

**Key features include:**

- **Integrated analyst workflow:** SQL editor supports rapid query iteration, then passes results directly to Python and R notebooks for advanced analysis.
- **Self-serve reporting and dashboards:** Publishes curated datasets and interactive dashboards, enabling non-technical users to explore results without modifying underlying queries.
- **Visual exploration at scale:** Lets users explore over one hundred thousand data points in-browser, sharing interactive explorations alongside narrative writeups.
- **Governance and scalability:** Offers identity provisioning, granular permissions, query-level search, collections, scheduled runs, and warehouse connections for growing teams.

**Programmability and embedding:** Provides programmatic APIs and embedded analytics capabilities to integrate reports and applications within existing products and workflows.

![](https://dagster.io/webflow-assets/2c0c9972b15cf3e00a12.avif)

#### 3. Tableau

![](https://dagster.io/webflow-assets/4682e46a1762216f73c1.avif)

Tableau is an analytics platform available as cloud or self-hosted deployments, with desktop authoring and an open architecture. It integrates trusted AI through Salesforce Einstein, supports governance and security certifications, and emphasizes drag-and-drop exploration with VizQL, Pulse metrics, and a broad ecosystem of connectors and extensions.

**Key features include:**

- **Multiple deployment options:** Offers Tableau Cloud and Tableau Server for hosted or self-managed analytics, plus Tableau Desktop for offline modeling and visualization.
- **AI-assisted analysis:** Integrates Einstein for insights, narratives, and natural language exploration, embedded within semantic-aware workflows and dashboard experiences.
- **Governance and security:** Supports SSO integration, centralized monitoring, and security certifications like SOC and ISO across enterprise cloud offerings.
- **Open, scalable platform:** Uses reusable models, accelerators, and extensions to scale mission-critical analytics without constant server configuration or hardware management.

**User-centric exploration:** Enables drag-and-drop analysis with VizQL and personalized metrics via Tableau Pulse to standardize definitions across analytics.

![](https://dagster.io/webflow-assets/060d65512232a0f7434e.avif)

#### 4. Looker

![](https://dagster.io/webflow-assets/860c8541e96921cc0fb6.avif)

Looker is a business intelligence and data platform from Google Cloud that delivers governed analytics, AI-powered insights, and custom data applications, built on a semantic modeling layer. It offers an API-first architecture that allows teams to explore and activate data across cloud environments. 

**Key features include:**

- **Semantic modeling layer:** Provides a centralized, governed framework for metrics and business logic, ensuring consistent definitions and results across all tools and users.

* **AI-powered analytics (Gemini in Looker):** Accelerates analysis with AI assistance for building visualizations, writing formulas, and modeling data guided by Google’s Gemini models and metadata-aware logic.

- **Self-service reporting and dashboards:** Helps users to explore, drill down, and create visual dashboards, using governed data to maintain trust and consistency across the organization.

**Looker Studio integration:** Offers drag-and-drop reporting connected to over 800 data sources, supporting both governed and ad hoc analytics for different user needs.

![](https://dagster.io/webflow-assets/c81bc123070e0997b080.avif)

#### 5. Power BI

![](https://dagster.io/webflow-assets/75401086cb7a9af22fd6.avif)

Power BI provides data visualization and reporting across Microsoft Fabric, Microsoft 365, and Azure ecosystems. It supports governed self-service and enterprise BI, embedded analytics, and Copilot-driven report creation. Options include desktop, cloud service, and on-premises report server deployment.

**Key features include:**

- **Governed single source of truth:** Connects disparate sources within one environment, combining enterprise scale and self-service to standardize metrics and definitions.
- **Copilot and templates:** Accelerates report creation with AI-generated content and hundreds of starter templates for common analytic scenarios and layouts.
- **Microsoft interoperability:** Integrates with Azure, Microsoft 365, Power Platform, Dynamics 365, and Purview for governance, discovery, and sensitivity labeling.
- **Flexible deployment modes:** Supports Power BI Desktop, cloud service, embedded experiences, and an on-premises Report Server for hybrid requirements.

**Scheduling and distribution:** Automates report refreshes and delivers updates through email or Slack alternatives, extending insights into daily productivity tools.

![](https://dagster.io/webflow-assets/40b48da4b2872ff02546.avif)

---

## Data Lineage in 2025: Types, Techniques, Use Cases & Examples

> Data lineage tracks and visualizes how data flows through an organization’s systems, from its source to its final destination

Source: https://dagster.io/learn/data-lineage

### What Is Data Lineage?

Data lineage is the process of tracking the flow of data as it moves through an organization’s systems, from its original source to its final destination. This includes capturing how data is transformed, joined, filtered, and aggregated at each stage, providing a full record of the data’s journey. By mapping data movement and transformations, data lineage tools create a traceable path that helps organizations understand, audit, and troubleshoot their data practices.

Data lineage not only documents the technical steps in data processes but also links them to business logic and rules. This allows users to see how specific inputs influence outputs and gain clarity on data dependencies. The result is a transparent environment where every stakeholder can trust the quality and history of the data, making it easier to identify issues, optimize workflows, and support decision-making.

This is part of a series of articles about [data catalog](https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices).

### Why Is Data Lineage Important? 

Data lineage plays a critical role in ensuring the reliability, integrity, and use of data across an organization. It goes beyond identifying data sources by capturing how, when, and by whom data is transformed or modified. This end-to-end visibility supports a wide range of operational and strategic functions:

- **Strengthens trust in data** by helping teams understand the context and quality of the information they use. Whether it's marketing, manufacturing, or management, all departments depend on accurate and meaningful data. By showing how data has changed over time and why, data lineage clarifies its relevance and improves its value in decision-making.
- **Addresses the challenge of data in flux**. As new data collection methods are introduced, lineage helps reconcile historical and current datasets. This makes it easier to adapt to evolving data landscapes and extract business value from diverse sources.
- **Supports system migrations**, by allowing IT teams to see where data resides, how it flows, and how it's used. This reduces the risk of errors, ensures smoother transitions, and helps preserve data integrity.
- **Supports data governance** by enabling audit trails, tracking compliance with internal policies, and meeting external regulatory requirements. Data lineage provides the transparency needed to manage risk and uphold data security across its lifecycle.

### How Data Lineage Works

Data lineage works by capturing metadata about data at rest and as it moves through processes, transformations, and storage layers. Specialized tools or platforms collect this metadata via connectors to databases, APIs, and monitoring solutions. The collected information is then catalogued, making it possible to monitor the movement and transformations between nodes such as source systems, ETL (extract, transform, load) jobs, data warehouses, and reporting tools.

In theory, it is possible to implement data lineage manually, but in modern data pipelines, automated solutions are needed due to the large volume of data, large variety of data sources, and frequent changes to the data and the pipeline itself. However, automation is not enough. Data lineage solutions must be integrated with existing systems to ensure continuous, real-time visibility into the evolving data flows within an organization.

### Types of Data Lineage 

#### Business Lineage

Business lineage represents the data journey from a business perspective, focusing on what the data means and how it supports business processes or decision-making. This view abstracts away technical details, instead emphasizing relationships between data elements and their alignment with business objectives, policies, and workflows. Business lineage diagrams and models allow stakeholders, such as business analysts and compliance officers, to trace data from origin to reports or dashboards with an emphasis on business context rather than code or infrastructure.

Stakeholders use business lineage to validate data definitions, ensure consistent interpretation, and support audit or regulatory inquiries. By mapping business events and rules to data movement, organizations improve communication between technical and business teams, leading to better alignment and understanding of how data drives value. Business lineage also helps bridge the documentation gap that often exists between IT and business users, fostering trust in the data ecosystem.

#### Technical Lineage

Technical lineage traces the physical movement and transformation of data as it passes through systems, databases, ETL jobs, and storage platforms. This approach gives engineers, database administrators, and data architects detailed knowledge of which processes move, change, or access specific data elements. Technical lineage is often visualized as directed graphs or flow diagrams, showing every transformation or join at each step in the data pipeline, along with dependencies between different systems.

Technical lineage helps technical teams conduct root-cause analysis when issues occur, optimize pipeline performance, and understand the downstream effects of schema or infrastructure changes. By identifying dependencies, organizations reduce the risks associated with code updates, migrations, or system decommissioning. Technical lineage is essential in complex, distributed environments where even a small change in one process can ripple across many dependent applications and datasets.

### Data Lineage Techniques

#### 1. Pattern-Based Lineage

Pattern-based lineage leverages recognizable data flow patterns, such as joins, filters, and aggregations, within scripts, stored procedures, or workflows to infer data movement and transformation. This technique analyzes code, configurations, and process logic to map how data is manipulated through standardized operations. Pattern-based lineage is effective in environments with consistent, repeatable data processing logic and can be applied across diverse technologies.

The primary advantage of pattern-based lineage is scalability; it enables quick mapping of complex data pipelines without the need for intrusive instrumentation. However, its accuracy depends on the clarity and regularity of the patterns. Highly customized or undocumented transformations can limit its effectiveness, requiring supplementary approaches for lineage coverage.

#### 2. Lineage by Data Tagging

Lineage by data tagging attaches metadata or unique identifiers to data as it moves through systems. These tags persist with the data, enabling tracking of its journey from source to destination. Tagging can be implemented at various levels, from field and record to table or file, depending on the desired granularity and overhead tolerance.

This technique provides precise, auditable trails for data, making it useful for meeting stringent compliance requirements or tracking sensitive information. However, it introduces some runtime overhead and requires careful coordination to ensure tagging consistency across heterogeneous systems. Despite implementation complexity, data tagging is a robust model where accuracy and audibility are top priorities.

#### 3. Self-Contained Lineage

Self-contained lineage embeds lineage information directly within datasets or files. For example, lineage data might be stored in header metadata, log files, or structured comments within files themselves. This approach keeps lineage closely coupled with the data, allowing movement and access tracing even if the data is transferred outside the main pipeline or organizational boundaries.

This method is beneficial for portable datasets, such as shared files or exported reports, making it possible to reconstruct data history without external tools. However, managing self-contained lineage in large-scale or frequently changing pipelines can be challenging, since embedded lineage data must be updated rigorously with each processing step to remain accurate and relevant.

#### 4. Lineage by Parsing

Lineage by parsing involves scanning data pipeline scripts, ETL workflows, SQL queries, or application code to extract lineage information. Dedicated parsers detect data sources, destinations, and the specific transformations applied, generating lineage maps by analyzing syntax and logic. This method is particularly effective for complex, code-driven data environments where automation is feasible.

Parsing enables detailed, automated capture of technical lineage. Its effectiveness depends on parser robustness and compatibility with diverse scripting languages and tools used in enterprise pipelines. While parsing can accelerate lineage documentation and reduce manual effort, evolving pipelines or unstructured transformations may limit precision, necessitating complementary methods for full coverage.

### Data Lineage Use Cases and Examples 

Here are a few common use cases of data lineage in modern data pipelines.

#### Data Modeling

Data lineage is integral to effective data modeling practices. By revealing how data attributes and entities evolve through various layers - source systems, staging areas, and data marts - lineage clarifies dependencies between models and their underlying sources. This transparency aids data architects in defining logical and physical models, ensuring the consistency and integrity of relationships among tables, views, and fields.

When organizations implement new or evolving data models, lineage enables validation of assumed data flows and transformations, minimizing risk of broken relationships or erroneous mappings. It also helps in model versioning, supporting rollback or audits to understand how and why particular changes affected analytic outputs. Data modeling depends on lineage for design, maintenance, and long-term sustainability.

<strong>Example: </strong>

A retail company building a new sales performance dashboard uses data lineage to validate its data model. Lineage shows that daily sales figures are sourced from both point-of-sale systems and online orders, passing through a staging area where discounts are applied before being loaded into the warehouse. When the team notices discrepancies in regional sales, lineage helps them trace the issue to a transformation step that applied outdated discount logic. By correcting the mapping, they restore data accuracy and ensure the dashboard reflects true sales performance.

#### Data Migration

During data migration projects, lineage enables teams to identify source data, track transformation logic, and document target mappings with accuracy. By making explicit the links between legacy systems and modern platforms, lineage ensures that critical business rules, [data quality](https://dagster.io/learn/data-quality) controls, and relevant metadata are preserved in the transition. This reduces the likelihood of data loss, corruption, or inconsistencies during migration.

Lineage also accelerates issue resolution by providing a clear audit trail during cutover, validation, and post-migration testing phases. When discrepancies or failures arise, teams can quickly pinpoint where data diverged from expectations, minimizing downtime or errors. Data lineage is essential to achieving successful, risk-mitigated migrations in environments with complex data integration requirements.

<strong>Example: </strong>

A healthcare provider migrating from an on-premises database to a cloud data warehouse relies on data lineage to document mappings between patient records, treatment histories, and billing data. Lineage reveals that certain transformation scripts were applying legacy coding standards to diagnosis fields, which would have led to inconsistent reporting in the cloud. By catching this during migration testing, the team adjusts the transformations and avoids regulatory compliance issues while ensuring continuity in reporting and analytics.

#### Impact Analysis

Impact analysis leverages data lineage to assess how changes to data sources, logic, or processes will affect downstream systems and applications. By mapping all dependencies, lineage clarifies which reports, dashboards, or business functions rely on specific data elements. This understanding supports informed decision-making when making schema changes, implementing new policies, or modernizing existing pipelines.

Using lineage for impact analysis minimizes the risk of unintended consequences, as teams can simulate, review, or test changes before wide deployment. It provides a safety net that enables continuous improvement or optimization initiatives while maintaining reliability, reducing costly rollbacks or customer-facing errors stemming from poorly understood dependencies.

<strong>Example: </strong>

A financial services firm plans to deprecate a legacy customer database and replace it with a new CRM platform. Using data lineage, the data engineering team identifies all reports, dashboards, and machine learning models that depend on the old database. The analysis shows that a credit risk scoring model uses customer attributes stored in the legacy system. This discovery prevents the team from inadvertently breaking the model and allows them to redesign the pipeline to pull equivalent data from the new CRM before decommissioning the old system.

#### Data Compliance

Data lineage supports compliance by establishing auditable, end-to-end traceability for sensitive or regulated data. This makes it feasible to prove how personal information is sourced, processed, and retained in accordance with laws such as GDPR, HIPAA, or CCPA. Lineage documentation can streamline responses to audits, regulatory requests, or breach investigations.

Additionally, lineage allows organizations to define and enforce data retention, masking, and minimization policies effectively. By tracking every instance of handling and transformation, compliance teams can identify gaps and demonstrate due diligence. As regulatory pressures increase, data lineage is becoming a required component of compliant data management strategies.

<strong>Example: </strong>

An insurance company preparing for a GDPR audit uses data lineage to demonstrate how personal customer data flows through its systems. Lineage shows that customer addresses are ingested from application forms, standardized in ETL jobs, and stored in a policy management system before being included in analytics reports. When auditors request evidence of data minimization, the company uses lineage to prove that sensitive identifiers are masked before reaching the analytics environment. This clear, auditable trace enables the organization to pass the audit and avoid fines.

### Best Practices for Implementing Data Lineage 

Here are some best practices that can help your organization implement data lineage more effectively.

#### 1. Automate Lineage Capture

Automation is key to effective data lineage capture in modern, dynamic data environments. Automated lineage tools connect directly to data sources, ETL frameworks, and analytics platforms, continually extracting metadata and mapping data flows without manual intervention. This continuous approach ensures that lineage information remains current, accurate, and comprehensive as systems evolve over time.

Relying on manual documentation not only increases the likelihood of oversight but also creates bottlenecks when changes are frequent. Automated solutions improve resilience to process changes and reduce the operational burden on technical staff, allowing organizations to scale lineage tracking as data volumes and pipelines grow. Choosing automation from the outset expedites compliance, troubleshooting, and root-cause analysis.

#### 2. Define Appropriate Granularity

Determining the right level of granularity for lineage tracking is crucial. Too granular, and lineage becomes overwhelming, cluttered with unnecessary details; too coarse, and stakeholders lack the specificity needed to troubleshoot or audit data flows effectively. Organizations should tailor lineage to specific roles - for example, summary-level for business users, detailed-level for data engineers.

Regular reviews ensure that lineage granularity aligns with evolving requirements, use cases, and team expertise. Organizations can selectively expose or hide layers of information to suit operational or compliance needs, optimizing usability while ensuring thorough traceability where it matters most.

#### 3. Integrate into Governance

Integrating data lineage with broader data governance initiatives strengthens both disciplines. Aligning lineage with governance frameworks, data catalogs, data stewardship, and access controls provides a unified foundation for stewarding business-critical data. Integrated platforms enhance visibility across the data estate, facilitating issue resolution, compliance, and stakeholder confidence.

Governance-integrated lineage links policy enforcement with practical tracking, enabling organizations to manage data quality, ownership, and regulatory compliance more effectively. It supports organizational maturity in data management and fosters a culture of accountability, ensuring clarity in how data is captured, processed, and utilized enterprise-wide.

#### 4. Monitor and Update Regularly

Data environments are dynamic, with new sources, transformations, and reporting needs emerging continuously. Regular monitoring and proactive updates to lineage models ensure ongoing accuracy, preventing information drift or outdated documentation from undermining trust. Change management processes should include triggers to review and update lineage whenever pipelines, schemas, or source systems change.

Automated monitoring tools can alert teams to discrepancies, failures, or undocumented changes in real time. By treating lineage as a living asset that requires dedicated stewardship, organizations maintain high-quality, actionable insight that supports effective governance and business operations.

#### 5. Orchestrate Data Pipelines

Effective data lineage requires comprehensive visibility into the orchestration layer—the system that manages the execution, dependencies, and scheduling of data workflows. Orchestrating [data pipelines](https://dagster.io/learn/data-pipeline) using tools like Apache Airflow, Dagster, or Prefect ensures that lineage information includes not just what transformations occur, but when, how often, and under what conditions. These orchestration tools expose job-level metadata such as task order, conditional branches, and failure retries, which enhance the completeness of lineage records.

Embedding lineage collection into orchestration processes allows teams to correlate data changes with specific job executions and operational events. For example, if a dashboard value is incorrect, lineage tied to pipeline orchestration can reveal whether an upstream job failed or produced unexpected results.

### Supporting Data Lineage with Dagster Data Orchestration

Dagster provides a unified orchestration framework that enriches data lineage by capturing the full context of how data is produced, transformed, and consumed across modern pipelines. Because Dagster models data assets as first-class objects with explicit relationships, it generates accurate lineage automatically as part of normal workflow execution. This approach eliminates the need for custom tracking logic and delivers a clear, traceable record of every step in the data lifecycle.

Dagster records metadata about each asset, including upstream and downstream dependencies, materialization events, code versions, and run-specific information. These details allow users to understand where data originated, which computations created it, and which downstream processes rely on it. By storing lineage in a central metadata store, Dagster makes it easy to query, visualize, and audit data flows across teams and environments.

The Dagster UI provides interactive lineage graphs that show connections between assets in a readable and intuitive form. Engineers can drill into each node to inspect execution logs, input and output metadata, and historical run details. This visibility supports rapid debugging, impact analysis, and compliance reporting. Business users benefit from clarity about data provenance, while technical users gain operational insight into the health of pipelines and workflows.

Dagster also integrates lineage with software-defined assets, allowing teams to version, test, and refactor pipelines with confidence. When code changes introduce new logic or dependencies, Dagster updates the lineage model automatically, ensuring that documentation stays accurate as the system evolves. This alignment between orchestration and lineage strengthens governance practices and supports reliable, trustworthy data operations.

---

## Data Management Strategy Foundations & 7 Steps to Building Yours

> A data management strategy is a blueprint for an organization's data lifecycle, guiding how data is collected, stored, secured, and used to achieve business goals. It includes defining policies, roles, and technologies to ensure data is accurate, accessible, and compliant.

Source: https://dagster.io/learn/data-management-strategy

### What Is a Data Management Strategy? 

A data management strategy is a blueprint for an organization's data lifecycle, guiding how data is collected, stored, secured, and used to achieve business goals. It includes defining policies, roles, and technologies to ensure data is accurate, accessible, and compliant, acting as a roadmap for turning data into a valuable asset.

Components of a data management strategy include:

- **Data governance:** Establishes rules, roles, and accountability for data, including data ownership, security, and compliance requirements. 
- [**Data quality**](https://dagster.io/learn/data-quality)**:** Implements processes to ensure data is accurate, consistent, and reliable. 
- **Data architecture:** Defines the systems and infrastructure for storing and managing data, such as databases, cloud platforms, and data warehouses. 
- **Data security:** Includes policies and technologies to protect data from unauthorized access, breaches, and loss. 
- **Data lifecycle management:** Covers all stages of the data journey, from creation and processing to archival and deletion.

Key steps to building your strategy:

1. **Start with business goals:** Link the data strategy to specific business objectives rather than focusing on technology first. 
2. **Define data-related roles:** Clearly assign responsibility for data management tasks to specific people and teams. 
3. **Establish policies and procedures:** Create clear rules for data handling, quality, and access. 
4. **Select the right technology:** Choose tools and systems that support the strategy, whether for data storage, analysis, or protection. 
5. **Create a roadmap:** Document a high-level plan that outlines how the organization will manage its data to support the business strategy.

**Deploy and operationalize data workflows:** Implement the data management plan by deploying workloads with automated orchestration tools.

### Core Objectives of a Modern Data Management Strategy 

#### Aligning Data with Business Outcomes

Aligning data with business outcomes involves ensuring that data initiatives directly support key organizational goals, such as revenue growth, efficiency improvements, or enhanced customer experiences. By aligning data collection, organization, and usage with desired business impacts, companies can prioritize investments and projects that deliver the highest value. 

This approach also clarifies what data needs to be captured, how it should be maintained, and which metrics will signal success. To achieve this alignment, companies must develop strong communication channels between business and data teams, promoting shared understanding of priorities.

#### Reducing Risk and Ensuring Compliance

Modern data management strategies prioritize risk reduction and compliance, which are crucial in today’s regulatory environment. Data breaches, unauthorized access, and non-compliance with regulations like GDPR or CCPA can result in heavy fines and reputational damage. By embedding governance and security measures into the strategy, organizations establish clear controls over who accesses data, how it is handled, and how audit trails are maintained.

Effective compliance goes beyond meeting baseline regulations; it also fosters customer trust and supports business continuity. Policies and tools that monitor data handling, automate retention schedules, and enforce privacy standards are vital.

#### Enabling Data-Driven Decision Making

A core objective of data management is enabling confident, data-driven decision making across all business levels. Accurate, timely, and relevant data forms the foundation for modern analytics, forecasting, and performance measurement. The strategy must ensure that data is accessible to decision-makers in a form they can trust and understand, which often involves simplifying data flows and integrating multiple sources.

Key to this objective is the removal of data silos and the promotion of data literacy within the organization. By empowering employees with tools, training, and clear processes, companies create a culture that favors evidence-based insights over gut instincts.

### The Building Blocks of a Data Management Strategy 

#### Data Governance

Data governance provides the framework for overall data management, setting the rules, roles, and responsibilities needed to ensure data consistency, accountability, and stewardship. Effective governance establishes policies for data ownership, quality standards, access rights, and regulatory compliance. The framework defines how decisions about data are made, who approves changes, and how conflicts are resolved.

Implementing data governance often involves forming a council or committee to oversee the process, with representation from IT, business, legal, and compliance teams. This collective effort enforces adherence to agreed-upon standards and ensures alignment with business priorities. 

#### Data Quality

Data quality management centers on ensuring that data is accurate, consistent, complete, and timely. High-quality data is essential for trustworthy analytics, regulatory compliance, and operational efficiency. Poor data quality leads to errors, inefficiencies, and lost business opportunities, impacting both day-to-day operations and long-term strategy.

Improving data quality typically involves implementing validation rules, automated cleansing routines, and monitoring processes that detect anomalies or inconsistencies. Organizations should establish clear metrics and KPIs to track data quality and invest in technologies that support proactive management. Ongoing training and user feedback loops help maintain high standards and quickly address emerging quality issues.

#### Data Architecture

Data architecture defines how data is organized, integrated, stored, and accessed across the enterprise. It includes specifying logical models, data flows, storage mechanisms, and integration points between systems. A robust data architecture ensures scalability, enables interoperability, and supports future technology adoption.

Well-designed data architecture simplifies integration between heterogeneous sources such as databases, cloud platforms, and external data feeds. By outlining clear guidelines for data modeling, APIs, and storage solutions, architecture reduces complexity and improves reliability. Regular reviews and updates ensure the architecture evolves alongside organizational needs.

#### Data Security

Data security protects data from unauthorized access, breaches, and misuse throughout its lifecycle. Comprehensive data security strategies encompass both physical and digital safeguards such as encryption, access controls, intrusion detection, and secure backup. Security protocols must cover all stages, from data creation to storage, transit, and deletion.

Building a security-first culture within the organization is critical. Employees and contractors need awareness training on recognizing threats like phishing and social engineering. Security controls must be regularly tested and updated to counteract evolving cyber threats. Proactive monitoring, incident response plans, and regulatory audit readiness are essential.

#### Data Lifecycle Management

Data lifecycle management (DLM) involves coordinating how data is created, classified, stored, retained, archived, and deleted. A clear DLM policy defines rules for each stage in the data lifecycle, ensuring data is maintained only as long as necessary and removed when it no longer holds value. This helps minimize storage costs and reduces the risk of regulatory violations.

Automated DLM solutions can enforce retention schedules, securely archive historical data, and permanently delete records according to policy. Effective lifecycle management ensures compliance with industry regulations while supporting agile data retrieval and usage. Reviewing and updating DLM practices is crucial as technologies and regulatory requirements evolve.

### Key Technologies Supporting Data Management Strategies 

#### Data Warehousing and Data Lakes

Data warehouses and data lakes are foundational technologies for storing, organizing, and retrieving vast volumes of structured and unstructured data. Data warehouses are optimized for structured data analytics, supporting reporting and business intelligence use cases with strict schema enforcement. Data lakes store raw, unstructured, and semi-structured data, making them suitable for big data analytics, machine learning, and flexible exploration.

Choosing the right mix of warehouse and lake technologies enables organizations to support a variety of analytics workloads while optimizing performance and costs. Today’s cloud-based solutions offer elasticity and integration with other data management services. 

#### Master Data Management (MDM) and Metadata Management

Master data management (MDM) involves creating a single, authoritative source for critical business data entities, such as customers, products, or suppliers. MDM systems ensure consistency and accuracy by synchronizing data across systems and resolving duplicates or conflicts. This reliability is fundamental for trustworthy reporting, regulatory compliance, and operational efficiency.

Metadata management focuses on organizing and governing information about data assets, such as definitions, lineage, and usage context. Robust metadata management allows organizations to track where data originates, how it has been used or transformed, and who is responsible for it. 

#### Data Integration, ETL/ELT and Replication

Data integration tools enable the consolidation of data from disparate sources into a unified format suitable for analysis and reporting. This often involves ETL (extract, transform, load) or ELT (extract, load, transform) pipelines that move data from operational systems to data warehouses or lakes. ETL transforms data before loading, while ELT performs transformations after data is loaded, offering flexibility and scalability, especially in cloud environments.

Replication tools ensure that data changes in source systems are propagated in near real-time to target systems, maintaining consistency across platforms. This is critical for operational reporting, disaster recovery, and high-availability applications. Together, integration, ETL/ELT, and replication technologies form the backbone of data flow orchestration, enabling reliable access to fresh and consistent data across the enterprise.

#### Data Virtualization Platforms

Data virtualization platforms allow organizations to access, integrate, and query data in real-time, regardless of where it resides without moving or replicating it. This abstraction layer unifies data from multiple sources (e.g., cloud, on-premises, third-party) and presents it as a single virtual view to analytics and reporting tools. This approach simplifies access while reducing data sprawl and duplication.

With data virtualization, organizations can accelerate deployment of new analytics projects and reduce the burden of complex data migrations. Governance and security policies can be centrally applied at the virtualization layer, simplifying compliance. The result is more agile, cost-effective access to trusted and up-to-date information across the enterprise.

#### AI-Driven Data Catalogs and Observability Tools

AI-driven data catalogs automate the discovery, classification, and indexing of organizational data assets. These platforms help users search for and understand available data sets, while machine learning models enrich metadata with relationships, quality scores, and usage patterns. This delivers faster, more accurate self-service data access for business users and analysts.

Observability tools extend these capabilities by providing real-time monitoring and analysis of [data pipelines](https://dagster.io/learn/data-pipeline), quality, and lineage. Through dashboards and proactive alerts, these solutions identify inconsistencies, bottlenecks, or anomalies. 

<strong><em>Learn more in our detailed guide to </em></strong>[<strong><em>data observability</em></strong>](https://dagster.io/guides/data-observability-in-2025-pillars-pros-cons-best-practices)<strong><em> </em></strong>

### Implementing a Data Management Strategy, Step-by-Step

Organizations should consider the following steps to ensure an effective data management strategy.

#### 1. Start with Business Goals

The first step in implementing a data management strategy is to define business and data objectives. These should align with wider corporate strategies and outline how data management will support digital transformation, regulatory compliance, customer service improvements, or other top priorities. Each goal should have success metrics and clear ownership.

Engaging executives, business leaders, and key data stakeholders ensures objectives are relevant and achievable. Prioritizing goals and connecting them to measurable benefits guides resource allocation and sets expectations for the broader initiative. This focused approach builds executive buy-in and clarifies the value of investing in robust data management.

#### 2, Define Data-Related Roles

Organizations should assign ownership for key functions such as data governance, quality assurance, architecture, and security. Common roles include data stewards (responsible for data accuracy and consistency), data custodians (overseeing technical implementation and infrastructure), and data owners (typically business stakeholders accountable for the value and compliance of specific data sets).

Establishing a data management hierarchy also helps avoid overlap and gaps in responsibility. A well-defined structure enables collaboration between business and IT teams, ensures policy enforcement, and supports faster decision-making. Role definitions should be documented, aligned with job descriptions, and reinforced through training and ongoing evaluation.

#### 3. Establish Policies and Procedures

Establishing governance and data policies is essential for ensuring consistent, secure, and compliant data usage across the organization. This includes formalizing data ownership, access controls, retention schedules, quality metrics, and compliance procedures. Written policies reduce ambiguity and serve as references for expected behaviors and escalations.

Enforcement of governance involves implementing automated controls, audits, and reporting systems. Regular policy reviews, triggered by regulatory updates or technology changes, keep governance effective and relevant. Ongoing communication and training help users understand responsibilities and the rationale behind each policy, reducing unintentional violations.

#### 4. Select the Right Technology 

Selecting an appropriate technology stack involves evaluating existing systems and scouting solutions that align with both current and future requirements. The technology stack should address needs for data storage, integration, quality, security, and analytics, while supporting interoperability and scalability. Key criteria include ease of deployment, maintenance, vendor support, and alignment with budget constraints.

Proof-of-concept projects and pilot deployments help organizations test new technologies in real-world conditions. Soliciting feedback from IT, data, and business teams ensures the chosen stack is practical and suited to intended use cases. Thorough selection processes minimize technical debt and reduce the risk of costly and disruptive platform changes down the line.

#### 5. Create a Roadmap

A data management roadmap translates strategic goals into actionable steps over a defined timeline. It prioritizes initiatives based on business impact, resource availability, and technical readiness. The roadmap should outline phases for deploying governance frameworks, data quality programs, architecture upgrades, and technology implementations, including milestones and performance indicators for each phase.

In addition to scheduling technical projects, the roadmap should incorporate change management, communication plans, and stakeholder engagement strategies. This ensures organizational alignment and prepares teams for process or tool changes. Regular reviews of the roadmap allow for course corrections in response to new regulations, business priorities, or technology trends.

#### 6. Migrate Existing Systems

Migrating legacy systems is a critical step in modernizing data management and aligning infrastructure with strategic objectives. This involves assessing existing platforms, identifying redundant or obsolete components, and planning transitions to modern cloud or hybrid environments. A successful migration strategy prioritizes high-value data sets, ensures minimal business disruption, and adheres to regulatory requirements for data integrity and privacy.

Migration may include data cleansing, reformatting, and validation to ensure quality in the new environment. It's essential to map dependencies between systems and engage both IT and business stakeholders in the process. Testing environments should be used to verify data integrity and performance before full deployment. Post-migration reviews and audits ensure that systems are functioning correctly and that all data was successfully transferred and secured.

#### 7. Deploy and Operationalize Data Workflows

Operationalizing data management involves deploying approved workflows for data acquisition, validation, integration, storage, analysis, and archiving. Automated orchestration tools are used wherever possible to reduce manual work and assure consistency. Logging, monitoring, and exception handling provide transparency and accountability throughout workflow execution.

Deployment planning should include contingency processes for failures, security incidents, or rapid recovery scenarios. Close coordination with IT, data teams, and business users ensures workflows align with operational requirements and compliance mandates. Continuous improvement loops help optimize performance, stability, and cost efficiency over time.

### Empowering Your Data Management Strategy with Dagster

A data management strategy only creates impact when it can be reliably executed. Dagster helps organizations operationalize their data management strategy by orchestrating, monitoring, and maintaining the data workflows that support analytics, reporting, and downstream business use cases.

Dagster operates at the execution layer of the modern data stack, coordinating data integration, transformation, and validation across data warehouses, data lakes, and operational systems. By treating data as first-class assets with defined dependencies, Dagster makes the data lifecycle easier to understand, manage, and evolve as business needs change.

This asset-centric approach strengthens data governance and data quality by embedding checks, expectations, and ownership directly into data workflows. Built-in lineage and metadata provide visibility into how data moves through the organization, supporting compliance requirements and increasing trust in data insights.

With automation and observability at its core, Dagster enables teams to detect issues early, resolve failures faster, and reduce operational risk. The result is a more resilient data platform that aligns data operations with business goals and turns data management strategy into a repeatable, measurable practice rather than a static plan.

---

## Data Management Tools: 5 Categories & 18 Tools to Know in 2026

> Data management tools help organizations collect, store, organize, and protect data, and can be categorized into types like database management systems (DBMS), data integration and ETL tools, data warehousing solutions, and master data management (MDM) and data governance tools.

Source: https://dagster.io/learn/data-management-tools

### What Are Data Management Tools? 

Data management tools are software systems designed to help organizations collect, store, organize, process, and govern data throughout its lifecycle. These tools address various aspects of data operations, from integration and transformation to quality control, cataloging, and security. They enable teams to ensure data consistency, reliability, and accessibility while supporting analytics, reporting, compliance, and other business needs. 

Here are the main types of data management tools and examples of popular solutions, reviewed further below:

- **Data integration platforms:** Dagster, Fivetran, Matillion, Airbyte
- **Master data management (MDM) tools:** Informatica Multidomain MDM, IBM InfoSphere Master Data Management, Semarchy xDM
- **Data warehousing and data lakes:** Snowflake, Google BigQuery, Databricks Lakehouse, Azure Synapse Analytics, Amazon Redshift
- **Data catalog and discovery tools:** Alation Data Catalog, Atlan, AWS Glue Data Catalog
- **Data governance and policy platforms:** Collibra Data Intelligence Cloud, OneTrust DataGovern, Precisely Data360 Govern

This is part of a series of articles about [data quality](https://dagster.io/learn/data-quality)

### Core Functions of Data Management Tools 

#### Data Collection and Integration

Data collection and integration tools aggregate data from various sources, such as internal systems, cloud applications, and external datasets. They enable organizations to unify data formats and protocols, resolving discrepancies and enabling data flow. By automating these processes, businesses reduce manual intervention and ensure data arrives in the required structure for downstream analytics and processing.

The effectiveness of integration depends on the ability to connect diverse endpoints, apply transformation logic, and handle errors efficiently. Modern platforms offer connectors, APIs, and automation to fetch data from databases, SaaS tools, IoT devices, and more. This enables real-time data feeds and up-to-date insights necessary for agile decision-making.

#### Data Storage and Warehousing

Data storage and warehousing solutions provide scalable repositories for data, supporting both operational and analytical needs. These tools allow companies to store large volumes of historical and transactional data securely, structured in a way that supports efficient querying and reporting. Typical implementations include relational databases, NoSQL stores, and modern cloud-based data warehouses.

Efficient data storage ensures that data is readily accessible and optimizes resource usage and cost. Data warehousing platforms include features like partitioning, indexing, and parallel processing, which significantly improve performance for analytical workloads. The move to the cloud has enabled organizations to leverage on-demand scaling, high availability, and managed infrastructure, reducing the burden on IT teams.

#### Data Processing and Transformation

Data processing and transformation tools are essential for cleaning, structuring, and enriching raw datasets into usable formats for analytics or operations. These tools handle tasks such as filtering, aggregating, joining, and normalizing data from multiple sources. By automating transformation pipelines, organizations can standardize incoming data, address inconsistencies, and produce consistent outputs for business intelligence or machine learning applications.

Transformation also includes enrichments like geocoding, sentiment scoring, and formatting exchanges that support different business functions. Processing engines work at significant scale, often integrating with tools for orchestration and parallelization, allowing for the rapid completion of complex data workflows. This speeds up the delivery of meaningful, actionable information to stakeholders across the enterprise.

#### Data Governance and Quality Control

Data governance and quality control are focused on establishing policies, roles, responsibilities, and processes to ensure data meets organizational standards. Tools in this domain help implement frameworks for data stewardship, lineage tracking, and compliance measures. Governance solutions assign ownership, enforce business rules, and document decisions to promote accountability and transparency across teams.

Quality control features include profiling, validation, deduplication, and error correction functions that protect the integrity of company data assets. Automation of these checks allows businesses to quickly surface anomalies, address root causes, and maintain trust in their reporting and analytics. 

#### Metadata and Catalog Management

Metadata and catalog management tools track and organize information about an organization’s data assets, including source, lineage, structure, and usage details. A centralized metadata catalog provides a searchable inventory that boosts data discoverability and reuse. These solutions enable collaboration among analysts and engineers by making it easier to find, assess, and understand datasets.

Automated metadata harvesting and lineage tracking help organizations comply with data privacy regulations and audit requirements. Catalog platforms enable impact analysis, help reduce data duplication, and empower data stewards to manage and curate datasets efficiently. With clear metadata documentation, enterprises improve data literacy and maximize return on their data investments.

#### Data Security and Compliance

Data security and compliance tools protect sensitive information, control access, and ensure regulatory adherence. These tools manage authentication, encryption, masking, and activity monitoring in line with laws such as GDPR, HIPAA, and CCPA. They enable organizations to define access policies, track usage, and respond to incidents quickly, reducing the risk of data breaches and non-compliance penalties.

Platforms often offer detailed audit logs, automated alerts, and reporting functions to support compliance audits and internal reviews. Security features extend to both data in motion and at rest, ensuring that protections are consistently applied throughout the lifecycle. Security and compliance tooling are essential elements of any modern data management strategy.

### Key Categories of Data Management Tools 

#### Data Integration and ETL Platforms

Data integration and ETL (Extract, Transform, Load) platforms enable the collection, blending, and movement of data across various sources and destinations. These systems automate the process of extracting data from disparate repositories, transforming it for consistency and quality, and loading it into data warehouses or lakes. 

Modern ETL platforms often incorporate real-time streaming and batch processing, along with connectors for databases, cloud services, and APIs. With built-in error handling, logging, and scheduling, they reduce manual maintenance and improve [data reliability](https://dagster.io/guides/data-reliability-challenges-measurement-best-practices). These platforms are essential for organizations pursuing scalable, automated, and solid data architectures.

#### Master Data Management (MDM)

Master data management (MDM) tools centralize and harmonize critical business information, such as customer, product, or supplier data, across systems. These solutions create a single, authoritative version of the truth (the "golden record"), which improves accuracy and consistency across business processes. By eliminating redundancies and resolving conflicts, MDM systems support reliable decision-making and consistent reporting.

An MDM platform typically supports data stewardship, governance, and workflow automation to manage updates, syndication, and data quality rules. Linking records across applications prevents fragmentation, while advanced matching and merging algorithms help maintain accurate master records. 

#### Data Warehousing and Data Lakes

Data warehousing solutions store structured data in a format optimized for query performance and business intelligence. Data lakes, on the other hand, ingest raw, semi-structured, or unstructured data at scale, retaining its native format for flexibility. Both environments are often connected, enabling organizations to separate storage from compute and access data for different use cases.

Cloud-native data platforms make it easier to deploy, scale, and secure warehouses or lakes without heavy infrastructure investments. They support features such as automatic scaling, multi-tenancy, and integration with analytics engines. Using both data warehouses and lakes enables organizations to address a wide spectrum of data needs, from real-time analytics to archival storage.

#### Data Catalog and Discovery Tools

Data catalog and discovery tools help organizations inventory, describe, and locate their data assets. These tools provide search, tagging, and lineage tracking to make datasets more transparent and accessible. By indexing data across warehouses, lakes, and business applications, catalog tools simplify self-service data discovery for analysts, data scientists, and business users.

Advanced catalogs offer collaborative features such as metadata enrichment, usage analytics, and rating systems. They improve data governance by documenting data ownership, usage patterns, and compliance status. Effective cataloging reduces data duplication, enforces standards, and ensures that teams spend less time searching for data and more time using it.

#### Data Governance and Policy Platforms

Data governance and policy platforms centralize the administration of data policies, standards, and compliance activities. These tools support the definition, enforcement, and auditing of data-related rules and responsibilities across organizational units. Key features include data stewardship workflows, policy distribution, and regulatory tracking.

Automated rule engines, workflow management, and reporting modules help ensure that governance policies are consistently applied. Policy platforms often integrate with other data management solutions for holistic oversight, supporting efforts to comply with privacy laws and internal data standards. 

### Notable Data Management Tools  

#### Data Integration Platforms

##### 1. Dagster

![Dagster logo](https://dagster.io/webflow-assets/29e9468525d8dc4fad51.png)

Dagster is an open-source data orchestration platform designed to help teams build, test, and operate reliable data pipelines. Rather than focusing solely on data movement, Dagster emphasizes pipeline correctness, observability, and maintainability across the entire data lifecycle. It is commonly used to orchestrate ETL/ELT jobs, machine learning workflows, and analytics pipelines that span multiple tools and systems.

**Key features of Dagster:**

- **Software-defined assets**: Treats datasets as first-class objects, making dependencies, freshness, and lineage explicit
- **Strong typing and validation**: Built-in checks ensure data quality and correctness at each pipeline step
- **Powerful orchestration engine**: Coordinates batch, event-driven, and scheduled workflows across diverse systems
- **Observability and debugging**: Rich UI for monitoring runs, inspecting metadata, and diagnosing failures
- **Flexible deployment options**: Supports local development, containerized environments, and cloud-native deployments via Dagster Cloud

**Extensive integrations**: Works seamlessly with tools like dbt, Snowflake, BigQuery, Spark, Kubernetes, and more

![Key features of Dagster](https://dagster.io/webflow-assets/7d58825356440553d391.webp)

##### 2. Fivetran

![Fivetran logo](https://dagster.io/webflow-assets/76a5fb9b9551e103e80e.webp)

Fivetran is a managed data integration platform that automates the movement of data from a wide range of sources to cloud destinations. It supports over 700 connectors, allowing organizations to ingest data from SaaS applications, databases, ERPs, and files without building or maintaining pipelines manually. 

**Key features include:**

- **Connector library:** Supports over 700 pre-built connectors for applications, databases, file systems, and more
- **Automated schema handling:** Detects and manages schema changes with minimal disruption to pipelines
- **Flexible deployment options:** Enables deployment in cloud, on-premise, or hybrid environments
- **Built-in transformations:** Prepares data for analytics using configurable, in-warehouse transformation models

**Security and compliance:** Adheres to standards like SOC 2, HIPAA, GDPR, and PCI DSS, with enterprise-grade controls

![Key features of Fivetran](https://dagster.io/webflow-assets/487ab25b406852b2dc23.webp)

##### 3. Matillion

![Matillion logo](https://dagster.io/webflow-assets/62740eb99343a027f7d0.webp)

Matillion is a cloud-native ETL/ELT platform that simplifies the process of extracting, transforming, and loading data into modern cloud data warehouses. It offers a visual development environment with low-code tools, making it accessible for data teams to build, schedule, and monitor data pipelines. Matillion integrates tightly with cloud services and supports real-time and batch data workflows.

**Key features include:**

- Pre-built connectors for cloud apps, databases, and file systems
- Visual pipeline builder with low-code interface
- Cloud-native architecture optimized for Snowflake, BigQuery, Redshift, and others
- ELT support with SQL pushdown for efficient processing
- Job scheduling, logging, and orchestration capabilities

![Key features of Matillion](https://dagster.io/webflow-assets/5fa2f4e7b608d1f5f009.webp)

##### 4. Airbyte

![Airbyte logo](https://dagster.io/webflow-assets/9251a462fa4e916c0108.webp)

Airbyte is an open-source data integration platform focused on ELT processes. It enables users to move data from hundreds of sources to data warehouses or lakes with minimal setup. Airbyte emphasizes connector extensibility, allowing users to build custom connectors. With flexible deployment options, it supports open-source and enterprise-grade use cases.

**Key features include:**

- Over 600 pre-built connectors for sources and destinations
- Open-source core with active community support
- Cloud, on-premise, and hybrid deployment models
- Support for real-time and batch data ingestion
- Custom connector development with minimal code

![Key features of Airbyte](https://dagster.io/webflow-assets/01dd27b160205e89bdd5.webp)

#### Master Data Management (MDM)

##### 5. Informatica Multidomain MDM

![Informatica Multidomain MDM logo](https://dagster.io/webflow-assets/cd3807eb142dbce1423f.webp)

Informatica Multidomain MDM provides a unified platform to manage master data across domains such as customers, products, and suppliers. It helps organizations maintain accurate, consistent, and complete master records through matching, merging, and governance workflows. Its cloud-native design and AI automation improve productivity and ensure data quality at scale.

**Key features include:**

- Multi-domain master data support
- Matching, merging, and conflict resolution to create golden records
- Cloud-native architecture with microservices support
- Integrated data quality, validation, and enrichment tools
- AI-driven automation for stewardship and onboarding

![Key features of Informatica Multidomain MDM](https://dagster.io/webflow-assets/4b84bca99a73279f9ee4.webp)

##### 6. IBM InfoSphere Master Data Management

![IBM logo](https://dagster.io/webflow-assets/cb41c59e0e11ceef26ff.webp)

IBM InfoSphere MDM is an enterprise-grade solution for managing master data across critical domains. It provides real-time access to trusted data and supports operational and analytical needs through matching, stewardship, and governance features. The platform is suitable for hybrid deployments and integrates with existing enterprise systems.

**Key features include:**

- Centralized master data for customer, product, account, and more
- Advanced matching and consolidation algorithms
- Stewardship dashboards, alerts, and audit logs
- Support for cloud, on-premise, and hybrid environments
- Integration with enterprise applications and services

![Key features of IBM](https://dagster.io/webflow-assets/19e64bf00e25a29f502b.webp)

##### 7. Semarchy xDM

![Semarchy xDM logo](https://dagster.io/webflow-assets/235d1d884ddae4a30450.webp)

Semarchy xDM is a unified platform for master data, reference data, and application data management. It offers an agile, model-driven approach that allows rapid development and deployment of data hubs. With built-in data quality, governance, and accessible interfaces, Semarchy xDM supports IT and business users in managing data assets.

**Key features include:**

- Multi-domain MDM, RDM, and ADM in a single platform
- Built-in data quality tools: matching, deduplication, validation
- Visual application builder for fast deployment
- Role-based security and granular access control
- Agile data modeling and quick configuration

![Key features of Semarchy xDM](https://dagster.io/webflow-assets/c4d948a5a8159080b5c4.webp)

#### Data Warehousing and Data Lakes

##### 8. Snowflake

![Snowflake logo](https://dagster.io/webflow-assets/66651e4e7d91b3e6c5b0.webp)

Snowflake is a fully managed cloud data warehousing platform built for high performance, scalability, and simplicity. It separates compute and storage so users can scale resources independently, and supports structured and semi-structured data for analytics across large volumes.

**Key features include:**

- Independent scaling of compute and storage for flexibility
- Support for structured, semi-structured (e.g., JSON, Parquet) and variant data types
- Zero-management infrastructure (no servers to provision)
- Multi-cluster concurrency to avoid resource contention
- Shared data marketplace and data-sharing capabilities across accounts

![Key features of Snowflake](https://dagster.io/webflow-assets/81e28a977855100abc62.webp)

##### 9. Google BigQuery

![Google BigQuery logo](https://dagster.io/webflow-assets/da41c0144e16c9a6237e.webp)

Google BigQuery is a serverless, fully managed enterprise data warehouse that runs at petabyte scale, enabling organizations to analyze massive datasets without having to manage infrastructure. The platform integrates with machine-learning and BI tools, supports streaming and batch ingestion, and offers flexible pricing models.

**Key features include:**

- Serverless architecture with no infrastructure to manage
- Scalable to petabyte-level datasets and high query concurrency
- Support for both batch and streaming ingestion
- Native integration with Google Cloud AI/ML services and geospatial analytics
- Flexible pricing (on-demand or flat-rate)

![Key features of Google BigQuery](https://dagster.io/webflow-assets/835c07a6346f75462811.webp)

##### 10. Databricks Lakehouse Platform

![Databricks Lakehouse Platform logo](https://dagster.io/webflow-assets/525cd817dc35a904733e.webp)

The Databricks Lakehouse Platform combines the best of data lakes and data warehouses into a unified architecture, enabling storage, processing, analytics and machine-learning workloads on one platform. It provides an open, scalable environment that removes silos and supports modern data-driven workflows.

**Key features include:**

- Unified lakehouse architecture supporting raw data, analytics and AI workflows
- Open standards and broad ecosystem compatibility
- Single platform for ETL/ELT, streaming, BI, data science and ML
- Central governance with unified metadata, access controls and cataloging
- Scalability and flexibility to handle both structured and unstructured data

![Key features of Databricks Lakehouse Platform](https://dagster.io/webflow-assets/0a3b63ececea9dd0e390.webp)

##### 11. Azure Synapse Analytics

![Azure Synapse Analytics logo](https://dagster.io/webflow-assets/3dd853b7d860310ede6d.webp)

Azure Synapse Analytics is an analytics service that brings together big data and data warehousing in a unified environment. It enables organizations to analyze data from operational stores, data lakes, and enterprise warehouses using a common workspace. 

**Key features include:**

- **Unified analytics workspace:** Combines data integration, warehousing, and big data analytics in one environment
- **Support for multiple data sources:** Access and analyze data across data lakes, operational stores, and warehouse systems
- **Integrated security and compliance:** Built-in protections aligned with over 100 global compliance standards
- **Flexible pricing model:** Consumption-based pricing supports cost optimization and scalability

**Migration readiness:** Tools and documentation for migrating to Microsoft Fabric from dedicated Synapse pools

![Key features of Azure Synapse Analytics](https://dagster.io/webflow-assets/c038fe1e96206dbf37e6.avif)

##### 12. Amazon Redshift

![Amazon Redshift logo](https://dagster.io/webflow-assets/dea56b5f52e0e7aef590.webp)

Amazon Redshift is a cloud-based data warehouse to deliver SQL-based analytics across data lakes and structured data stores. With support for serverless deployment and zero-ETL integrations, it enables near real-time insights without managing infrastructure. 

**Key features include:**

- **Serverless and provisioned deployment:** Choose between managed infrastructure or serverless execution for flexible scaling
- **Zero-ETL integrations:** Ingests data from sources like streaming platforms and operational databases in near real-time
- **Lakehouse compatibility:** Unified analytics across Redshift warehouses, Amazon S3, and federated sources
- **SQL-based machine learning:** Native integration with Amazon SageMaker and support for AI/ML workflows

**Optimized price-performance:** High throughput and cost efficiency for enterprise-scale analytics

![Key features of Amazon Redshift](https://dagster.io/webflow-assets/51c9e0621a78668dc240.webp)

#### Data Catalog and Discovery Tools

##### 13. Alation Data Catalog

![Alation logo](https://dagster.io/webflow-assets/8802bf8624567de41f8f.webp)

Alation Data Catalog is a metadata management and data-discovery tool that helps organizations find, understand, and govern their data assets. It provides searchable metadata, collaborative documentation, lineage tracking, and connectors to many data sources.

**Key features include:**

- Centralized searchable catalog of data assets across warehouses, lakes, BI tools
- Metadata harvesting, tagging and enrichment to improve discoverability
- Usage analytics, lineage and overview of who’s using what data and how
- Collaboration features (annotations, comments) for data stewards and analysts
- Integration with a range of data sources via open connector framework

![Key features of Alation](https://dagster.io/webflow-assets/1ef195ab271f8cf16e2b.webp)

##### 14. Atlan

![Atlan logo](https://dagster.io/webflow-assets/201afad5ec80de476478.webp)

Atlan is a metadata management and data collaboration platform that unifies discovery, governance, and automation across modern data stacks. It provides a metadata layer that integrates with various tools and systems, enabling users to explore column-level lineage, define data products, and apply governance policies in context.

**Key features include:**

- **Active metadata platform:** Captures and activates metadata across the data stack for discovery and governance
- **Column-level lineage and automation:** Tracks data flow and automates workflows for consistency and control
- **No-code connectors:** Integrates with tools like Snowflake, Databricks, dbt, Tableau, and more
- **Collaboration and data products:** Enables teams to define and manage data products and governance policies together

**AI readiness and context-aware interfaces:** Supports AI agents and semantic layers to bring business context into analytics

![Key features of Atlan](https://dagster.io/webflow-assets/d53b875ed135c3ff265d.webp)

##### 15. AWS Glue Data Catalog

![AWS Glue Data Catalog logo](https://dagster.io/webflow-assets/668462b8dd37b5560ccf.webp)

AWS Glue Data Catalog is a managed metadata repository service within the AWS ecosystem. It stores table definitions, schemas, partitions and other control information, enabling users to discover and query data assets, manage schema evolution, and support ETL workflows.

**Key features include:**

- Automatic schema discovery via crawlers for varied data sources
- Central metadata repository for varied AWS services (S3, Redshift, Athena, EMR)
- Schema versioning, tracking, and evolution over time
- Integration with ETL jobs managing data preparation and transformation
- Metadata accessible for lineage, governance and performance optimization

![Key features of AWS](https://dagster.io/webflow-assets/f9091f90ffd3629ba5f6.webp)

#### Data Governance and Policy Platforms

##### 16. Collibra Data Intelligence Cloud

![Collibra Data Intelligence Cloud logo](https://dagster.io/webflow-assets/eb33b11ab9c880f90e00.webp)

Collibra Data Intelligence Cloud is a unified platform for data governance, quality, cataloging, and lineage. It helps organizations build trust in their data by providing consistent governance frameworks, policy enforcement, and data stewardship workflows. The platform enables business and technical users to collaborate on data definitions, compliance, and data lifecycle management across distributed environments.

**Key features include:**

- Centralized data catalog, governance, and lineage in one platform
- Pre-built workflows for stewardship, policy approvals, and issue resolution
- Business glossary and metadata management for shared understanding
- Role-based access and data usage tracking for compliance
- Cloud-native architecture with scalable microservices design

![Key features of Collibra Data Intelligence Cloud](https://dagster.io/webflow-assets/cc7eeb14bb52ae2292ef.avif)

##### 17. OneTrust DataGovern

![OneTrust logo](https://dagster.io/webflow-assets/ef450792e51e1e0a1f27.webp)

OneTrust DataGovern focuses on data governance and privacy management by enabling organizations to classify, control, and monitor their data usage. It automates the enforcement of policies and integrates with consent and privacy frameworks, helping ensure regulatory compliance. With visibility into data flows and access rights, it supports ethical and secure data practices.

**Key features include:**

- Automated data classification aligned to business and compliance frameworks
- Policy management and enforcement for data usage and sharing
- Integration with consent and privacy tools for regulatory alignment
- Data lifecycle tracking from ingestion to deletion
- Dashboards for data discovery, access control, and usage analytics

##### 18. Precisely Data360 Govern

![Precisely logo](https://dagster.io/webflow-assets/02895bc057fe82e31e7a.webp)

Precisely Data360 Govern is a governance solution for managing metadata, data lineage, and business context across enterprise data assets. It supports collaboration between data stewards and business users through visual workflows, impact analysis, and issue tracking. The platform improves data trust by linking technical metadata to business meaning.

**Key features include:**

- Metadata harvesting and automated cataloging
- Business glossary and definitions tied to data assets
- Lineage visualizations and impact analysis for audit readiness
- Governance workflows for issue resolution and stewardship
- Data quality scoring and annotation tools

![Key features of Precisely](https://dagster.io/webflow-assets/74a779c40be8a6966626.webp)

### Conclusion 

Effective data management tools are essential for ensuring data integrity, usability, and security across the enterprise. As organizations face increasing data volume, variety, and velocity, these tools provide the infrastructure to collect, process, store, govern, and protect information at scale. By investing in the right mix of data management capabilities, businesses can improve operational efficiency, support informed decision-making, meet compliance requirements, and unlock the full value of their data assets.

---

## Data Observability Tools: Key Features & Top 8 Solutions in 2026

> Data observability tools are software that help organizations monitor and ensure the health, reliability, and quality of their data and data pipelines. Popular tools include Dagster, Monte Carlo, Datafold, Bigeye, Sifflet, Soda, and Anomalo.

Source: https://dagster.io/learn/data-observability-tools

### What Are Data Observability Tools? 

Data observability tools are software that help organizations monitor and ensure the health, reliability, and quality of their data and data pipelines. Popular tools include Dagster, Monte Carlo, Datafold, Bigeye, Sifflet, Soda, and Anomalo, which offer automated monitoring, anomaly detection, and root cause analysis to help prevent and resolve data issues.

Key features of data observability tools include:

- **Automated monitoring:** They use agents and integrations to automatically monitor data across its lifecycle, covering aspects like freshness, volume, and schema changes. 
- **Anomaly detection:** Tools use machine learning to identify unexpected deviations from normal data patterns, helping to detect issues proactively. 
- **Root cause analysis:** They provide tools to trace the origin of data problems, helping teams understand and resolve incidents faster. 
- **Data lineage:** They map the flow of data from source to destination, providing context and helping to assess the impact of issues. 
- **Alerting and notifications:** Configurable alerts notify teams of problems via tools like Slack or email, enabling timely responses.

This is part of a series of articles about [data quality](https://dagster.io/learn/data-quality).

### Key Use Cases of Modern Data Observability Tools 

#### Automated Monitoring

Continuous data monitoring is the foundation of observability tools, automatically tracking metrics such as completeness, freshness, accuracy, and schema changes. Monitoring modules surface relevant key performance indicators (KPIs) on customizable dashboards, giving data engineers and analysts immediate visibility into system status and data quality health. 

These dashboards update in real time, ensuring issues are quickly noticed and acted upon. Beyond passive monitoring, alerting systems deliver proactive notifications when thresholds or validation rules fail, sending alerts via email, Slack, pager services, or other communication platforms. 

#### Anomaly Detection

Anomaly detection uses statistical, machine learning, or rule-based models to automatically identify irregularities in data streams and datasets. These could be sudden spikes, drops, missing data, or inconsistencies that deviate from historical patterns or expected thresholds. Continuous anomaly detection is key for organizations to spot quality issues or operational failures early, allowing data teams to intervene before these anomalies impact analytical outputs or business decisions.

Modern tools typically support both automated and customizable anomaly detection, letting users define their own conditions or enable algorithms that learn what "normal" looks like for specific datasets. Automated alerting is then triggered when an abnormal trend occurs, either through dashboards or direct notifications. 

#### Root Cause Analysis

Automated root cause analysis quickly diagnoses why a detected data issue has occurred, pinpointing its origin within the complex web of modern data ecosystems. These features sift through pipeline logs, metadata, and event triggers to map out the sequence of events that contributed to the anomaly or failure. They often generate suggested remediation steps, which can significantly reduce the mean time to resolution (MTTR) for data incidents.

Another benefit of automated analysis is that it lessens the dependency on individual expertise within the team. Rather than relying on manual investigation or tribal knowledge, teams receive direct insights to guide them toward corrective actions. This level of automation helps foster a culture of accountability, transparency, and consistent quality in managing complex and evolving data infrastructure.

#### Data Lineage 

Data lineage visualization provides a graphical representation of data’s path across the organization, from its origin through every transformation and downstream consumer. This capability is essential for understanding dependencies between datasets, tracing the source of errors or unexpected values, and complying with data governance requirements. 

Lineage tools expose both high-level flow diagrams and detailed step-throughs of granular transformations, giving data teams clarity into how, where, and why data changes. Lineage diagrams aid impact analysis, helping teams evaluate the consequences of upstream changes on downstream processes before making modifications. 

#### Alerting and Notifications 

Alerting and notifications are critical for ensuring data issues are identified and acted upon in real time. Observability tools allow teams to configure alerts based on predefined thresholds, validation rules, or anomaly detection triggers. These alerts can be fine-tuned to specific datasets, pipeline stages, or business logic, ensuring only relevant and actionable incidents are surfaced. 

Notifications are typically integrated with collaboration and incident management platforms like Slack, Microsoft Teams, PagerDuty, or Opsgenie. This integration ensures alerts are routed to the appropriate personnel or teams without delay, enabling faster triage and resolution. Some platforms also support escalation policies and alert grouping to avoid redundant messaging and to simplify incident workflows.

### Notable Data Observability Tools

#### 1. Dagster

![](https://dagster.io/webflow-assets/4a0a94efc9af18991ae8.webp)

Dagster is a data orchestration and observability platform that helps teams build, operate, and monitor reliable data pipelines by embedding observability directly into data asset development and execution. Instead of treating observability as a separate monitoring layer, Dagster provides asset-centric visibility into data freshness, lineage, failures, and quality as pipelines run, enabling teams to identify issues earlier, understand downstream impact, and resolve incidents within the same system used to define and operate data workflows.

**Key features include:**

- **Asset-based observability**: Tracks the health, freshness, and execution status of data assets aligned to business concepts rather than just tables or jobs
- **Native data lineage**: Automatically maps upstream and downstream dependencies to support impact analysis and root cause investigation
- **Integrated data quality checks**: Runs declarative quality checks and validations as part of pipeline execution to prevent bad data from propagating
- **Event-driven alerting**: Sends alerts for failures, freshness violations, and custom conditions via Slack, email, or incident management tools

**Unified orchestration and observability**: Combines scheduling, retries, backfills, and monitoring in a single platform to reduce operational complexity

![](https://dagster.io/webflow-assets/0358cb2fd392c45e0111.webp)

#### 2. Monte Carlo

![](https://dagster.io/webflow-assets/c734702f0e479f9363f3.webp)

Monte Carlo is a [data observability](https://dagster.io/guides/data-observability-in-2025-pillars-pros-cons-best-practices) platform that monitors the health and reliability of data pipelines, systems, and AI workloads. It unifies observability across traditional data infrastructure and AI models, helping teams address the growing trust gap between data quality and AI reliability. 

<strong>Key features include:</strong>‍

- **End-to-end observability**: Tracks data from ingestion through transformation to consumption and AI output, helping detect both pipeline failures and AI drift or bias
- **Integrated monitoring for data and AI**: Observes pipeline reliability, data completeness, and timeliness alongside model behavior and output quality
- **Automated issue detection**: Identifies anomalies across datasets and models without relying on manual checks or siloed monitoring tools
- **Root cause analysis across domains**: Diagnoses whether issues stem from data pipelines, system infrastructure, or model behavior

**Improved collaboration**: Aligns data, AI, and business teams by providing a shared view of health and performance across the stack

![](https://dagster.io/webflow-assets/8f7a2a1b5580f61e6163.webp)

#### 3. Acceldata

![](https://dagster.io/webflow-assets/c373fffe9fc0d3be9552.webp)

Acceldata is a data observability platform that introduces *agentic data management* (ADM), an AI-driven approach that unifies monitoring, governance, and optimization across the modern data stack. Unlike traditional solutions that treat data quality, lineage, and profiling as isolated functions, Acceldata uses agents to manage these dimensions in a coordinated and autonomous manner.

<strong>Key features include:</strong>‍

- **AI-powered data agents**: Autonomous agents handle core observability tasks (quality, lineage, and profiling) using contextual intelligence to optimize decisions and prevent failures
- **Data quality agent**: Monitors and resolves data quality issues, ensuring reliable data for analytics and AI use cases
- **Data lineage agent**: Tracks data movement across systems, enabling root cause analysis, dependency mapping, and informed change management
- **Data profiling agent**: Surfaces anomalies, schema issues, and statistical insights from datasets to strengthen governance and improve data usability

**Unified agentic architecture**: Integrates multiple observability domains under a single AI-driven framework for holistic, automated data management

![](https://dagster.io/webflow-assets/7bc9fb96b0641130ab4b.webp)

#### 4. Soda

![](https://dagster.io/webflow-assets/2cc54accd0a339984cd1.webp)

Soda is a data observability platform intended to move teams from reactive issue detection to proactive data quality prevention. It combines monitoring with early-stage testing and collaborative workflows to ensure data issues are caught before they impact production. It shifts observability left into CI/CD pipelines and enables contract-based collaboration between data producers and consumers.

<strong>Key features include:</strong>‍

- **Metrics observability**: Detects anomalies in production data using an engine that is faster and more accurate than traditional Prophet-based models
- **Pipeline testing**: Integrates directly into CI/CD workflows, allowing teams to validate data early and prevent low-quality data from reaching production
- **Collaborative data contracts**: Converts identified anomalies into data contracts, aligning expectations between producers and consumers without requiring code changes
- **Operational data quality management**: Automates issue routing to the appropriate data owners and simplifies resolution workflows to minimize downtime

**Explainable alerts**: Sends context-rich notifications with low false positives, helping teams understand, prioritize, and act on data issues quickly

![](https://dagster.io/webflow-assets/b6a73a00bd1e10caa00e.webp)

#### 5. Sifflet

![](https://dagster.io/webflow-assets/9c8d8be9b7e4719ec1a2.webp)

Sifflet is an AI-augmented data observability platform for technical teams and business users. It combines automated monitoring, lineage tracking, and data cataloging into a unified solution that promotes transparency, accountability, and collaboration across the organization. It enablies all stakeholders to understand, monitor, and trust their data.

<strong>Key features include:</strong>‍

- **AI-augmented monitoring**: Delivers out-of-the-box and customizable monitoring, powered by AI to reduce noise, prioritize critical issues, and optimize coverage over time
- **Data lineage and tracing**: Maps upstream and downstream dependencies to help teams quickly identify the origin of data issues and assess their impact
- **Data catalog**: Simplifies data discovery with an automated, searchable catalog that supports collaboration and improves understanding of available assets
- **Business-ready interface**: Built with both engineers and business users in mind, making it easy for non-technical teams to participate in monitoring and data ownership

**Self-serve and collaboration**: Allows users to view data health, contribute to monitoring rules, and take responsibility for their data, supporting data mesh initiatives

![](https://dagster.io/webflow-assets/39e1b8eac687b1592ba9.webp)

#### 6. Bigeye

![](https://dagster.io/webflow-assets/660ff0fa18cfab755e62.webp)

Bigeye is an enterprise-grade data observability platform to support modern and legacy data stacks at scale. By combining integration coverage with lineage-enabled workflows, Bigeye helps data teams detect, diagnose, and resolve data issues before they affect business outcomes. It includes automated monitoring, root cause analysis, and data health summaries. 

<strong>Key features include:</strong>‍

- **Lineage-enabled observability**: Leverages column-level data lineage to trace dependencies, pinpoint root causes, and assess downstream impact with speed and precision
- **Comprehensive stack coverage**: Supports both legacy and modern infrastructure with over 50 connectors, including cloud, on-premises, and hybrid deployments
- **Automated root cause & impact analysis**: Simplifies investigation workflows, enabling teams to prioritize and resolve high-impact issues faster
- **Data health summaries for business users**: Delivers intuitive dashboards and BI-integrated summaries so stakeholders can monitor and trust the data they rely on

**Proactive monitoring**: Automatically maps critical business fields and deploys targeted upstream monitoring to track only what matters

![](https://dagster.io/webflow-assets/f310d3523986d6c8e34a.webp)

#### 7. Anomalo

![](https://dagster.io/webflow-assets/5e81a6d453ed3ed9ddc4.webp)

Anomalo is a scalable data observability platform built to automatically monitor data quality, reliability, and freshness across data warehouses without requiring deep configuration. It uses AI-based checks, rules-based validations, and metrics monitoring to detect anomalies in table-level data and catch pipeline issues before they impact business outcomes. 

<strong>Key features include:</strong>‍

- **Automated table monitoring**: Continuously tracks availability, freshness, and row counts across every table to catch issues like late updates or missing data
- **AI-powered anomaly detection**: Uses machine learning to identify unexpected changes in data values, catching subtle issues traditional monitoring might miss
- **Custom rules and metrics monitoring**: Supports SQL-based validations and metric tracking for critical tables to match business-specific quality expectations
- **Data pipeline monitoring**: Detects pipeline failures such as dropped tables, delayed jobs, or incomplete data loads before they affect downstream models or dashboards

**Governance and compliance support**: Enables warehouse-wide oversight, integrates with data catalogs, and provides dashboards for quality initiatives and audit reporting

![](https://dagster.io/webflow-assets/b070c29f4911643267f9.webp)

#### 8. Datafold

![](https://dagster.io/webflow-assets/ceff378cad679ba32640.webp)

Datafold is a data observability solution focused on proactive, upstream data monitoring to catch issues before they impact downstream processes. By moving observability “left” in the data lifecycle, Datafold helps teams detect data quality problems during development, deployment, or integration phases, minimizing risk and reducing incident response time.

<strong>Key features include:</strong>‍

- **Data diffs**: Compares data between source and destination systems to ensure value-level consistency, reducing the risk of silent data divergence in pipelines
- **Metrics monitoring**: Uses machine learning to detect anomalies in key indicators like row counts, freshness, and uniqueness, helping teams catch issues early
- **Custom data tests**: Allows users to define validation rules for business-critical logic, such as identifying nulls, duplicates, or invalid values at the record level
- **Schema change alerts**: Notifies teams instantly of unexpected changes in table structures or column types, preventing breakages in downstream jobs or dashboards

**CI/CD integration**: Embeds into development workflows with support for real-time alerting via Slack, PagerDuty, and other tools to catch and resolve issues during code changes

![](https://dagster.io/webflow-assets/fba43f57501897bb555f.webp)

### Considerations for Choosing the Right Data Observability Tool 

Selecting the right data observability platform requires careful evaluation of your organization’s data environment, team structure, and operational goals. While many tools offer similar core features, key differences in scope, flexibility, and ecosystem compatibility can significantly affect value and effectiveness. Below are critical considerations to guide your decision:

- **Depth of integration with existing stack**: Ensure the tool supports your current and planned data sources, warehouses, transformation layers, and BI platforms. Tools that integrate natively with your tech stack minimize setup time and maximize observability coverage.<br><br>
- **Support for real-time vs. batch workloads**: Some tools excel in streaming and real-time monitoring, while others are optimized for batch pipelines. Choose based on the nature and criticality of your workflows, especially if latency or near-real-time alerting is a priority.<br><br>
- **AI and automation capabilities**: Evaluate the tool’s ability to automate anomaly detection, root cause analysis, and remediation. Platforms with ML or AI agents reduce manual oversight and scale better in complex environments.<br><br>
- **Ease of use for technical and non-technical users**: Consider whether the interface, dashboards, and alerting systems are accessible to both engineers and business stakeholders. Broad usability supports data ownership across the organization.<br><br>
- **Customization and flexibility**: Look for tools that allow custom rules, tailored alerts, and flexible metric tracking. This ensures alignment with unique data quality policies and operational SLAs.<br><br>
- **Scalability and performance**: Assess how well the platform performs as data volumes and pipeline complexity grow. High-performance tools should maintain observability without significantly increasing infrastructure overhead or lag.<br><br>
- **Security, compliance, and governance alignment**: The tool should align with your organization's compliance needs, offering audit logs, role-based access control, and integration with data catalogs or policy engines where needed.<br><br>
- **Pricing model and cost predictability**: Review how the tool charges: by number of tables, data volume, usage hours, or users. Unpredictable pricing can lead to hidden costs at scale, especially in fast-growing data environments.<br>

By mapping these considerations to your technical needs and organizational goals, you can choose a data observability solution that delivers measurable value without unnecessary complexity.

---

## Data Pipeline Orchestration Tools: Top 6 Solutions in 2026

> Data pipeline orchestration tools are software solutions designed to automate, manage, and monitor complex data workflows or pipelines. These tools ensure the reliable and efficient movement and transformation of data from various sources to their intended destinations.

Source: https://dagster.io/learn/data-pipeline-orchestration-tools

### What Are Data Pipeline Orchestration Tools? 

Data pipeline orchestration tools are software solutions designed to automate, manage, and monitor complex data workflows or pipelines. These tools ensure the reliable and efficient movement and transformation of data from various sources to their intended destinations, often involving tasks like ingestion, ETL/ELT, quality checks, and reporting.

Orchestration tools provide a central interface for scheduling, triggering, monitoring, and troubleshooting data workflows. This coordination ensures that dependencies are respected, tasks execute in the right sequence, and operational requirements, like retries or notifications, are handled automatically. Such orchestration is essential for ensuring [data quality,](https://dagster.io/learn/data-quality) reliability, and consistency in large-scale or distributed data systems.

This is part of a series of articles about [data platform](https://dagster.io/learn/data-platform)

### Key Functions and Capabilities of Data Pipeline Orchestration Tools 

#### Workflow / Pipeline Definition

Defining workflows is fundamental to orchestration. Tools typically allow users to express workflows as directed acyclic graphs (DAGs), code, or configuration files, outlining task dependencies and execution order. This approach abstracts away the complexity of handling these dependencies explicitly in application code. 

Many orchestration tools support programmatic, declarative, or UI-driven approaches, tailoring to different user preferences and technical skills. A well-defined workflow ensures modularity and consistency across [data pipelines](https://dagster.io/learn/data-pipeline). This fosters reusability and better maintainability, as code changes and improvements can be reflected across workflows. 

#### Scheduling and Triggering

Scheduling and triggering are core aspects of orchestration platforms. Schedulers enable pipelines to run at specified intervals or times, such as every midnight or at regular minute intervals. This automates repetitive data processes, ensuring timely data delivery without manual intervention. 

Triggers can be based on external events, such as file arrivals, API callbacks, or table updates, providing flexibility for event-driven pipelines. Advanced tools support a mix of time-based and event-based scheduling, allowing for more responsive and optimized workflows. Conditional triggers and dependency management enable execution based on the completion or status of upstream tasks. 

#### Execution Engine

The execution engine is responsible for running pipeline tasks reliably and efficiently. It handles task queuing, resource allocation, and parallelization. Modern engines can distribute workload across multiple nodes, scale according to demand, and recover from temporary failures automatically, reducing the operational burden on data engineers. 

An effective execution engine also integrates with existing compute environments such as on-premises clusters, Kubernetes, or cloud services. This integration ensures that orchestrated pipelines run close to where data is generated or stored, optimizing performance and cost. It may also support configuration of concurrency, retries, and custom execution logic.

#### Monitoring, Logging, and Observability

Monitoring and logging features provide real-time visibility into pipeline execution, making it easier to detect and resolve issues. Orchestration tools offer dashboards that show pipeline status, individual task progress, and historical runs. Detailed logs can help troubleshoot failed tasks, enabling engineers to identify root causes and resolve issues quickly. 

Observability extends beyond simple logs and metrics by including alerting for failures, latency, or performance degradation. Integration with external monitoring tools allows organizations to centralize their incident response and maintain compliance with operational standards. 

#### Error Handling

Robust error handling ensures that transient issues or infrastructure outages do not cause data loss or workflow failure. Orchestration tools allow specification of retry logic, where failed tasks can be re-run automatically with configurable delay and maximum attempt policies. Notification mechanisms can alert operators or stakeholders when failures occur that require manual intervention. 

Advanced error handling includes support for conditional branching, so downstream tasks can react differently to upstream failure or success. This flexibility allows teams to implement compensating actions, skip tasks, or trigger alternative workflows in response to errors. 

#### Data Lineage

Data lineage tracks how data flows through pipelines and how each transformation affects it. Orchestration tools increasingly offer lineage features, capturing metadata about task execution, input-output relationships, and dependencies between tables or datasets. 

This visibility is crucial for debugging, auditability, and regulatory compliance. Lineage capabilities also support impact analysis. When data definitions or code change, teams can assess which downstream assets may be affected. By understanding source-to-destination pathways, organizations can trust the provenance and quality of analytics outputs.

#### Security, Access Control and Governance

Security features enforce access controls over who can view, edit, or execute workflows. Orchestration tools support user authentication, role-based access control (RBAC), and integration with enterprise identity providers. This minimizes risk of unauthorized changes or accidental data exposure. 

Governance functions provide audit trails tracking who modified, triggered, or approved pipeline runs. Combined with lineage and logging, these controls support regulatory requirements and internal security best practices. Automated policy enforcement helps prevent privilege escalation or compliance drift over time.

### Notable Data Pipeline Orchestration Tools 

#### 1. Dagster

![](https://dagster.io/webflow-assets/bd38e0ce6114124fee73.avif)

Dagster is an open-source data orchestrator built for modern data engineering teams that need reliable, maintainable, and testable pipelines. It emphasizes software engineering best practices and introduces software-defined assets that allow teams to represent data pipelines around the datasets they produce instead of traditional task-centric DAGs. This approach improves clarity, observability, and long-term maintainability across complex data ecosystems.

**Key features include:**

- **Asset-centric design:** Pipelines are modeled around data assets with explicit dependencies, enabling first-class lineage, reproducibility, and a clear understanding of how data moves through the platform.
- **Built-in quality enforcement:** Strong typing, schema checks, and runtime validation create predictable pipelines and reduce data quality issues before they reach consumers.
- **Developer-friendly workflow:** Local-first development, Python-native APIs, and testable components give engineers a fast, iterative environment for building and refining pipelines.
- **Comprehensive observability:** A modern UI provides detailed views of lineage, run status, partitions, metadata, and freshness, helping teams diagnose issues quickly and maintain trustworthy data services.
- **Scalable orchestration options:** Supports schedules, sensors, event triggers, partitions, and large-scale backfills across multiple execution environments such as Kubernetes, serverless, and local compute.

**Ecosystem-wide integrations:** Works seamlessly with major data engineering tools including dbt, Snowflake, BigQuery, Spark, Kafka, DuckDB, and other systems used across ingestion, transformation, analytics, and machine learning workflows.

![](https://dagster.io/webflow-assets/42b63941965f3662a688.avif)

#### 2. Apache Airflow

![](https://dagster.io/webflow-assets/b65240b55b2d614d91aa.avif)

Apache Airflow is an open-source platform for authoring, scheduling, and monitoring workflows using Python code. Originally developed at Airbnb to manage complex internal workflows, it allows users to define workflows as directed acyclic graphs (DAGs), where each node represents a task and the edges define dependencies. 

**Key features include:**

- **Python-based DAGs:** Workflows are defined in Python, allowing use of standard programming constructs and libraries.
- **Flexible scheduling:** Supports both time-based scheduling (e.g., daily, hourly) and event-based triggers.
- **UI for monitoring:** Built-in web interface for visualizing DAGs, tracking task execution, and managing workflow runs.
- **Extensible and modular:** Users can create custom operators, sensors, and hooks to integrate with various data systems.

**Open source ecosystem:** Backed by a large community and extended by third-party providers with tools for deployment and monitoring.

![](https://dagster.io/webflow-assets/77dea9b3f9b590fc1f83.avif)

#### 3. Prefect

![](https://dagster.io/webflow-assets/806208c3d7b002491c26.avif)

Prefect is a data orchestration tool to support dynamic, Python-native workflows without forcing developers into rigid DAG structures. It adapts to the way teams already write code, turning Python scripts into resilient, observable workflows with minimal changes. Prefect is designed to handle complex, changing workflows across ETL, ML pipelines, and operational automation.

**Key features include:**

- **Dynamic workflow engine:** Prefect's orchestration adapts to real-time data conditions and execution paths, avoiding stalls caused by static DAGs.
- **Python-native interface:** Workflows are written in plain Python with minimal annotations, eliminating the need to rewrite existing code or adopt a new DSL.
- **Built-in observability:** Provides clear error messages, audit logs, and real-time visibility into flow runs to simplify debugging and monitoring.
- **Scalable deployment:** Flows can be deployed independently in isolated environments with customizable infrastructure, supporting hybrid and multi-cloud strategies.

**Flexible execution model:** Resources are attached to individual jobs rather than environments, supporting diverse workloads and improving efficiency.

![](https://dagster.io/webflow-assets/b2e58acae77c519b1dd4.avif)

#### 4. Flyte

![](https://dagster.io/webflow-assets/f8f975776d68a999b7af.avif)

Flyte is a Kubernetes-native orchestration platform built for creating and managing reliable, scalable, and crash-proof workflows, especially in AI/ML, data, and analytics use cases. It  allows developers to define workflows as standard Python code and run them at scale with minimal overhead. 

**Key features include:**

- **Kubernetes-native scaling:** Tasks are dynamically scaled across compute nodes, leveraging Kubernetes for elastic, cost-efficient execution with no idle resources.
- **Crash-proof reliability:** Failure recovery with automatic retries, checkpointing, and state tracking keeps workflows running even when systems fail.
- **Python-native workflow definition:** Author workflows using a clean Python SDK without needing a new DSL or framework; supports dynamic branching and decision-making.
- **Run locally, deploy remotely:** Developers can debug and test locally, then deploy the same code to remote clusters for production without modification.

**Data lineage:** Automatically tracks inputs, outputs, and dependencies across tasks and workflows for better traceability and auditability.

![](https://dagster.io/webflow-assets/407d797a1191633b2080.avif)

#### 5. Mage

![](https://dagster.io/webflow-assets/0e7b1185f920beef84a4.avif)

Mage is a data orchestration and integration platform that combines the interactive experience of notebooks with modular, production-ready code. It allows data teams to build, run, and monitor batch or streaming pipelines using Python, SQL, or R within a unified development environment. 

**Key features include:**

- **Hybrid development interface:** Combines a notebook-style UI with modular file-based code, enabling rapid experimentation and clean production code in one system.
- **Multi-language support:** Write pipeline steps in Python, SQL, or R, and mix them within a single pipeline for flexibility.
- **Real-time and batch processing:** Build pipelines that handle both real-time streaming and batch data workloads.
- **Interactive code execution:** Instantly preview the output of each block in the pipeline; no need to wait for full DAG execution.

**Orchestration and scheduling:** Manage triggers, monitor runs, and set schedules directly through the platform with full observability.

![](https://dagster.io/webflow-assets/39655aa3b8ea039a983a.avif)

#### 6. Metaflow

![](https://dagster.io/webflow-assets/a23655b27f4e691e070c.avif)

Metaflow is a human-centric Python framework that simplifies building, scaling, and deploying data science and machine learning workflows. Originally developed at Netflix, Metaflow provides a unified interface across the full stack of infrastructure (data, compute, orchestration, and versioning) so that data scientists can focus on modeling and experimentation.

**Key features include:**

- **Unified infrastructure API:** Abstracts away complexity by providing a single, high-level Python API to manage data, compute, orchestration, and deployment across the ML lifecycle.
- **Local-to-cloud workflow:** Prototype locally and scale to cloud environments (AWS Batch, Step Functions, Kubernetes, etc.) without rewriting code.
- **Production-ready orchestration:** Offers support for orchestrators with high availability and failure recovery, making it suitable for business-critical deployments.
- **Automatic versioning:** Tracks code, parameters, and artifacts across runs, enabling reproducibility, collaboration, and auditing without extra tooling.

**Flexible data handling:** Supports access to external data sources and manages data movement internally, ensuring consistent data access across environments.

![](https://dagster.io/webflow-assets/21bd009b987bb2dd9e5c.avif)

### Conclusion

Data pipeline orchestration tools play a critical role in managing the growing complexity of modern data systems. By automating task execution, enforcing dependencies, and ensuring observability, these tools reduce operational overhead and improve reliability. They enable teams to build scalable, maintainable workflows that align with evolving business and data requirements, supporting faster decision-making and more efficient data operations across the organization.

---

## Data Platform as a Service: 6 Key Capabilities & Top Use Cases

> A Data Platform as a Service (DPaaS) is a cloud-based, end-to-end managed service that simplifies data management by handling ingestion, storage, transformation, and analysis, freeing organizations from managing underlying infrastructure. DPaaS offers a unified, centralized platform for data.

Source: https://dagster.io/learn/data-platform-as-a-service

### What Is a Data Platform as a Service (DPaaS)? 

A [data platform](https://dagster.io/learn/data-platform) as a service (DPaaS) is a cloud-based, end-to-end managed service that simplifies data management by handling ingestion, storage, transformation, and analysis, freeing organizations from managing underlying infrastructure. 

DPaaS offers a unified, centralized platform for data, providing tools and services for various teams to share and use data efficiently, promoting data democratization and faster innovation through automated tasks and access to advanced analytics like AI and machine learning.

DPaaS platforms typically provide:

- **Centralized data management:** Offers a single location for storing, processing, and accessing an organization's data. 
- **Managed infrastructure:** The provider handles setup, maintenance, and upgrades of data infrastructure, reducing the burden on the organization. 
- **Automated workflows:** Automates repetitive, low-value tasks like data collection, cleaning, and manual processing, allowing teams to focus on higher-value work.
- **Data access and governance:** Provides secure, on-demand access to data via APIs or queries, often with built-in governance and security features. 
- **Scalability and agility:** Automatically scales to meet demand, enabling rapid innovation and flexibility in responding to business challenges. 

**Advanced analytics:** Includes tools and integrations for advanced analytics, business intelligence (BI), and artificial intelligence (AI), enhancing data-driven insights.

### What DPaaS Provides: 6 Key Capabilities 

#### 1. Centralized Data Management

DPaaS centralizes disparate data sources, giving organizations a unified view of their data landscape. This consolidation means businesses can manage structured and unstructured data from multiple environments within a cohesive framework. Through integration connectors, data pipelines, and metadata catalogs, users can discover, cleanse, enrich, and organize data assets for better downstream consumption.

Managing all data assets from one platform allows data stewards, engineers, and analysts greater visibility and control. They can define access controls, maintain [data quality](https://dagster.io/learn/data-quality), and implement lineage tracking to understand how and where data is being used.

#### 2. Managed Infrastructure

With DPaaS, infrastructure provisioning, scaling, and maintenance are handled entirely by the vendor. This offloads operational burdens, allowing organizations to avoid tasks like server patching, storage allocation, or network configuration. Managed infrastructure includes automated resource allocation and elastic scaling based on workload demand, helping organizations ensure high availability and performance without manual tuning.

Since updates and upgrades are managed centrally by the DPaaS provider, users can take advantage of the latest capabilities and security fixes with minimal disruption. Typical support includes managed databases, storage, compute, and backup services, often accompanied by built-in resiliency and disaster recovery.

#### 3. Automated Workflows

DPaaS platforms typically offer workflow automation features that orchestrate complex data tasks. This includes automating data ingestion, transformation, cleansing, and enrichment using visual workflow builders or declarative scripts. Automation reduces manual effort, minimizes errors, and ensures that data processes run on schedule or in real time to meet business demands. For example, organizations can set up rules to trigger ETL jobs the moment new data lands, or automatically process incoming streams for real-time analytics.

The benefits of automated workflows extend to repeatability and governance. Standardized pipelines enable consistent and auditable processing steps, which is crucial for data quality and regulatory compliance. Organizations can monitor execution, audit logs, and maintain visibility over who performs what actions within the platform.

#### 4. Data Access and Governance

DPaaS solutions provide access control mechanisms integrated with enterprise identity management systems. These controls let organizations define and enforce fine-grained policies over who can view, modify, or share specific datasets. Role-based access control (RBAC), attribute-based access control (ABAC), and data masking are common features, helping organizations comply with data privacy standards such as GDPR or HIPAA. 

Good governance in DPaaS encompasses data classification, data lineage, and retention policies. Automation of governance workflows helps maintain compliance and reduces manual overhead. Data owners can track how data is used throughout its lifecycle, set expiration schedules for outdated data, and ensure regulatory mandates for data retention and deletion are consistently applied across the organization.

#### 5. Scalability and Agility

DPaaS is designed for both vertical and horizontal scalability, making it suitable for organizations with fluctuating data volumes and diverse analytic needs. The cloud-native architecture of DPaaS enables automatic resource scaling, ensuring that as demand spikes, either from user growth or analytic complexity, performance is maintained without manual intervention. 

Agility in DPaaS is manifested in rapid provisioning of services, self-service analytics, and the ability to quickly add or decommission data sources and processing pipelines. Teams can experiment with new data-driven projects without waiting for infrastructure setup, which greatly accelerates innovation cycles. 

#### 6. Advanced Analytics

DPaaS platforms often integrate with advanced analytics tools, including machine learning libraries, predictive modeling engines, and data visualization frameworks. Users can train and deploy models directly within the platform, using built-in compute resources and scalable storage. This integration eliminates the need to move data between systems, reducing latency and preserving data security.

In addition to native capabilities, many DPaaS providers offer connectors to external analytics environments like Python, R, or notebook interfaces such as Jupyter. This flexibility allows data scientists and analysts to work with familiar tools while leveraging centralized data and infrastructure, enabling faster development of AI-driven applications and insights.

### Key Benefits of Data Platform as a Service 

A data platform as a service offers a unified solution for managing data at scale without the operational overhead of maintaining infrastructure or building custom integrations. Here are the key benefits DPaaS delivers to organizations:

- **Faster time to insights:** With pre-integrated tools for ingestion, transformation, and analytics, teams can move from raw data to actionable insights quickly, accelerating the value derived from data projects.
- **Reduced operational complexity:** By offloading infrastructure and platform management to the provider, organizations eliminate the need for in-house setup, maintenance, and scaling of systems.
- **Cost efficiency:** DPaaS uses a pay-as-you-go pricing model, converting capital expenses into predictable operational costs while avoiding over-provisioning and resource waste.
- **Improved data security and compliance:** Centralized governance features, access controls, and audit trails help organizations enforce security policies and meet regulatory requirements with less manual intervention.
- **Enhanced collaboration:** Centralized access to standardized data and tools enables data teams, analysts, and business users to collaborate more effectively using the same platform.
- **Rapid experimentation and innovation:** Self-service capabilities and quick provisioning allow teams to test new ideas, integrate data sources, and deploy analytics solutions without long lead times.
- **Future-proof architecture:** DPaaS solutions evolve with emerging technologies, ensuring access to modern features like real-time processing, AI integration, and multi-cloud support without costly migrations.

### DPaaS vs. Other Service Models 

#### DPaaS vs. IaaS

**Infrastructure as a service (IaaS)** delivers raw computing resources (servers, storage, and networking) on demand, but leaves all higher-level configuration and management to the customer. **DPaaS** provides a complete data management ecosystem on top of managed infrastructure, including services like data integration, governance, analytics, and automation. This abstraction layer removes the need for teams to maintain operating systems or middleware, focusing instead on leveraging and delivering data value.

While IaaS offers greater flexibility for organizations that want direct control over their stack, it also increases complexity and operational overhead. DPaaS, by offering “out-of-the-box” data services, reduces maintenance burdens and accelerates project delivery. Organizations balancing customization against ease of use often find that DPaaS better matches environments where fast innovation and productivity are a priority, not deep infrastructure control.

#### DPaaS vs. SaaS

**Software as a service (SaaS)** delivers specific software applications (such as CRM or ERP) via cloud, but does not offer the flexibility or control needed for custom data operations across multiple apps or sources. **DPaaS**, instead, provides a generalized data platform capable of supporting multiple workloads, data types, and custom analytics scenarios, extending utility beyond a single business function or vertical.

Whereas SaaS users typically interact only with application-layer data and preset workflows, DPaaS enables organizations to ingest, integrate, analyze, and govern data from various origins. The broader set of capabilities allows for custom pipeline creation, modeling, and advanced analytics, suiting enterprises seeking to build their own data products and workflows rather than relying solely on the capabilities of packaged applications.

#### DPaaS vs. PaaS

**Platform as a service (PaaS)** provides runtime environments and deployment frameworks to build, test, and deliver applications, but generally focuses on application rather than data workflow management. **DPaaS** is purpose-built for data lifecycle management; it covers both the hosting of applications as well as the ingestion, transformation, governance, and analysis of data at scale.

PaaS may offer some database services or basic data tooling, but DPaaS delivers a full suite for data engineering, sharing, BI, ML, and compliance, all with managed infrastructure and automation. Organizations looking for data-centric approaches, not just application development, derive more value from DPaaS, as it ensures end-to-end support for both operational and analytical data workloads within a single consolidated service.

### Typical Use Cases of DPaaS 

#### Real-Time Analytics and Streaming Data

DPaaS provides the backbone for processing and analyzing streaming data in real time, enabling organizations to respond instantly to business events. By integrating connectors for IoT sensors, web logs, or transactional streams, DPaaS can ingest high-velocity data, apply automated transformations, and deliver insights through dashboards or triggers. 

This capability supports operational intelligence use cases, such as fraud detection, targeted marketing, or supply chain optimization. The managed nature of DPaaS ensures resources scale automatically as event rates fluctuate, while prebuilt workflow automation enables continuous ingestion and processing without manual intervention. 

#### AI/ML Model Training and Deployment

DPaaS platforms feature built-in support for machine learning lifecycle management, including data preparation, feature engineering, model training, evaluation, and deployment. By offering managed compute clusters and easy [data pipeline](https://dagster.io/learn/data-pipeline) integration, DPaaS allows data scientists to scale model development efficiently, regardless of dataset size or complexity. 

Advanced analytics functions, such as distributed training and automated hyperparameter tuning, are made accessible without complex setup. Integration with model repositories and deployment tools means organizations can take models from experimental notebooks to production endpoints directly within the platform. 

#### IoT and Sensor-Driven Systems

IoT applications generate vast amounts of high-frequency, diverse, and often unstructured data coming from sensors, devices, and edge endpoints. DPaaS platforms are uniquely suited for collecting, storing, and analyzing this data due to their scalable ingest pipelines and integration with edge computing frameworks. 

Automated data normalization, aggregation, and enrichment ensure high data quality before downstream analysis or machine learning. Real-time processing and event-driven workflows help identify anomalies, trigger maintenance, or optimize operations in sectors such as manufacturing, smart infrastructure, or logistics. 

#### Business Intelligence and Reporting

DPaaS simplifies the process of curating, transforming, and visualizing enterprise data for business intelligence (BI). Data from disparate sources can be brought together in a central repository, regularly cleaned and prepared through automated workflows, then made available to reporting and analytics tools. 

This automation accelerates the creation of up-to-date dashboards and custom reports that underpin critical business decisions. Integrated security, governance, and auditing features ensure that only trusted, compliant data fuels BI initiatives. Business analysts can use built-in visualizations or connect BI software to self-serve data needs, minimizing reliance on IT staff. 

### Best Practices for Adopting DPaaS 

Organizations should consider the following when choosing data platform-as-a-service offerings.

#### 1. Define Clear Governance Policies Early

Organizations should establish governance frameworks as the foundation of their DPaaS adoption. This includes setting data ownership roles, classification standards, access controls, and compliance rules before scaling workloads on the platform. Early governance alignment reduces friction, helps prevent shadow IT, and ensures consistent security and regulatory compliance as data volumes and users grow.

Clear policies must be documented, periodically reviewed, and adapted to evolving business and regulatory needs. Automated enforcement, using features like role-based access control and audit logging, minimizes manual intervention and human error. With governance as a first principle, organizations can confidently expand DPaaS use while reducing long-term risk.

#### 2. Standardize on Contracts for Data Exchange

Standardizing data exchange contracts, such as schema definitions or API specifications, consistently across teams and integrations is key to maintaining quality and interoperability in DPaaS environments. Data contracts provide a clear, enforced agreement on data structure, semantics, and expectations, reducing integration errors and misunderstandings between workflows or partnering systems.

These standards help future-proof data systems by making it easier to onboard new data sources or swap out components without breaking downstream processes. Consistent use of well-defined schemas, [data catalogs](https://dagster.io/guides/data-catalog-components-challenges-5-critical-best-practices), and version control supports both compliance and operational resilience. This approach paves the way for better collaboration and scalability as your DPaaS implementation expands.

#### 3. Prioritize Interoperability and Portability

Organizations adopting DPaaS should ensure that their chosen platform supports open standards, APIs, and connectors to avoid lock-in and maintain flexibility as business needs change. Prioritizing interoperability allows integration with external systems, legacy applications, and third-party data products, enabling a wider range of hybrid or multi-cloud deployments.

Open formats and standardized interfaces enable data portability, minimizing migration challenges and future-proofing your investment. Portability also includes leveraging modular and loosely coupled architectures within the data platform. This approach enables the movement of data, models, or workloads between environments or providers when required, preserving business continuity and agility. 

#### 4. Automate Observability and Monitoring

Automated observability is essential for ensuring the health, security, and performance of DPaaS workloads. Organizations should leverage built-in platform tools, such as real-time dashboards, alerts, and telemetry, to monitor data pipelines, job execution, and infrastructure utilization. This enables rapid detection of anomalies, bottlenecks, or failures, minimizing downtime and ensuring data quality.

Continuous monitoring also helps by providing audit trails, change histories, and compliance reports necessary for regulated industries. Automated alerts and self-healing mechanisms allow operators to proactively manage incidents, reducing reliance on manual checks.

#### 5. Invest in Data Literacy and Training

Successful DPaaS adoption depends heavily on organizational readiness and the skills of its users. Investing in data literacy programs ensures all stakeholders, from technical teams to business analysts, understand both the platform’s capabilities and their responsibilities regarding data usage, security, and governance. Regular training and upskilling help everyone maximize value and minimize risk.

Ongoing education should cover platform updates, new features, and emerging best practices, fostering a culture of continuous improvement. Equipping teams to experiment safely and efficiently with data tools ensures widespread adoption and accelerates maturity on the DPaaS journey.

---

## Data Platform: Core Functions and 6 Tools to Know in 2026

> A data platform is a system to manage, process, store, and analyze data from various sources.

Source: https://dagster.io/learn/data-platform

### What Is a Data Platform?

A data platform is a system to manage, process, store, and analyze data from various sources. It provides an integrated environment where data can flow through different stages—from ingestion to transformation and analysis—offering tools for data management and accessibility.

Data platforms are used by data engineers, data scientists, business analysts, and IT administrators. These users typically aim to simplify data workflows, ensure the quality of data, and derive actionable insights. For example, data engineers use platforms to automate data pipelines, while analysts leverage the platform's analytics capabilities to uncover trends and patterns.

Organizations benefit from data platforms by gaining centralized control over their data, enabling better decision-making, enhancing collaboration, and improving operational efficiency. Additionally, the scalability of these platforms ensures that they can adapt to growing data needs without compromising performance.

### The Benefits of Data Platforms

Using a data platform provides the following advantages.

#### Centralized Data Management

Centralized data management consolidates data from diverse sources into a single, unified platform. This approach simplifies data governance by providing a central location to enforce security policies, ensure compliance, and manage access controls. It also eliminates data silos, making it easier to maintain consistent data quality and prevent duplication.

#### Scalability

A scalable platform allows organizations to add resources seamlessly, ensuring performance remains consistent without service disruption. This capability is crucial for organizations operating in rapidly changing markets or expanding their data operations. Scalable platforms also offer cost efficiency by aligning resources to current needs, reducing waste.

#### Improved Data Accessibility

Accessible data enhances collaboration among team members, fostering more informed decision-making processes. Easy access is enabled by interfaces and connectivity, reducing time spent in data retrieval and boosting productivity. Enhancing accessibility also democratizes data usage within an organization, allowing users to generate insights independently.

#### Analytics and Machine Learning Integration

Integrating analytics and machine learning within data platforms enables organizations to gain deeper insights and drive predictive decision-making. These capabilities allow organizations to process large data sets at speed, identifying patterns and trends that are not visible through traditional analysis methods.

### The Core Functions of Modern Data Platforms

A data platform should support the following functions.

#### Data Storage and Processing

Data storage and processing layers ensure data is accessible, stored securely, and processed in a format suitable for analysis. With technologies like cloud storage and distributed databases, organizations can store data at scale while maintaining rapid access for real-time analysis.

Storage solutions are complemented by processing capabilities that transform raw data into structured insights. Processing layers use parallel computing and in-memory analytics to handle complex computations, enabling companies to keep pace with data velocity.

#### Data Ingestion

Data ingestion is the process of Data ingestion capabilities are built on scalable architectures that manage changing data loads. They provide data validation mechanisms, ensuring data accuracy and reliability before processing. This pre-processing step is vital for maintaining data quality and integrity.

#### Data Transformation and Modeling

Data transformation involves converting raw data into a consumable format while modeling defines the shape and structure of data for analysis. This phase ensures that data aligns with business rules and objectives, delivering meaningful insights. Transformation tools automate processes like cleaning, aggregating, and enriching data, making it analysis-ready.

Data modeling techniques establish relationships among data entities, supporting complex queries and analytics tasks. Effective data transformation and modeling improve data usability, allowing users to derive actionable insights.

#### Business Intelligence (BI) and Analytics

Business intelligence tools present data through dashboards and reports, aiding stakeholders in strategic planning and operational decisions. These tools allow for real-time monitoring of key performance indicators.

Advanced analytics further extends BI capabilities by supporting predictive and prescriptive analysis. This aspect of data platforms enables organizations to identify future opportunities and threats, optimizing strategic initiatives.

#### Machine Learning and AI

Modern data platforms integrate [machine learning and AI](https://dagster.io/solutions/ai) to enable advanced analytics and automation. These tools analyze vast datasets to uncover patterns, optimize operations, and generate predictions. For example, ML models can detect anomalies in real-time, supporting fraud detection or predictive maintenance in industrial systems.

Additionally, AI capabilities improve decision-making by automating routine tasks, such as data classification or model deployment. This integration allows organizations to scale their data initiatives more easily, providing faster insights and actionable intelligence.

#### Data Observability

Data observability provides insight into the health and performance of data systems, ensuring reliability and efficiency across data flows. By monitoring data pipelines, platforms can detect errors, anomalies, and bottlenecks, helping to mitigate issues before they impact operations. Observability tools offer dashboards and alerts that give teams visibility into data processes.

This monitoring extends across all layers, contributing to data quality and compliance. Observability is critical for diagnosing issues quickly and maintaining seamless data operations.

#### Data Orchestration

[Data orchestration](https://dagster.io/platform-overview/data-orchestration) is the coordination of complex data processes, simplifying the flow of data across platforms and systems. It involves automating workflows, ensuring that data moves seamlessly from ingestion through transformation to analytics. Orchestration tools manage dependencies and scheduling, reducing manual intervention.

Through orchestration, organizations can achieve more precise data management, optimizing resources and minimizing errors. This automation reduces bottlenecks, allowing more agile responses to data processing needs.

### Data Platforms vs Related Concepts

#### Data Platform vs Data Architecture

**A data platform** refers to the integrated set of tools and infrastructure used to collect, store, process, and manage data, providing an operational framework for handling data. It includes the hardware, software, and systems that allow data to be efficiently ingested, stored, processed, and analyzed.

**Data architecture** defines the structural framework or blueprint for how data is managed, stored, and moved throughout an organization. It governs the design principles, standards, and processes that ensure data is accessible, secure, and properly organized.

While a data platform provides the tools and operational environment, the architecture outlines the structure and governance needed for effective data management.

#### Data Platform vs Data Warehouses and Data Lakes

**A data platform encompasses** a broad set of functions, including data ingestion, storage, processing, transformation, and analysis. It integrates various data technologies to manage data across its lifecycle.

**A data warehouse** is a specialized component focused on storing (primarily) structured data from multiple sources, optimized for querying and reporting. Data warehouses are suitable for historical data analysis and business intelligence tasks.

**A data lake** is another type of storage system within a data platform, designed to hold large amounts of raw, unstructured, or semi-structured data. Unlike data warehouses, data lakes store data in its native format until it's needed for processing.

#### Data Platforms vs ETL Pipelines

**An ETL pipeline** (Extract, Transform, Load) refers to the processes that move and transform data from one system to another, typically from raw data sources into a more usable format in databases or data warehouses. It focuses on data flow and transformation logic, ensuring data is clean and ready for analysis.

**A data platform** provides an environment that includes ETL pipelines but also covers data storage, processing, analytics, and governance. ETL pipelines are just one component within the broader scope of a data platform, which supports the entire data lifecycle from ingestion to advanced analytics.

#### Data Platforms vs Data Analytics

**A data platform** provides the infrastructure and tools to manage data, enabling various data-related tasks, including analytics. It offers storage, processing power, and integration capabilities for data from multiple sources, enabling a smooth workflow for analysis.

**Data analytics** refers to the process of analyzing data to extract actionable insights. While a data platform supplies the necessary environment to support analytics, data analytics focuses on the methodologies and tools used to interpret data, such as statistical analysis, machine learning, or business intelligence tools.

### Notable Data Platforms

#### 1. Dagster

![Dagster](https://dagster.io/webflow-assets/c614eacf60673508ea11.png)

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle, with a declarative programming model, integrated lineage and observability, data validation checks, and best-in-class testability.

**Key features of Dagster:**

- **Data asset-centric:** Focuses on representing data pipelines in terms of the data assets (e.g., tables, machine learning models, etc.) that they generate, yielding an intuitive, declarative mechanism for building data pipelines.
- **Observability and monitoring:** Aims to be the “single pane of glass” for your data team, with a robust logging system, built-in data catalog, asset lineage, and quality checks.
- **Cloud-native and cloud-ready:** Provides a managed offering with robust, managed infrastructure, elegant CI/CD capability, and multiple deployment options for custom infrastructure.
- **Integrations:** Extensive library of integrations with the most popular data tools, including the leading cloud providers (AWS, GCP, Azure), ETL tools (Fivetran, Airbyte, dlt, Sling), and BI tools (Tableau, Power BI, Looker, and Sigma).
- **Flexible:** As a Python-based data orchestrator, Dagster affords you the full power of Python. Using Pipes, It also lets you execute arbitrary code in other programming languages and on external (remote) environments, while retaining Dagster’s best-in-class observability, lineage, cataloging, and debugging capabilities.
- **Declarative Automation:** This feature lets you go beyond cron-based scheduling and intelligently orchestrate your pipelines using event-driven conditions that consider the overall state of your pipeline and upstream data assets. This reduces redundant computations and ensures your data is always up-to-date based on business requirements and SLAs, instead of arbitrary time triggers.

[**Learn more about the Dagster platform**](https://dagster.io/platform-overview)

![](https://dagster.io/webflow-assets/df4a53188af57663405e.png)

#### 2. Snowflake

![Snowflake](https://dagster.io/webflow-assets/af09746e1787aa19584e.png)

Snowflake is a fully managed data platform supporting the modern AI Data Cloud. It enables organizations to securely manage data at scale while offering tools to productize AI, analytics, and applications.

**Key features of Snowflake:**

- **Elastic compute:** Supports diverse workloads, including streaming pipelines, AI, and analytics, with flexible compute options like GPUs.
- **Unified data access:** Consolidates structured, semi-structured, and unstructured data with nearly infinite scalability and interoperability.
- **Cortex AI:** Enables serverless large language models (LLMs) for natural language processing and custom summaries at scale.
- **Fully managed cloud services:** Automates complex operations and applies performance improvements, reducing overhead.
- **Cross-cloud collaboration:** Supports data sharing, governance, and business continuity across different cloud environments.

![](https://dagster.io/webflow-assets/46c2931e762484b4e37e.png)

#### 3. Databricks

![Databricks](https://dagster.io/webflow-assets/c6dac9591202e230bc7a.png)

Databricks is a unified data platform that simplifies data and AI usage across an organization. Built on a lakehouse architecture, it combines data management and governance with AI capabilities, making it easier to understand enterprise data.

**Key features of Databricks:**

- **Data Intelligence Engine:** Utilizes generative AI to automatically optimize performance, infrastructure, and workflows based on your data’s specific needs.
- **Unified lakehouse architecture:** Provides a single platform for managing all data types with integrated governance, ensuring easy access and collaboration.
- **Natural language interface:** Simplifies data discovery and application development with large language models (LLMs), allowing users to ask questions or write code using natural language queries.
- **End-to-end AI and MLOps:** Supports the entire AI lifecycle, from building models with APIs like OpenAI to developing custom solutions, while maintaining strict data privacy and intellectual property control.
- **Security:** Ensures secure, private AI development with a unified approach to governance and data protection.

![](https://dagster.io/webflow-assets/496be09abd1fe0d27bd0.png)

#### 4. Google BigQuery

![BigQuery](https://dagster.io/webflow-assets/a09cd029b013b902afb5.png)

Google BigQuery is a fully managed, serverless data platform that enables organizations to analyze massive datasets with built-in machine learning, geospatial analysis, and business intelligence features. With its distributed architecture, BigQuery separates compute and storage layers, allowing management and real-time analysis of structured and unstructured data.

**Key features of Google BigQuery:**

- **Serverless architecture:** Automatically manages infrastructure, enabling users to run queries without provisioning or scaling resources.
- **Separation of compute and storage:** Ensures independent, dynamic resource allocation for optimal performance without downtime.
- **Built-in machine learning and geospatial analysis:** Provides integrated tools for predictive analytics and geographic information systems (GIS), simplifying complex data analyses.
- **Multi-language support:** Enables data analysis with SQL, Python, Java, and other popular languages.
- **Scalable streaming and batch ingestion:** Handles continuous data ingestion and queries terabytes of data in seconds or petabytes in minutes.

![](https://dagster.io/webflow-assets/daccad5d008ccf8a6f68.png)

#### 5. Microsoft Fabric

![Fabric](https://dagster.io/webflow-assets/04ed6defab4b951ee059.png)

Microsoft Fabric is a unified data platform that helps accelerate AI innovation, providing teams with integrated tools for analytics, data management, and artificial intelligence. By combining AI-powered capabilities with mission-critical infrastructure, Fabric enables organizations to harness their data and drive smarter decision-making.

**Key features of Microsoft Fabric:**

- **AI-powered tools**: Provides tailored solutions for a range of data projects, enabling teams to build AI-ready workflows and applications more easily.
- **Unified data integration**: Offers tools to unify data estates, simplifying complex ETL scenarios and ensuring seamless data flow across the organization.
- **Data engineering**: Supports the design of systems capable of managing and analyzing large-scale datasets.
- **Data science workflows**: Includes AI tools to create end-to-end workflows, enrich data, and deliver actionable insights.
- **Scalable data warehousing**: Features independently scalable compute and storage with industry-leading SQL performance for optimized data management.

![](https://dagster.io/webflow-assets/cfeca55977800fd348ad.png)

#### 6. Apache Kafka

![Kafka](https://dagster.io/webflow-assets/a3fb8bb236cd5925ffb9.png)

Apache Kafka is an open-source distributed event streaming platform for high-performance data pipelines, real-time analytics, data integration, and mission-critical applications across various industries. It is offered under the Apache-2.0 license. It enables organizations to handle vast amounts of data while ensuring high availability and fault tolerance.

**Key features of Apache Kafka:**

- **High throughput:** Capable of delivering messages at network-limited speeds with latencies as low as 2 milliseconds, supporting trillions of messages daily.
- **Scalable architecture:** Easily scales up to thousands of brokers, petabytes of data, and hundreds of thousands of partitions, while elastically managing storage and processing.
- **Durable storage:** Stores streams of data safely in a fault-tolerant, distributed cluster that ensures data integrity and availability.
- **Built-in stream processing:** Offers exactly-once processing and supports complex stream transformations, joins, and aggregations using event-time semantics.
- **Extensive connectivity:** Integrates with hundreds of event sources and sinks, including databases, cloud services, and messaging systems via the Kafka Connect interface.

### Best Practices to Build an Effective Data Platform

Here are some of the measures organizations should consider to provide an effective platform for their data.

#### Modular Architecture

A modular architecture ensures flexibility, scalability, and ease of maintenance. In a modular setup, different components such as data ingestion, storage, processing, and analytics are decoupled, allowing each module to be developed, updated, or replaced independently without disrupting the entire system.

This approach also enables integration with new technologies and tools, allowing organizations to adapt quickly to changing business needs or innovations in the data space. A modular architecture promotes reusability and reduces development time, as existing modules can be leveraged across multiple use cases.

#### Batch and Streaming Support

The data platform must support both batch and streaming data processing to handle diverse data needs. Batch processing is suitable for large volumes of data that can be processed at scheduled intervals, such as overnight data jobs or monthly reports. It is efficient for tasks that do not require real-time results.

Streaming support is important for use cases requiring real-time data processing, such as fraud detection, monitoring systems, or dynamic pricing models. Streaming enables immediate data analysis and decision-making, providing organizations with timely insights.

#### Automated ETL/ELT Pipelines

Automated ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines are critical for managing the continuous flow of data from various sources into the data platform. Automation ensures that data is consistently extracted, cleaned, and transformed without manual intervention, reducing errors and saving time.

ETL pipelines are typically used when data transformation is needed before loading it into a target system, such as a data warehouse. ELT is beneficial when data is loaded in its raw form and transformed later, often using the processing power of the data platform itself. Both approaches should be automated to streamline data ingestion and improve reliability.

#### Data Storage Optimization

Optimization techniques such as data partitioning, compression, and tiered storage (storing frequently accessed data in faster, more expensive storage, while archiving less-used data in cheaper, slower storage) can significantly reduce storage costs and improve performance.

Effective storage optimization also includes managing data redundancy and ensuring that storage systems are scalable to handle future data growth. Leveraging cloud storage solutions with built-in optimization capabilities, such as auto-scaling and distributed storage, ensures that the platform can meet data demands without bottlenecks.

#### Data Catalogs

A data catalog provides a comprehensive inventory of data assets, including metadata, data lineage, and data ownership, making it easier for users to find and understand available datasets. Implementing a data catalog promotes data discoverability and governance within an organization.

Data catalogs help ensure that data is well-documented and easily discovered, reducing duplication of effort and increasing efficiency in data analysis. They also support compliance with regulatory requirements by tracking data usage and ensuring proper access controls are in place.

#### Performance Optimization

Performance optimization is crucial for ensuring that a data platform can process and analyze large datasets quickly and efficiently. This can be achieved through techniques such as query optimization, caching frequently used data, and using distributed computing to parallelize tasks across multiple nodes.

Additionally, monitoring tools should be used to identify performance bottlenecks, such as slow-running queries or overloaded resources. Optimizing the platform's performance ensures that data pipelines run smoothly, minimizing delays and enabling faster insights.

### Conclusion

Building an effective data platform requires careful consideration of modularity, scalability, and automation to ensure seamless data processing and analytics. By integrating both batch and streaming capabilities, optimizing data storage, and utilizing automated pipelines, organizations can streamline their data workflows and unlock valuable insights. Adopting performance optimization techniques and proper governance through data catalogs further ensures that the platform remains efficient, compliant, and ready to scale with evolving business needs.

Dagster enables these best practices and more by serving as the unified control plane for your data platform. You can use it to define and orchestrate your workflows in a declarative manner and improve data governance and developer productivity with its native support for data lineage and observability, data validation checks, and best-in-class testability.

[**Learn more about the Dagster platform**](https://dagster.io/platform-overview)

### FAQs about Data Platforms

#### Which functions do modern data platforms support?

Modern data platforms support a comprehensive suite of capabilities that manage the entire data lifecycle, from raw ingestion to actionable insights. Core functions include:

- Data storage and processing. Securely stores data at scale and enables efficient processing for real-time and historical analysis.
- Data ingestion. Collects data from diverse internal and external sources, ensuring reliability and consistency as it enters the platform.
- Data transformation and modeling. Cleans, enriches, and structures data so it’s ready for analytics and aligned with business logic.
- Business intelligence and analytics. Provides visualization and reporting tools that turn data into insights for strategic and operational decisions.

#### How does a data platform contribute to business value?

By centralizing data management, data platforms enable better decision-making, boost operational efficiency, and ease the growing pains that come with scaling up. They help organizations uncover insights faster, automate repetitive data tasks, and power advanced use cases like predictive analytics and AI. In doing so, they turn data into a direct driver of growth and innovation.

#### What are common pitfalls to avoid when implementing a data platform?

When implementing a data platform, organizations should watch out for several common mistakes that can limit scalability, reliability, and long-term success:

- Siloed data environments. Keeping data isolated across teams or systems prevents unified visibility and reduces collaboration.
- Over-engineering solutions. Building custom tools for challenges already solved by existing technologies adds unnecessary complexity and maintenance burden.
- Short-sighted design. Failing to plan for future data growth or new use cases leads to costly rework and scalability issues later on.
- Monolithic architectures. Rigid, tightly coupled systems slow development and make it difficult to adapt to changing business needs.

Avoiding these pitfalls helps ensure that a data platform remains flexible, scalable, and dependable as organizational needs evolve.

#### How do data platforms differ from data warehouses or data lakes?

The main difference: data warehouses and data lakes are parts of a data platform. A data platform connects and manages warehouses and lakes, among other components. 

A data warehouse stores structured, historical data optimized for reporting, while a data lake holds large volumes of raw or semi-structured data for future processing. A data platform serves as the foundation that unifies these components, enabling consistent governance, scalability, and end-to-end visibility across all data operations.

‍

---

## Data Testing: Methods, Examples, and Techniques

> Data testing is the process of validating datasets for quality, accuracy, and reliability to ensure they meet requirements and are fit for use in business decisions and operations. It involves checking for issues like missing values, duplication, and inconsistencies to uphold data integrity.

Source: https://dagster.io/learn/data-testing

### What Is Data Testing?

Data testing is the process of validating datasets for quality, accuracy, and reliability to ensure they meet requirements and are fit for use in business decisions and operations. It involves checking for issues like missing values, duplication, and inconsistencies to uphold data integrity throughout its lifecycle. Key aspects include testing the accuracy of data transformations, validating data structures, and ensuring data adherence to business rules to prevent negative consequences from poor [data quality](https://dagster.io/learn/data-quality).

Data testing uses rules, checks, and validation steps to confirm data integrity and suitability throughout data processing. This practice extends beyond simple data entry validation. It identifies data quality issues such as missing records, incorrect values, duplication, and anomalies. In software development, analytics, and data engineering projects, data testing detects potential errors early in the data pipeline.

### Why Is Data Testing Important? 

Data testing ensures systems built on data deliver accurate, reliable outcomes. Without it, errors can propagate silently and compromise business decisions, analytics, and operations. The following points outline its importance:

- **Ensures data quality**: Detects and prevents issues like duplicates, missing fields, incorrect formats, and inconsistent values that can corrupt analyses or application behavior.
- **Supports reliable decision-making**: Maintains trust in reports and analytics by ensuring the underlying data is valid, consistent, and timely.
- **Reduces risk of costly errors**: Identifies and fixes issues early in [data pipelines](https://dagster.io/guides/data-pipeline), minimizing the cost and impact of defects found later in production or reporting stages.
- **Improves system integration**: Validates data flows between systems, ensuring that transformations, migrations, and synchronizations preserve data accuracy and completeness.
- **Enables compliance and governance**: Helps organizations meet regulatory requirements by ensuring that data meets predefined rules and standards, reducing legal and compliance risks.
- **Boosts automation confidence**: Valid data allows for more reliable automation in processes like machine learning, reporting, and user-facing applications.
- **Increases development efficiency**: Identifying issues early in development cycles reduces rework, shortens debugging time, and helps teams deliver faster.

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data reliability</em></strong>](https://dagster.io/guides/data-reliability-challenges-measurement-best-practices)

### Key Methods of Data Testing 

#### Data Completeness Testing

Data completeness testing checks whether expected data elements are present or missing from datasets, tables, or records. This method evaluates if all fields required for an application or process to function are populated. Completeness is tested through rules that assert the presence of mandatory values, the count of expected records, and the absence of nulls in critical columns. By running completeness tests at ingestion, transformation, and storage phases, teams catch absence of data early.

#### Data Consistency Testing

Data consistency testing ensures that data is uniform and conforms to defined rules across different systems, datasets, or batches within a single system. This includes verifying that similar data fields hold the same values and formats, and that changes in one part of a system are reflected appropriately wherever the data is replicated or referenced. Consistency checks are run when synchronizing databases, reconciling between environments, migrating data, or updating distributed systems.

#### Data Accuracy Testing

Data accuracy testing measures whether data values match the actual, correct, or intended real-world values. This may involve comparing stored data against authoritative references or sources, applying rules to detect impossible or illogical values, or validating against known patterns (such as date ranges, product codes, or geographic boundaries). By embedding accuracy tests, like verifying customer records, financial transactions, or sensor outputs, teams prevent propagation of bad data.

#### Data Integrity Testing

Data integrity testing assesses whether data maintains its validity, reliability, and structure across its lifecycle. It checks for adherence to constraints such as primary keys, foreign keys, referential integrity, and other schema-level rules that prevent orphaned or duplicated records. Integrity tests are vital during schema changes, data migrations, and as part of ongoing database maintenance routines.

#### Data Validation Testing

Data validation testing enforces rules and constraints at the time of data entry, processing, or transmission. These rules can include value ranges, type checks, format validations (e.g., acceptable date or email formats), and business logic validations that enforce domain-specific policies. Validation testing prevents incorrect or malicious data from entering the system.

#### Data Regression Testing

Data regression testing verifies that recent changes to data pipelines, logic, or applications have not inadvertently introduced data defects, loss, or inconsistencies. This is done by comparing output datasets, metrics, or business outcomes before and after changes are made, often using automated scripts.

#### Data Performance Testing

Data performance testing measures the efficiency and responsiveness of systems when processing, storing, or moving data. Key metrics include execution time, throughput, latency, and resource utilization under various workloads, user loads, or data volumes. By regularly measuring, monitoring, and stress-testing at key parts of the data pipeline, such as ingestion, transformation, and query layers, teams can proactively address scalability problems.

### Examples of Data Testing

#### ETL / ELT Testing

ETL and ELT testing verifies that data extraction, transformation, and loading steps behave as expected across pipeline stages. It focuses on confirming that source records are read correctly, transformation rules are applied consistently, and target tables receive complete and accurate outputs. These tests also check mappings, type conversions, row counts, and logic that reshapes or filters data before it reaches downstream systems.

**Examples:**

- Daily customer snapshots are transformed incorrectly because a date-parsing rule drops records with regional formats.
- Product category mappings fail when a new source code is introduced but not reflected in the transformation logic.
- A batch load duplicates rows after a developer changes the merge condition in the load script.

#### Database Testing

Database testing evaluates schema structure, constraint enforcement, and the behavior of stored code such as triggers and procedures. It ensures that tables accept only valid data, referential integrity rules prevent broken relationships, and CRUD operations return consistent results. It also checks versioned schema changes to confirm that updates preserve existing records and that rollback paths restore expected states.

**Examples:**

- A new foreign key constraint blocks order creation because the lookup table was populated incorrectly.
- A trigger on an audit table fires twice after a code update and creates duplicated audit entries.
- A stored procedure that calculates discounts produces different results after a schema migration changes numeric precision.

#### API Testing

API testing validates how data flows through service endpoints by examining payload formats, parameter handling, response codes, and contract adherence. It ensures that services reject malformed inputs, serialize and deserialize data correctly, and return consistent structures. These tests identify mismatches between producer and consumer systems and catch issues that lead to incomplete or corrupted data transfers.

**Examples:**

- A GET endpoint returns timestamps in an unexpected format after a library upgrade, breaking a downstream parser.
- A POST request accepts missing fields, causing incomplete records to propagate to the database.
- A service returns an HTTP 200 code for partial failures instead of the expected error code.

#### Streaming Data Testing

Streaming data testing verifies ingestion and processing pipelines that handle continuous, time-ordered events. It checks message ordering, windowing logic, and aggregation rules while simulating changing data rates, dropped messages, or network delays. These tests validate that the system processes events within required latency limits and handles out-of-order or abnormal data without corrupting downstream analytics.

**Examples:**

- A sensor stream produces events out of order, causing incorrect rolling averages in a time-windowed operator.
- A high-volume spike from mobile clients results in delayed processing and missed event windows.
- A consumer fails to handle malformed events, causing the stream to stall until manual intervention.

#### Big Data Testing

Big data testing examines large, distributed workloads running on platforms such as Spark or Hadoop. It checks how data is partitioned, processed, and replicated across nodes, and evaluates job performance under scale. Tests measure data completeness after transformations, identify skewed partitions, verify consistency across storage layers, and detect bottlenecks that arise when parallel tasks handle uneven or corrupted inputs.

**Examples:**

- A Spark job produces inconsistent results because a partition key change creates extreme data skew.
- A large aggregation job drops records after hitting executor memory limits.
- A Hadoop ingestion pipeline writes incomplete HDFS blocks when a node experiences intermittent failures.

### How to Determine What You Should Test 

Choosing what to test depends on the type of system, the role of the data, and the risks that incorrect data introduces. Not every dataset or pipeline requires the same level of scrutiny. A systematic approach helps teams prioritize tests where they add the most value.

- **Understand business and regulatory requirements:** Start by identifying how the data will be used and what decisions or processes depend on it. Data that feeds compliance reporting, financial transactions, or customer-facing applications should receive the strictest testing.
- **Analyze data flow and transformation points:** Focus testing on stages where data is ingested, transformed, or moved between systems. These transitions are the most common points for errors such as truncation, mapping mistakes, or type mismatches.
- **Assess data criticality and sensitivity:** Classify datasets by importance and sensitivity. Critical master data (e.g., customers, products, accounts) and sensitive personal or financial data require more thorough integrity, accuracy, and security testing than low-value operational logs.
- **Evaluate system complexity and change frequency:** Complex pipelines or systems that undergo frequent updates demand regression testing to ensure changes don’t introduce errors. Stable, rarely changing systems may need fewer ongoing tests but still benefit from periodic validation.
- **Balance coverage with resources:** Testing every possible scenario is rarely feasible. Teams should prioritize high-risk areas and automate recurring checks while using sampling or targeted tests for lower-risk data.

### Data Testing Strategies and Techniques 

#### Data Validation Rules and Constraints

Validation rules and constraints form the first line of defense against bad data entering a system. At the structural level, they enforce schema requirements such as data types, field lengths, primary and foreign key relationships, and uniqueness. 

At the business logic level, they enforce rules like valid ranges (e.g., dates must not be in the future), acceptable enumerations (e.g., ISO country codes), or dependencies between fields (e.g., a shipping address is required if an order has a delivery option). These rules can be applied during data entry through forms or APIs, within ETL pipelines, or directly in database constraints. 

#### Data Profiling and Baseline Establishment

Data profiling involves exploring datasets to understand their structure, content, and quality before applying formal tests. Profiling typically analyzes distributions, value frequencies, unique keys, null rates, min/max ranges, outliers, and correlations between fields. These insights establish a baseline: what the “normal” dataset looks like in terms of size, distribution, and patterns. 

Baselines act as reference points for detecting anomalies, drifts, or deviations when new data arrives. Profiling also helps testers prioritize where to add checks. Tools like Apache Griffin, Great Expectations, or custom SQL scripts are often used to automate profiling and baseline generation.

#### Source-to-Target Reconciliation

Source-to-target reconciliation ensures that data migrated, synchronized, or transformed between environments maintains integrity. It begins with verifying record counts to ensure no rows are lost or duplicated. Next, key column checks, such as sums of amounts, max/min values, or hash totals, are compared to validate content accuracy. 

For transformations, reconciliation confirms that business rules (e.g., currency conversion, tax calculation, data enrichment) were applied correctly and consistently. In large-scale migrations, reconciliation may use sampling combined with aggregation to handle massive volumes.

#### Sampling and Partition Testing

Validating every record in large-scale datasets is often computationally infeasible. Sampling reduces test execution time by validating subsets of data while still achieving statistical coverage. Random sampling ensures a broad representation, while stratified or rule-based sampling ensures edge cases and critical segments are included. 

Partition testing goes further by deliberately dividing datasets into logical segments, such as date ranges, value boundaries, or categorical groups, and validating each partition independently. Together, sampling and partitioning strike a balance between efficiency and thoroughness.

#### Exception / Negative Testing

Exception and negative testing validate system robustness by introducing incorrect, incomplete, or malicious data into the pipeline. This includes testing with missing mandatory fields, invalid characters, oversized payloads, duplicate keys, out-of-range numbers, or inconsistent cross-field relationships. 

The goal is to verify that the system does not silently accept or corrupt such data, but instead rejects it or raises meaningful errors. Exception testing is critical for security as well, ensuring that injection attacks, malformed requests, or corrupted file formats do not bypass validations. Negative testing is often automated by combining fuzzing techniques with schema-based validation.

### Best Practices for Effective Data Testing 

Organizations can improve their data testing strategies by applying the following best practices.

#### 1. Align Testing with Business Requirements

Data testing should not exist in isolation from the organization’s goals. Each test case needs to reflect how data is actually used in decision-making, compliance reporting, or customer-facing processes. 

For example, a retail company may prioritize validating inventory accuracy across warehouses, while a financial institution must focus on reconciling transaction records to meet audit standards. By starting with business priorities, teams can design tests that directly protect outcomes such as revenue recognition, customer experience, or regulatory compliance.

#### 2. Automate Tests for Scalability

Manual checks quickly become impractical in modern data environments, where pipelines handle millions of records per day. Automation ensures that validation can run continuously at scale, catching issues in real time rather than weeks later during reporting cycles. 

Automated data quality frameworks like Great Expectations, dbt tests, or custom SQL-based test harnesses can run checks at ingestion, transformation, and delivery layers. Integrating these into CI/CD pipelines means every schema migration, ETL change, or data model update is automatically validated before deployment. Automation also enables regression testing, where historical outputs are compared against new ones to detect unintended drifts.

#### 3. Use of Production-Like Test Data

Testing is only as good as the data it runs against. If test datasets are too small, too clean, or lack edge cases, they fail to surface real-world problems. Using production-like data (either by sampling, masking sensitive fields, or generating synthetic data that mirrors distributions) ensures tests simulate actual operating conditions. 

This approach helps detect skew, null rates, encoding mismatches, and rare anomalies that toy datasets overlook. Careful anonymization techniques, such as tokenization or hashing, make it possible to safely use production-like data while protecting sensitive information.

#### 4. Shift-Left in Data Testing

Traditional pipelines often defer testing to the end, when issues are harder and more expensive to fix. A shift-left approach embeds validation at earlier stages, catching problems before they propagate. Schema enforcement at ingestion ensures only correctly structured records enter the pipeline. 

Transformation logic can include automated assertions to check that business rules are applied correctly. Even at the design phase, teams can define data contracts that describe expected formats, ranges, and relationships between fields. By validating early, teams prevent “garbage in” scenarios and reduce downstream debugging effort.

#### 5. Collaborate Across Engineering, QA, and Data Teams

Data testing spans multiple technical and functional areas, making collaboration essential. Engineers ensure that infrastructure constraints and performance requirements are met. QA specialists bring structured testing practices and automation expertise. Data analysts and business stakeholders contribute domain-specific rules and expectations for accuracy. 

Without collaboration, tests may cover only part of the picture. Jointly defining validation criteria ensures that tests address both structural correctness and business relevance.

#### 6. Continuously Improve Based on Test Feedback

Data testing is not static; rules that were sufficient last year may no longer catch today’s issues as systems, datasets, and business rules evolve. Continuous improvement ensures that test suites adapt to changing realities. Teams should monitor recurring test failures and analyze whether they reflect genuine data quality problems, schema drift, or overly strict validation rules that cause false positives. 

Metrics such as coverage, defect detection rate, and time-to-fix can guide improvements to the test process. Feedback loops also include updating baselines when normal patterns shift, such as seasonal spikes in sales or introduction of new product categories. 

### Data Testing with Dagster

Dagster makes data testing a first-class part of building and operating data pipelines. The asset model provides a clear structure for where tests should live, how they should run, and how teams can enforce data quality in both development and production environments.

#### **Why Assets Improve Testability**

Assets represent the logical outputs of your data system. Each asset has a defined set of inputs, a clear computation boundary, and a stable contract for what it produces. This structure creates several advantages for testing.

<strong>1. Tests map directly to real data products<br></strong>Because assets describe individual tables, files, or model outputs, tests naturally attach to the specific artifact they validate. There is no need to reverse-engineer where a given dataset is created or which upstream code needs to be executed to reproduce it.

<strong>2. Asset definitions reduce mocking and glue code<br></strong>Assets explicitly declare their dependencies. This makes it simple to construct unit tests or integration tests because a test can run an asset in isolation with controlled inputs. There is no need to spin up entire pipeline graphs or build complex fixtures to simulate upstream behavior.

<strong>3. Manifest driven execution ensures reproducibility<br></strong>Dagster’s asset graph provides a deterministic view of how data flows through your system. Tests that run against assets always operate on a consistent execution path. This eliminates ambiguity about the order of operations that often complicates testing in traditional orchestration tools.

**4. Local development mirrors production execution<br>** Developers can run assets directly during development, using the same definitions and logic that will run in production. This makes test failures easier to reproduce, debug, and fix.

Together these elements create a testing workflow that is more predictable, simpler to maintain, and better aligned with the actual data products your organization relies on.

#### **Asset Checks for Production Data Quality**

In production, Dagster uses asset checks to enforce ongoing data quality and operational reliability. Asset checks are small, declarative validation steps attached to assets that run automatically when the asset updates.

<strong>1. Built-in guardrails for live data<br></strong>Asset checks ensure that each asset continues to meet expected quality standards long after code is deployed. These checks can validate row counts, schema structure, distributions, or custom business rules. If a check fails, Dagster surfaces the issue immediately and prevents downstream assets from consuming invalid data.

<strong>2. Continuous monitoring of critical assets<br></strong>Teams can mark certain checks as required for key assets. This makes them behave like reliability contracts. If a required check fails, the system treats it as a production incident and creates clear visibility for operators, rather than letting data quality problems spread silently.

<strong>3. Observability integrated with your data graph<br></strong>Check results appear directly in the Dagster UI alongside asset materializations. Operators can trace failures through the asset graph, understand which downstream assets are affected, and take corrective action. This creates an operational feedback loop that is more informative than isolated alerting systems.

<strong>4. Flexible execution patterns for real-world needs<br></strong>Dagster allows checks to run on every materialization, on schedules, or on demand. Some checks may validate lightweight properties during every run, while others may perform deeper scans on a daily or hourly cadence. This flexibility supports both rapid anomaly detection and cost-efficient validation of large datasets.

#### **Bringing It All Together**

The combination of assets for development-time testing and asset checks for production-time validation creates a unified approach to data quality. Assets make tests easier to write, easier to maintain, and more robust because they tie tests directly to the data products they validate. Asset checks provide ongoing enforcement and monitoring that keeps production systems safe.

With Dagster, data testing becomes a continuous, integrated part of how pipelines are built, deployed, and operated.

---

## Data Transformation in 2025: Types, Techniques, Tools & Tips

> Data transformation converts data between formats, reorganizes it, combines sources, or modifies values to meet analytical needs.

Source: https://dagster.io/learn/data-mesh

### What Is Data Transformation?

Data transformation involves converting data from one format or structure into another. This process can include reorganizing data, combining it from multiple sources, or modifying its values for analytical needs. Transformation is about making data usable by changing its format, structure, or content to align with business requirements or system specifications.

Besides restructuring, data transformation can involve enrichment by adding new data points or context, which improves analysis quality and depth. This process is key in data integration, enabling disparate data sources to communicate effectively by sharing a common format or structure, ensuring consistency across platforms and applications.

This is part of a series of articles about data mesh.

### Importance of Data Transformation

Data transformation is crucial for optimizing data usability, ensuring that raw data is converted into formats compatible with business operations, analytics, and decision-making processes. It enables businesses to standardize data from disparate sources, ensuring consistency across platforms and reducing the risk of errors in data interpretation.

By transforming data, organizations can derive actionable insights from structured and unstructured datasets, driving more accurate analytics and better decision-making. It also enhances data accessibility, improving collaboration across departments and enabling efficient data sharing and integration.

Data transformation ensures compliance with data governance policies and industry regulations. By applying processes like anonymization and encryption during transformation, businesses can protect sensitive information and meet legal requirements, such as those mandated by GDPR or HIPAA.

### The Data Transformation Process

#### 1. Data Discovery and Profiling

Data discovery involves identifying and understanding data sources. Profiling is the assessment of data quality and structure to identify anomalies or gaps. Together, they establish a foundation for transformation by ensuring data completeness and integrity. This step allows for effective planning of transformation strategies, aligning them with organizational goals and data governance standards.

Profiling helps in detecting inconsistencies and errors in data sets. These include null, duplicate, and out-of-range values which can skew analysis. By addressing these during the discovery phase, the process ensures data quality, leading to more accurate transformations.

#### 2. Data Mapping and Planning

Data mapping identifies relationships between data fields across disparate systems. It involves matching fields in source data to target schemas. This step is essential for outlining the transformation pathway and ensuring data is moved correctly. Mapping is often aided by automation tools to increase efficiency and accuracy, reducing human error in alignment and compliance checks.

Once mapping is complete, planning outlines the logistics of how data will move through the transformation process. This includes defining transformation rules and execution sequence, ensuring the process is efficient.

#### 3. Transformation Execution

In transformation execution, identified rules and mappings are applied to the data. This may involve changing data formats, structures, or both, depending on the requirements. Execution can be achieved through ETL tools, custom scripts, or transformation engines, ensuring data is processed in accordance with mapped designs and quality standards.

During execution, attention must be given to efficiency, especially when dealing with large data volumes. Parallel processing techniques and optimization algorithms can be employed to handle high-throughput and complex transformations.

#### 4. Review and Validation

Review and validation are essential after transformation execution to ensure accuracy. This involves checking transformed data against predefined criteria such as data integrity, completeness, and conformity to target formats. Validation ensures that no data is altered incorrectly during transformation, maintaining data quality standards throughout the process.

Review processes often involve both automated checks and manual inspections for edge cases. It validates that necessary transformations have occurred and identifies any adjustments that need correction.

### Types of Data Transformation

#### Data Cleaning and Normalization

Data cleaning involves identifying and correcting errors or inconsistencies in data, such as duplicates or invalid entries. Normalization organizes data into a standard format, which is crucial for ensuring compatibility across systems and enhancing data quality. Together, these processes prepare data for reliable analysis and integration, reducing noise and potential for error.

Normalization includes eliminating redundancy, separating data into logical units, and establishing coherent relationships among data sets. A well-normalized data set is more efficient to query and consumes less storage.

#### Data Integration and Consolidation

Data integration combines data from multiple sources into a unified view, reducing fragmentation and providing a more comprehensive dataset for analysis. Consolidation involves aggregating similar data sets, often requiring transformations like format conversion or field alignment to maintain coherence.

The consolidation process can involve merging duplicate entries, resolving conflicting values, and standardizing data formats. These steps ensure that integrated data appears seamless and homogeneous, despite originating from varied sources.

#### Data Aggregation and Summarization

Data aggregation is the process of compiling and summarizing data from various sources to present a cohesive view. This involves computing metrics or statistics, such as averages or totals, to provide high-level insights. Aggregation transforms raw data into summary reports that are easier to analyze.

#### Data Conversion and Format Transformation

Data conversion alters data formats to suit application or system requirements, ensuring interoperability and usability. This involves changing data types, encodings, or structures. Format transformation is critical in environments with heterogeneous systems, ensuring that data can be read and manipulated by different application layers without loss of meaning or function.

Different systems and applications often require specific data formats for processing. Conversion processes ensure that data adheres to these formats, enhancing compatibility. Automation tools are often used to facilitate this process, reducing manual intervention and increasing efficiency.

#### Data Anonymization and Masking

Data anonymization involves altering data to prevent identification of individuals, protecting privacy. This is achieved by removing or encrypting personal identifiers. Masking obscures specific data elements, crucial in environments that handle sensitive information, such as test systems or public data sharing. Both practices ensure data privacy and mitigate unauthorized access risks.

Anonymization techniques include masking, pseudonymization, and encryption to ensure data cannot be traced back to individuals. These methods maintain the utility of data for analysis without compromising privacy.

### Data Transformation Techniques

Here are some of the common techniques used to carry out data transformation:

#### Smoothing and Filtering

Smoothing techniques reduce noise and highlight significant data trends by averaging out minor fluctuations within datasets. This is particularly useful in time-series data where short-term volatility can mask underlying patterns. Filtering involves removing unwanted data to focus on relevant information for analysis, ensuring clarity and accuracy in datasets.

These techniques are essential in preparing data for precise analysis by eliminating distortions or false signals. They improve the signal-to-noise ratio, making it easier to identify consistent patterns and behaviors.

#### Discretization

Discretization transforms continuous data into discrete buckets or intervals, simplifying analysis and improving data manageability. This technique is vital in simplifying data structures, facilitating pattern recognition, and supporting classification tasks. By reducing data complexity, discretization and binning enable more straightforward interpretation of results and facilitate machine learning and statistical analysis.

#### Generalization

Generalization reduces data specificity, ensuring broader applicability by transforming detailed data into higher-level abstractions. This technique maintains essential information while removing unnecessary detail, which simplifies analysis and supports strategic decision-making. Generalization is crucial when data precision could lead to privacy issues or overfitting in models.

This process involves creating a less detailed view of data by substituting general categories for specific data points.

#### Attribute Construction

Attribute construction involves creating new variables or attributes from existing data sets to improve the performance of models. This is achieved through transformation formulas or algorithms, combining or aggregating data fields. Properly constructed attributes can reveal patterns or relationships that were not previously apparent, enriching data analysis or modeling efforts.

Through attribute construction, new insights can be drawn by synthesizing meaningful aggregates and interactions from raw fields. This step potentially augments the predictive power of machine learning models by capturing complexities in data that are otherwise missed.

#### Data Encoding and Decoding

Data encoding and decoding involve converting complex data structures into simpler formats for processing and transforming them back as needed. Encoding ensures data integrity and security by converting it into machine-readable formats or protecting it from unauthorized access. Decoding is the reverse process, needed to regain accessibility for users or applications.

These processes are integral in data communication, storage, and interoperability across platforms, encompassing techniques like base64 encoding, SSL encryption, or XML conversion. Encoding improves data reliability by enhancing error detection and compression, while decoding restores usability for users while maintaining control over data dissemination.

### Tools and Technologies for Data Transformation

#### Scripting and Programming Languages

Scripting languages like Python, R, and SQL are commonly used for data transformation tasks. They offer flexibility and a wide range of libraries and frameworks for handling data manipulation processes. Python's extensive library support makes it popular for data transformation, while SQL excels in database-related transforms and queries.

These languages allow for tailored transformation scripts that can automate complex data manipulations, enhancing productivity and efficiency.

#### ETL Platforms and Software

ETL platforms such as Talend and Apache NiFi offer solutions for carrying out data transformations. These tools often support drag-and-drop interfaces and pre-built connectors, streamlining workflows for non-technical users while providing advanced capabilities for seasoned professionals, handling tasks from data extraction to transformation efficiently.

Such platforms enhance data transformation by offering scalable solutions able to handle large-scale operations with ease. They ensure data governance and quality through integrated checks and can manage complex dependencies within workflows.

#### Data Transformation Libraries and APIs

Data transformation libraries and APIs provide developers with predefined functions and modules to facilitate data manipulation without reinventing the wheel. Libraries such as Pandas in Python or Apache Spark offer capabilities for conducting complex transformations efficiently, supporting high-performance data processing and real-time transformations.

APIs enable integration of transformation capabilities into existing applications, offering flexibility and extensibility. This is particularly useful for businesses seeking customized transformation workflows tailored to specific organizational needs.

#### Cloud-Based Data Transformation Services

Cloud-based services like AWS Glue, Google Cloud Dataflow, and Azure Data Factory offer scalable, flexible solutions for data transformation. These services support real-time data processing and integration, leveraging cloud resources to scale tasks as needed. They reduce infrastructure concerns, allowing businesses to focus on data quality and analysis.

These services provide built-in connectors for various data sources and targets, simplifying integration across platforms. The pay-as-you-go model of cloud services ensures cost-efficiency, while their security protocols protect data throughout the transformation lifecycle.

#### Data Orchestration Platforms

Data orchestration platforms play an important role in managing and automating the flow of data across various systems and processes. These platforms coordinate data movement, ensuring that data is efficiently extracted, transformed, and loaded across disparate environments. By streamlining workflows, orchestration platforms ensure that all components in the data pipeline work in sync, reducing manual intervention and minimizing errors.

Data orchestration platforms like Dagaster offer scheduling and monitoring features that allow for the automation of complex, multi-step workflows. These tools also provide error-handling capabilities and allow organizations to visualize and manage dependencies between tasks. With orchestration platforms, data transformation processes can be dynamically scaled, monitored in real-time, and adjusted based on performance or resource availability, increasing overall operational efficiency.

### Challenges in Data Transformation

#### Data Quality Issues

Data quality issues are a significant hurdle in data transformation. Inconsistent, incomplete, or erroneous data can result in inaccurate outcomes, undermining business decisions. Addressing these issues involves rigorous data cleaning, validation, and enrichment processes to ensure that transformed data is reliable and fit for purpose.

To mitigate these challenges, organizations may implement automated data checks and validation rules. These techniques preemptively address common data quality issues, reducing manual intervention and ensuring data integrity.

#### Handling Large Data Volumes

Handling large data volumes can pose technical and logistical challenges. The need for robust infrastructure and efficient algorithms is essential to manage storage, processing, and transformation without incurring significant delays or costs. Scalable solutions and parallel processing frameworks are often employed to handle these demands effectively.

Organizations utilize distributed computing environments like Snowflake or Spark to manage large datasets. These platforms enable concurrent processing and efficient resource utilization, significantly reducing transformation time.

#### Resource and Cost Constraints

Resource and cost constraints are common in data transformation projects, impacting the choice of tools and approaches. Organizations need to balance the financial and personnel resources required for data transformations against potential benefits, opting for solutions that maximize efficiency while minimizing overhead.

Scalable cloud solutions and open-source tools offer cost-effective alternatives to expensive proprietary software. Strategic investment in training and automation can further optimize resource use, ensuring teams are equipped to handle transformation tasks effectively.

#### Maintaining Data Security and Compliance

Maintaining data security and compliance during transformation is critical, especially when dealing with sensitive or regulated data. Stringent security protocols and adherence to data governance policies are necessary to protect data integrity and ensure compliance with legal standards such as GDPR or HIPAA.

Implementing encryption, access controls, and audit logs helps secure data throughout the transformation lifecycle. Regular audits and compliance checks ensure ongoing adherence to regulations, mitigating risks associated with data breaches or non-compliance.

### Best Practices for Effective Data Transformation

#### 1. Define Clear Objectives and Requirements

Defining clear objectives and requirements is essential for successful data transformation. Clear goals ensure alignment with business needs and guide decision-making throughout the transformation process. Articulating specific requirements helps avoid scope creep and ensures all stakeholders are on the same page, increasing project efficiency and success.

Detailed requirements enable better planning and risk management, providing a roadmap for implementation and evaluation. Establishing metrics for success allows for effective monitoring of transformation processes, ensuring project objectives are met.

#### 2. Use Automated Tools Where Possible

Automated tools streamline data transformation processes, reducing time and manual effort required. By leveraging tools for data mapping, validation, and quality checks, organizations can improve efficiency and consistency across transformations. Automation reduces human errors and frees up resources, allowing data teams to focus on higher-level analytical tasks.

Automation scales with data needs, enabling rapid adaptations to changing data requirements. It supports repeatability and standardization, crucial for maintaining accuracy across complex transformation operations.

#### 3. Ensure Data Quality at Each Step

Ensuring data quality at each step in transformation is vital for reliable outcomes. This involves implementing rigorous quality checks and validation processes throughout the transformation lifecycle, from initial extraction to final loading, ensuring data integrity and accuracy are maintained.

Continuous quality monitoring and corrective actions prevent contamination of datasets, reducing downstream errors in analysis. Establishing robust quality frameworks ensures data is consistently transformed to meet organizational standards.

#### 4. Maintain Documentation and Metadata

Maintaining comprehensive documentation and metadata is essential for successful data transformations. Documentation provides insights into transformation processes, while metadata describes data characteristics, ensuring transparency and auditability. This practice supports consistency and understanding, improving stakeholder collaboration in transformation projects.

Metadata management aids in tracking data lineage, relationships, and dependencies, supporting compliance and data governance. Thorough documentation facilitates troubleshooting and streamlines updates to transformation processes.

#### 5. Regularly Review and Update Transformation Processes

Regularly reviewing and updating transformation processes ensures adaptability to changes in data sources, technologies, or business requirements. Continuous assessment identifies inefficiencies or bottlenecks, enabling optimization and improvement over time, maintaining the relevance and efficiency of data transformation efforts.

Staying abreast of technological advancements and industry best practices supports strategic updates, enhancing transformation capabilities. By fostering a culture of continuous improvement, organizations can ensure data transformations remain effective and aligned with evolving business goals.

### Optimize and Orchestrate Data Transformation with Dagster

As a next-generation data orchestrator, Dagster allows teams to seamlessly define, manage, and execute complex data transformations.

Native support for partitions enables incremental workloads and reduces redundant computation, while an intelligent, declarative mechanism for triggering jobs ensures transformation workloads are run only when needed, minimizing compute costs.

Dagster features robust integrations with the most popular data transformation engines, like DBT and SDF, allowing you to quickly integrate your existing data transformation projects into your wider data platform.

It also lets you run data quality checks alongside your transformations, without the overhead of needing external tooling.

To learn more about how you can orchestrate your data transformations with Dagster, check out [our course on using Dagster and DBT together](https://dagster.io/blog).

‍

---

## DataOps in Practice: Principles, Lifecycle & Tips for Success

> DataOps is a data management methodology and set of practices that combines principles from DevOps and Agile to simplify the entire data analytics pipeline, from data preparation to reporting. It uses automation, collaboration, continuous improvement, and advanced monitoring.

Source: https://dagster.io/learn/dataops

### What Is DataOps? 

DataOps is a data management methodology and set of practices that combines principles from DevOps and Agile to simplify the entire data analytics pipeline, from data preparation to reporting. It uses automation, collaboration, continuous improvement, and advanced monitoring to accelerate the delivery of high-quality, reliable data insights to users, making data operations more efficient, agile, and valuable to the business.

Key aspects of DataOps include:

- **Agile and DevOps inspired:** DataOps borrows from Agile software development for iterative development and from DevOps for continuous delivery and automation to speed up the data analytics cycle. 
- **Automation and continuous improvement:** It leverages automation for processes like data pipeline development, testing, and deployment, fostering a culture of continuous improvement. 
- **Collaboration:** DataOps breaks down silos between data teams (engineers, scientists) and business stakeholders, promoting better integration and communication to deliver the data products that meet business needs. 
- **Quality and reliability:** By incorporating statistical process control and automated validation, DataOps focuses on ensuring [data quality](https://dagster.io/learn/data-quality) and minimizing errors throughout the data lifecycle. 
- **Speed and velocity:** The aim is to shorten the time it takes to develop and deliver new insights, allowing organizations to respond more quickly to business needs and market changes.

Benefits of DataOps include:

- **Increased business value:** Faster and more reliable data insights enable smarter, data-driven decision-making across the organization. 
- **Improved data quality:** Automated testing and monitoring significantly reduce errors and increase trust in the data. 
- **Enhanced team productivity:** By automating repetitive tasks and improving collaboration, DataOps allows data teams to focus on innovation and delivering valuable insights. 

**Reduced risk:** Automated testing and validation before deployment help prevent disruptions and data loss, ensuring more stable and reliable data pipelines.

### Why DataOps Is Important 

DataOps is critical for modern data-driven organizations because it addresses both the complexity and scale of today’s data environments. Traditional, manual approaches to managing [data pipelines](https://dagster.io/learn/data-pipeline) are too slow and fragile to meet the fast-changing needs of businesses. By automating data workflows and promoting collaboration across teams, DataOps makes it possible to deliver trusted, high-quality data more quickly and consistently.

DataOps aids in breaking down silos between data producers and consumers. This ensures that data is no longer locked within departments, but accessible to teams who need it, whether for analysis, reporting, or operational decision-making. Cross-functional collaboration not only improves the accuracy and relevance of insights but also supports a shared understanding of the data across the organization.

As data volume, variety, and velocity continue to grow, DataOps provides the structure to scale data operations without losing control. It integrates capabilities such as pipeline orchestration, data quality monitoring, and governance, which are essential for maintaining reliability, compliance, and performance. Tools that automate data extraction, transformation, and loading help manage complexity and reduce manual workload.

Incorporating security and self-service access into this framework also enhances trust and usability. Data is protected through encryption and access controls, while business users can access the data they need without waiting on IT.

### Core Principles and Aspects of DataOps 

#### Collaboration Across Teams

Effective DataOps requires collaboration between diverse teams, including data engineers, analysts, scientists, and operations. Traditional silos, where responsibilities and information are segregated, often result in misunderstandings, delays, and data quality issues. DataOps breaks down these barriers by fostering shared responsibilities, open communication, and simplified workflows. 

Cross-functional teams can quickly address data issues and adapt to changing requirements, resulting in more reliable and useful analytics outputs. This collaborative model also involves shared tooling and documentation, ensuring that all contributors have [visibility into data](https://dagster.io/guides/5-things-you-need-to-achieve-data-visibility) processes and status. 

#### Continuous Delivery for Data

Continuous delivery is a central pillar of DataOps, translating software deployment best practices into the realm of data pipelines. Instead of releasing new analytics features or data sets infrequently, DataOps supports frequent, incremental updates through automated processes. 

This accelerates time-to-value by making data and analytics available as soon as they're ready, rather than waiting for infrequent scheduled releases. Automated deployment and rollback mechanisms also reduce the risk associated with changes. Practicing continuous delivery for data means treating data products, such as dashboards, reports, and machine learning models, as evolving artifacts. Teams use version control and automated testing to validate each change.

#### Automation-First Approach

Manual interventions increase the risk of errors and slow down processes; therefore, DataOps invests heavily in automating routine and repetitive tasks. Automation covers data transformations, quality checks, deployments, and even remediation steps when data issues are detected. This ensures consistent, reproducible outcomes and frees staff to focus on higher-value work.

Automated pipelines not only reduce cycle time but also support scalability as data volumes and complexity grow. Organizations can handle more projects and data sources without a corresponding increase in headcount. Automated processes also support compliance by ensuring every action is logged and traceable.

#### Governance Embedded in Processes

Effective governance is essential for data quality, privacy, and compliance. DataOps incorporates governance controls directly into automated workflows. This means access controls, lineage tracking, data retention policies, and auditing features are built into the pipeline itself. By embedding governance, organizations can maintain oversight and accountability without adding friction to the delivery process.

This approach allows organizations to adapt to regulatory changes more swiftly and ensures policies are enforced uniformly across all data products. Automated governance reduces the chances of accidental exposure or misuse of data, and enables faster responses to governance-related incidents.

#### Observability and Transparency

By implementing robust monitoring and alerting across pipelines, teams gain visibility into every stage of data processing. This transparency helps detect anomalies or bottlenecks early, reducing downtime and enabling faster incident resolution. Comprehensive logs, metrics, and dashboards ensure everyone involved has an accurate picture of system health.

Transparency also extends to business stakeholders, who benefit from being able to trace data lineage and verify source and transformation details. Clear visibility into data processes fosters greater trust in analytics outcomes and aids in root cause analysis when issues arise.

#### Metrics-Driven Improvement

Continuous improvement is central to DataOps, anchored by quantifiable metrics that monitor pipeline performance, data quality, deployment frequency, and incident response times. These metrics provide objective insight into the effectiveness of data processes, making it easier to identify inefficiencies and prioritize improvements. 

By establishing baseline performance and tracking changes over time, organizations can make data-driven decisions about where to invest in automation or process redesign. Metrics-driven practices encourage a culture of accountability and transparency. Teams are incentivized to meet agreed-upon goals and quickly act on negative trends.

<strong><em>Learn more in our detailed guide to </em></strong>[<strong><em>data observability</em></strong>](https://dagster.io/guides/data-observability-in-2025-pillars-pros-cons-best-practices)<strong><em> </em></strong>

### DataOps vs DevOps 

DataOps and DevOps share common principles, such as automation, continuous integration, and cross-functional collaboration, but are tailored to distinct domains. 

**DevOps** focuses on software delivery, emphasizing the rapid and reliable deployment of applications through CI/CD pipelines. Its primary concerns include managing code, infrastructure as code, application releases, and incident response to ensure robust production systems. 

**DataOps** takes inspiration from DevOps but targets the unique challenges of managing data pipelines, analytics products, and complex data integrations.

A key distinction lies in the complexity and variability of data flows versus code deployment. DataOps must handle highly dynamic data schemas, varying data quality, lineage tracking, and compliance mandates for regulated data. This requires a heavier emphasis on observability, governance, automated testing, and data quality validation.

### The DataOps Lifecycle

The DataOps lifecycle typically includes the following stages.

#### 1. Data Ingestion and Integration

Data ingestion is the first step in the DataOps lifecycle, involving the collection and assimilation of data from diverse sources, including databases, APIs, streaming platforms, and external files. Integration transforms disparate data formats and structures into a unified, usable form. Automated tools and connectors simplify this process, decrease manual effort, and reduce the probability of errors or inconsistencies. 

Scalable ingestion pipelines ensure that organizations can handle increasing data volumes without sacrificing reliability or performance. Effective integration involves not only moving data but also standardizing and consolidating it, often in real-time, to support downstream analytics. DataOps emphasizes reproducibility and traceability, ensuring that every data point's origin and transformation history are captured.

#### 2. Transformation and Orchestration

In the transformation stage, raw data is cleansed, enriched, and reshaped to fit analytic or business requirements. Transformation logic may include data cleansing, enrichment, normalization, and aggregation, performed through distributed processing frameworks or ETL (extract, transform, load) tools. Orchestration tools schedule, trigger, and manage these processes, allowing teams to define dependencies and ensure steps occur in the correct order.

Orchestration also enables error handling, conditional logic, and parallel execution, which increases efficiency and resilience. DataOps places a strong focus on codifying these transformations as infrastructure-as-code or scripts checked into version control, making pipelines reproducible and auditable.

#### 3. Testing and Validation

Automated tests verify schema consistency, null values, range checks, referential integrity, and business logic validations before data is promoted downstream or delivered to users. This reduces the likelihood of propagating erroneous or incomplete data into analytics and reporting environments. Every change to the data pipeline should be accompanied by validations to maintain trust in analytics outputs.

Beyond technical validation, DataOps encourages business logic checks that align with stakeholder requirements and intended use cases. By integrating these checks automatically into CI/CD for data, teams can catch problems early in development, improving overall reliability and reducing post-production incidents.

#### 4.Deployment and Monitoring

Deployment in DataOps refers to moving data products, artifacts, or pipeline changes from development into production environments. This step should be automated wherever possible to reduce manual errors, maintain consistency, and speed up release cycles. Deployment automation supports safe testing, rollback, and auditing, enabling teams to iterate rapidly and deliver analytic updates without significant risk.

Once deployed, data pipelines and analytic products require ongoing monitoring for performance, failures, data quality, and compliance. Monitoring tools provide real-time alerts and visibility, enabling teams to detect issues quickly and intervene before they affect business outcomes.

### Key Features of DataOps Tools 

#### Orchestration and Workflow Management

DataOps tools provide orchestration capabilities that allow for scheduling, coordinating, and managing complex data workflows. This eliminates manual handoffs and ensures that tasks are executed in the correct sequence, factoring in dependencies, branching, retries, and error handling. 

Orchestration tools also provide logging and visualization, making it easier to track pipeline status and diagnose issues. Workflow management extends to defining SLAs (service-level agreements), automating triggers for data movement, and supporting parallel or conditional task execution.

#### Connectivity and Integration

Comprehensive DataOps tools offer extensive integration capabilities out of the box. They connect to a wide array of sources, such as cloud storage, on-premises databases, SaaS platforms, message queues, and streaming services. Native connectors and adapters reduce implementation effort and support rapid provisioning of new data sources. 

Integration functionality also includes transformation options, schema mapping, and support for both batch and real-time ingestion. DataOps tools often centralize configuration, making it easier to maintain and govern access across the enterprise.

#### Data Monitoring

Monitoring tools continuously track data flow, latency, and volume across pipelines, ensuring that anomalies such as missing records, delayed loads, or schema drifts are detected early. Real-time dashboards and alerting systems provide visibility into operational metrics like throughput, error rates, and resource utilization, allowing teams to take corrective action.

Effective monitoring also includes data quality metrics (completeness, consistency, timeliness, and accuracy) measured automatically during pipeline execution. These metrics feed into feedback loops that support continuous improvement and enable root cause analysis. Many DataOps platforms incorporate machine learning–based anomaly detection.

#### Lineage, Metadata and Cataloging

Effective DataOps tools automatically capture and catalog data lineage, tracing the origin, movement, and transformation of every data asset. This provides complete visibility, auditability, and enables compliance with regulations. Metadata management features allow users to annotate, tag, and govern assets centrally, making data discovery and reuse straightforward. 

Cataloging enables self-service for analysts and scientists, providing searchable inventories of available data sets, metrics, and models. Lineage diagrams and metadata reports support both technical and non-technical stakeholders in understanding data origins and flows.

#### Deployment Automation

Deployment automation is a crucial feature for mature DataOps processes. It allows automated promotion of data models, analytics products, and pipeline changes from development through staging to production. 

Version control integration, blue-green deployments, and automated rollbacks increase confidence in releases and reduce risk. Automated deployment supports frequent iterations and consistently enforces policies and best practices.

### Best Practices for Successful DataOps 

Organizations should consider these practices when implementing a DataOps approach.

#### 1. Gain Stakeholder Alignment on KPIs

Successful DataOps teams involve business stakeholders, product owners, and data consumers early in defining key performance indicators (KPIs) that reflect organizational goals and priorities. This shared understanding helps prioritize metrics such as data freshness, pipeline reliability, and time-to-insight over less relevant technical benchmarks.

Open communication and regular review sessions ensure KPIs remain aligned as business needs change or new objectives emerge. DataOps teams should integrate KPI tracking into their dashboards and workflow monitoring, providing stakeholders with visibility and timely feedback on progress.

#### 2. Automate Testing and Validation Processes

Manual testing is error-prone and unsustainable as data volume and complexity increase. DataOps teams should implement automated unit, integration, and regression tests that run as part of every pipeline execution, covering schema changes, data quality rules, business logic, and compliance checks.

Continuous testing reduces the chance of propagating errors downstream and enables faster feedback cycles. Automated validations should be updated as data models evolve, ensuring that new requirements are addressed promptly.

#### 3. Prioritize Data Quality Before Scaling

Organizations often rush to scale their data platforms or analytics outputs without first ensuring robust data quality controls. This can result in the rapid spread of inaccurate or incomplete data, magnifying operational and compliance risks. DataOps practitioners should focus initially on building strong quality gates, including comprehensive validation and automated monitoring.

Data quality should be measured, reported, and treated as a first-class metric for every pipeline and dataset. Once high-quality processes are in place and reproducible, scaling efforts such as adding new data sources or expanding analytics coverage can proceed with confidence.

#### 4. Invest in Self-Service and Collaboration

Empowering users with self-service capabilities is a key enabler of DataOps success. By providing well-documented, intuitive interfaces to data catalogs, transformation tools, and analytic resources, organizations make it easier for analysts, engineers, and scientists to work together without bottlenecking on a central data team. Self-service reduces ticket queues, enhances productivity, and accelerates innovation.

Collaboration tools enable transparent communication, shared knowledge, and real-time troubleshooting across functions. DataOps teams should invest in platforms that support commenting, asset annotation, and feedback loops.

#### 5. Embrace a “Ship and Iterate” Culture

A “ship and iterate” mindset is critical for DataOps, reflecting a willingness to deliver incremental value and rapidly refine data products. Instead of waiting for perfect solutions, teams release workable features or datasets, gather feedback, and make improvements in short cycles. This agile approach reduces time-to-value and ensures that evolving business needs are addressed in real time.

Frequent iteration also uncovers issues earlier, enabling faster remediation and learning. DataOps teams should implement robust release management, rollback, and versioning practices so they can confidently make changes without fear of breaking existing workflows.

#### 6. Monitor and Improve Cycle Times

Monitoring end-to-end cycle times, from data ingestion to analytics delivery, provides actionable insights into process efficiency and bottlenecks. DataOps teams should track how long it takes for changes, requests, or incidents to be resolved, and analyze delays at each pipeline stage. This illuminates areas for automation, policy change, or resource allocation.

Cycle time metrics also serve as performance benchmarks for continuous improvement. By regularly reviewing and adjusting processes based on these metrics, teams can sustain high delivery velocity and responsiveness to business needs.

### Powering Your DataOps Pipeline with Dagster

Dagster provides a unified framework for building, orchestrating, and monitoring data pipelines that aligns naturally with the principles of DataOps. It offers a central control plane for managing the entire data lifecycle, from ingestion through transformation to delivery. By treating data assets as first-class objects, Dagster makes it easy to understand dependencies, maintain lineage, and ensure reproducibility across teams.

Dagster supports a modular and declarative approach to pipeline design. Engineers can define assets, transformations, and schedules in code while taking advantage of a rich developer toolkit that includes local testing, type checking, and integrated quality validation. This enables teams to adopt continuous delivery practices for data, with fast iteration cycles and automated checks built into every change.

Observability is a core strength of Dagster. The platform provides detailed logs, metrics, and structured event data for every step of a pipeline, making it simple to identify issues, diagnose failures, and monitor performance. Real-time visibility ensures that data consumers can trust the freshness and quality of the data that powers their analytics and applications.

Dagster also integrates seamlessly with modern data stacks. Native support for tools such as dbt, Snowflake, BigQuery, Spark, Airbyte, and ML frameworks allows teams to orchestrate heterogeneous workloads from a single interface. These integrations reduce operational complexity and help maintain consistency across environments.

By combining orchestration, quality controls, metadata management, and collaboration in one ecosystem, Dagster enables organizations to scale their DataOps programs with confidence. Teams can focus on delivering valuable insights rather than maintaining fragile infrastructure. As data environments grow in volume and complexity, Dagster provides the reliability, transparency, and automation needed to sustain high-quality, efficient data operations.

---

## dbt Build vs Run: 6 Key Differences & Critical Best Practices

> The dbt build and dbt run commands serve different purposes in a dbt project, although dbt build encompasses the functionality of dbt run and more.

Source: https://dagster.io/learn/dbt-build-vs-run

### Understanding the dbt build and dbt run Commands 

The dbt build and dbt run commands serve different purposes in a dbt project, although dbt build encompasses the functionality of dbt run and more.

**dbt run** executes the data models by running the SQL queries defined in your project. It focuses solely on materializing your models (e.g., creating or updating tables/views in your data warehouse), which is useful during development when you are iterating on model logic and don't necessarily need to run tests with every change.

The dbt run command transforms raw data into the tables or views specified in a dbt project. It compiles and executes the SQL for all selected models, materializing them according to their configurations. It is useful for iterative model development and rapid testing of transformation logic without the overhead of running validations.

**dbt build** is a more comprehensive command that performs a sequence of actions: 

- **Runs models:** Materializes your dbt models, similar to dbt run. 
- **Runs tests:** Executes the data tests defined in your project to ensure data quality. If a test on an upstream resource fails, downstream resources that depend on it will be skipped. 
- **Runs snapshots:** Updates your dbt snapshots, which capture changes to source data over time. 
- **Runs seeds:** Loads seed data (CSV files) into your data warehouse. 

The build command is designed for production environments or when you need a holistic validation of your dbt project. It ensures data quality by incorporating testing as part of the build process, preventing downstream issues that could arise from faulty upstream data. 

**Summarizing the key differences:**

- **Scope:** dbt run is limited to model materialization, while dbt build includes models, tests, snapshots, and seeds. 
- **Customizability:** dbt run applies selections only to models, while dbt build applies them across models, tests, seeds, snapshots, and freshness checks.
- **Execution:** dbt run executes only model SQL, while dbt build runs a full pipeline including models, tests, seeds, and snapshots.
- **Data quality assurance:** dbt build prioritizes data quality by running tests before materializing dependent models, while dbt run does not inherently include testing. 
- **Supported flags and filtering:** dbt run flags are straightforward since they affect only models, while dbt build flags require more care because they impact multiple resource types and dependencies.
- **Use cases:** dbt run is often preferred for rapid iteration during development, while dbt build is recommended for production deployments and comprehensive project validation.

### dbt Build vs. dbt Run: Key Differences in Depth 

#### 1. Usage

The `dbt run` command has a simple syntax:

‍

`dbt run [flags]`

‍

By default, it runs all models in the project. You can use flags such as `--select` or `--exclude` to target specific models. For example:

‍

`dbt run --select "tag:daily”`

‍

This runs only the models with the daily tag. The command is often executed locally during development to test model logic without additional steps like testing or snapshotting.

The `dbt build` command has a broader scope but uses a similar syntax:

‍

`dbt build [flags]`

‍

It runs models, tests, snapshots, and seeds. For instance:

‍

`dbt build --select "tag:daily”`

‍

This builds all modes, snapshots, and seeds with the daily tag, as well as their associated tests. In production pipelines, `dbt build` is often preferred because it ensures both data transformation and validation are performed using a single command.

#### 2. Scope

The `dbt run` command has a narrow scope: it only builds models by compiling and running the SQL in your project. It does not handle any testing, snapshotting, or data validation tasks. This makes it suitable for workflows focused solely on transformation logic, such as local development or debugging individual models.

`dbt build` offers a broader scope. It executes models like `dbt run` but also includes schema and data tests, runs snapshots, and refreshes seed tables.. This makes `dbt build` an all-in-one command that covers both transformation and quality assurance. It's useful in production deployments where full validation of the data pipeline is required.

#### 3. Supported Flags and Filtering

Both commands support similar filtering mechanisms using `--select`, `--exclude`, `--selector`, and `--defer` flags, which define what part of the project to execute. In `dbt run`, these flags affect only models, which simplifies their usage and interpretation.

With `dbt build`, the same flags control a broader set of actions, as the command includes tests, snapshots, and seeds. For example, selecting a model with `--select` can lead to tests and snapshots being executed as part of the dependency graph. This requires more careful planning when using filtering options to avoid running unintended tasks.

In addition, `dbt build` is sensitive to model dependencies and test failures, so filters can have cascading effects. A selection that includes a parent model may also trigger builds and tests on child models or associated tests. Alternatively, failing tests on upstream models may cause downstream models to be skipped. Understanding how the DAG (directed acyclic graph) is structured is critical when using `dbt build` to fine-tune execution behavior.

#### 4. Execution

When you execute `dbt run`, the command compiles the SQL files for selected models and runs them against your target database. Each model is materialized based on its configuration: view, table, incremental, or ephemeral. There is no execution of tests, snapshots, or other validation routines. This leads to faster execution times and is particularly useful for iterative model development or targeted transformations.

`dbt build` runs a more complex dependency graph. It begins by building the selected models, seeds, and snapshots in DAG order, then automatically runs associated unit, schema and data tests. This full-stack execution process is slower but ensures that the entire pipeline meets defined data quality standards. It is optimized for completeness rather than speed.

#### 5. Data Quality Assurance

`dbt run` performs no validation: no tests are executed. This means any model outputs are unchecked, which can be risky if used in automated or production environments without further validation.

`dbt build` integrates quality assurance into the build process. After models are run, dbt automatically triggers any unit tests, schema tests (like uniqueness or not null checks), and data tests (custom SQL-based assertions). This built-in validation helps catch data quality issues before downstream users or systems are affected, making `dbt build` far more robust for production workflows.

#### 6. Use Cases

**dbt run** is best suited for:

- Development and debugging tasks. When a developer is iterating on SQL logic, changing model configurations, or validating incremental build strategies, dbt run provides fast feedback by skipping testing and validation. 
- Ad hoc tasks, such as refreshing a single model or running a small set of transformations without invoking the full pipeline.

**dbt build** is intended for production workflows where both data transformation and validation must occur together. For example:

- Ensuring models are built, tested, and validated before being promoted for downstream use. 
- Integrating dbt into CI/CD pipelines, scheduled production jobs, and environments where data quality checks are required to prevent invalid data from reaching analytics systems. 
- Running end-to-end data pipelines that combine seeds, transformations, and snapshots.

### Best Practices for Using dbt Build and dbt Run 

Follow these best practices to make effective use of the dbt build and dbt run commands in your projects. 

#### 1. Command Choice & Scoping

Efficient use of `dbt run` and `dbt build` starts with selecting the right command and narrowing execution to the parts of the project that matter:

- **Pick the command by intent:** Use `dbt run` for fast, local iteration on a few models. Use `dbt build` in CI/CD and scheduled jobs to run models + tests (+ snapshots) together.
- **Keep runs small with selection:** Always scope work: `--select model_a+`, `--exclude tag:slow`, `--selector <yaml_selector>`. Avoid project-wide builds unless required.
- **Prefer state-aware execution:** Use `--state` and selectors like `state:modified+` to run only changed models and their dependents. This shortens CI and reduces warehouse cost.
- **Use tags to shape pipelines:** Tag models (e.g., `tag:core`, `tag:slow`, `tag:hourly`) and target them in commands to create predictable, layered runs.

#### 2. Environments & Safety

Separating environments and guarding against accidental misuse prevents issues that can disrupt production systems:

- **Separate dev from prod:** Point dev runs to isolated schemas/warehouses via `target` profiles. Never develop against production datasets.
- **Use environment variables for secrets:** Keep credentials and toggles out of project code. Drive behavior (e.g., `RUN_SLOW_TESTS=false`) via project and environment variables.
- **Add runtime guards:** Implement row-count reasonableness checks and sentinel tests on critical tables to catch silent upstream issues.

#### 3. Testing & Quality Controls

Testing should focus on signals that matter, ensuring that builds fail only for meaningful reasons and prevent low-quality data from moving forward:

- **Make tests actionable:** Write minimal, high-signal tests (e.g., `unique`, `not_null`, key constraints, invariants). Avoid noisy tests that fail often without impact.
- **Fail fast in CI:** Add `--fail-fast` to surface the first error quickly. Combine with `--warn-error` to treat warnings as failures when quality is critical.
- **Control freshness checks:** Make sure to run `dbt source freshness` before `dbt run` or `dbt build` in production, since `dbt build` doesn’t check source freshness automatically.
- **Enforce contracts:** Enable `contracts` on critical models and sources to catch column shape or type drift during `dbt build`.
- **Document and surface ownership:** Populate `meta/owner` for models. Route test failures to on-call teams with clear ownership metadata.

#### 4. Incremental Models, Snapshots & DAG Hygiene

Incremental models and snapshots require careful setup, and structuring runs in stages helps maintain a clean, reliable DAG:

- **Tame incremental models:** Validate `unique_key`, `on_schema_change`, and filters in `is_incremental()` blocks. Use `--full-refresh` sparingly and never by default in production.
- **Snapshot deliberately:** Snapshots are often expensive and designed to run at controlled intervals (e.g., daily). Using dbt build in local or CI environments may unnecessarily update snapshots, polluting them with redundant versions.
- **Use defer in PR CI:** In lightweight CI, run `dbt build --select state:modified+ --defer --state <prod_artifacts>` to reference prod results instead of rebuilding the entire project.
- **Order work with buckets:** Run in stages: seeds → sources/freshness → snapshots → core models → marts → tests on marts. This makes downstream failures easier to diagnose.

#### 5. Performance, Cost & Reliability

Managing execution resources, monitoring artifacts, and handling unstable models reduces costs and makes pipelines more stable:

- **Tune concurrency:** Set --threads to match warehouse slots/queues. Over-threading can lead to slow runs or hit resource limits.
- **Log and persist artifacts:** Store `manifest.json`, `run_results.json`, and test results from every run. Use them for selectors, trend analysis, and flaky-test detection.
- **Handle flaky dependencies:** Quarantine unstable models with a tag and exclude from scheduled `dbt build` until stabilized. This prevents pipeline-wide failures.
- **Review cost hotspots:** Track model runtime and bytes scanned. Refactor or materialize high-cost models differently (e.g., table vs. view) based on usage.

### Orchestrating dbt Data Pipelines with Dagster

Dagster is an open-source data orchestration platform with first-class support for dbt. As a general-purpose orchestrator, Dagster allows you to go beyond just SQL transformations and seamlessly connects your dbt project with your wider data platform.

It offers teams a unified control plane for not only dbt assets, but also ingestion, transformation, and AI workflows. With a Python-native approach, it unifies SQL, Python, and more into a single testable and observable platform.

Best of all, you don’t have to choose between Dagster and dbt Cloud™ — start by integrating Dagster with existing dbt projects to unlock better scheduling, lineage, and observability. Learn more by heading to the docs on [Dagster’s integration with dbt and dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud).

---

## dbt Scheduler: The Basics and a Quick Tutorial

> The dbt job scheduler, included in dbt Enterprise and Enterprise+ tiers, is an orchestration tool to automate and manage the execution of dbt (data build tool) projects.

Source: https://dagster.io/learn/dbt-schedule

### What Is dbt Job Scheduler? 

The [dbt](https://dagster.io/learn/data-build-tool) job scheduler, included in dbt Enterprise and Enterprise+ tiers, is an orchestration tool to automate and manage the execution of dbt (data build tool) projects. Rather than running data transformations manually, teams use the job scheduler to automate model builds, testing, documentation generation, and deployment within their analytics workflow. By handling task scheduling, execution order, and monitoring, the scheduler ensures that [data pipelines](https://dagster.io/learn/data-pipeline) run reliably and employees are alerted to failures or inconsistencies in their models or data sources.

dbt job scheduler typically integrates with cloud-based data warehouses and can [run on dbt Cloud](https://docs.dagster.io/integrations/libraries/dbt/dbt-cloud) or other orchestrators like airflow or prefect. It supports complex scheduling options, allowing teams to set up jobs that run at specific intervals or are triggered by external events. Its centralized job management, notifications, and logging capabilities are crucial for analytics engineering teams to ensure data consistency and deliver fresh, reliable insights to business stakeholders.

### dbt Scheduler Core Concepts

Understanding the core components of the dbt job scheduler helps clarify how scheduling and execution are handled behind the scenes:

**Scheduler**

The scheduler is the engine responsible for managing job execution. It queues runs triggered by schedules or API calls, sets up temporary environments in the cloud [data platform](https://dagster.io/learn/data-platform), and manages run-related artifacts like logs.

‍

**Job**

A job is a configuration that defines what dbt commands to run, when to run them, and under what settings. Jobs are the primary way teams automate dbt tasks such as building models or running tests.

‍

**Job queue**

When a job is triggered, it enters the job queue. The scheduler continuously checks this queue, evaluating whether each job is ready to start. If so, it provisions the necessary environment and begins execution.

‍

**Run**

A run is a single execution of a job. Each time a job is triggered—either by schedule or API—it results in a distinct run.

‍

**Run slot**

Run slots determine how many jobs can run at the same time. Each job in progress consumes one slot, and teams may need more slots to reduce wait times and support concurrent workloads.

‍

**Prep time**

This is the time taken to set up the temporary environment for the run. Prep time can vary, especially during peak usage periods like the start of the hour.

‍

**Wait time**

Wait time is the delay between when a job is queued and when it actually starts, often due to limited run slots or overlapping job runs.

‍

**Over-scheduled job**

When a job's execution time exceeds its schedule interval, new runs begin to stack in the queue. If left unresolved, this can lead to a backlog and performance issues.

‍

**Deactivated job**

If a job fails 100 times in a row, it's marked as deactivated and stops running automatically. This protects system stability and signals that intervention is likely needed.

‍

**Threads**

Threads allow dbt to run parts of the DAG in parallel. The thread count defines how many steps dbt can execute simultaneously, with a default of 4 per job.

### Understanding dbt Scheduler Queue 

When a job is triggered—whether by a schedule, a completed job, an API call, or manual action—the scheduler places it into a queue before execution begins. The queued run then goes through several checks to determine whether it can start.

The first check is whether a run slot is available. If all slots are in use, the job remains in the queue, and the wait time is displayed in dbt.

The second check is whether the same job is already running. To avoid collisions in model builds, the scheduler only runs one instance of a given job at a time. If a run is already in progress, the new run will wait until the current one completes.

If both conditions are met, the scheduler begins preparing the run environment. This setup includes provisioning a Kubernetes pod, installing the correct dbt version, configuring environment variables, and loading credentials from the data platform and Git provider. The time spent here appears as *Prep time* in the UI.

**CI job queue behavior**

Continuous integration (CI) jobs are handled differently from deployment jobs. CI runs are triggered by pull requests, do not consume run slots, and can execute in parallel. Each CI run builds into its own temporary schema, allowing teammates to run checks without blocking production or waiting for other runs to finish.

**Merge job queue behavior**

Merge jobs, triggered by merged pull requests, do consume run slots and follow a stricter execution pattern. Only one run can be active at a time. If multiple runs are queued, the scheduler cancels older ones and keeps only the most recent. Any new run must wait until the active one finishes before starting.

### Tutorial: Create and Schedule Jobs in dbt 

This tutorial shows how to create and schedule jobs in dbt. Instructions are adapted from the [dbt documentation](https://docs.getdbt.com/docs/deploy/deploy-jobs).

To create and schedule a deploy job in dbt, start by ensuring the following prerequisites are met:

- You have a dbt account with a Developer seat license
- Your dbt project is connected to a cloud data platform
- You have access permissions to create, modify, or run jobs
- A deployment environment is already set up

#### Step 1: Create a Deploy Job

1. In your deployment environment, click **Create job > Deploy job**.<br><br>
2. In the **Job settings** section:<br> <br>
   - **Job name**: Enter a name, such as *Daily Build*.<br><br>
   - **Description** *(optional)*: Add details about what the job does.<br><br>
   - **Environment**: This defaults to the environment where you're creating the job.

#### Step 2: Configure Execution Settings

In the **Execution settings** section:

- **Commands**: By default, the job runs dbt build. Click **Add command** to include additional steps. Commands are run sequentially, and if any step fails, the job will fail.<br><br>
- **Generate docs on run** *(optional)*: Enable this to build dbt docs during the job run. Failure in this step won't cause the job to fail if later steps succeed.<br><br>
- **Run source freshness** *(optional)*: Runs dbt source freshness before executing the job. Like docs, its failure won’t cause the whole job to fail if other steps succeed.

#### Step 3: Set Up Job Triggers

In the **Triggers** section, you can choose how the job is triggered:

##### Option A: Run on Schedule

- **Timing**:<br> <br>
  - **Intervals**: Runs the job every *X* hours (e.g., every 2 hours).<br><br>
  - **Specific hours**: Run the job at specified UTC hours (e.g., 0,12,23 for midnight, noon, and 11 PM).<br><br>
  - **Cron schedule**: Use custom cron syntax for full control (e.g., 0 0 L \* \* for the last day of the month).<br><br>
- **Days of the week**: Specify which days the job should run (defaults to every day when using Intervals or Specific hours).

**Note**: All scheduling is based on UTC. dbt does not adjust for local time zones or daylight savings.

##### Option B: Trigger on Job Completion

To run this job after another deploy job completes:

1. Enable **Run when another job finishes**.<br><br>
2. Select the upstream **Project** and **Job**.<br><br>
3. Under **Completes on**, choose which statuses (e.g., success, error) will trigger this job.

#### Step 4: (Optional) Configure Advanced Settings

- **Environment variables**: Customize behavior using environment-specific variables.<br><br>
- **Target name**: Define a target profile (used interchangeably with environment variables).<br><br>
- **Run timeout**: Set a time limit for the job to complete.<br><br>
- **Compare changes against**:<br> <br>
  - **No deferral** (default): No state comparison.<br><br>
  - **Environment**: Compares against the last successful run in that environment using the manifest.<br><br>
  - **This Job**: Compares against the last successful run of this job.<br><br>
- **dbt version**: Inherits the environment's version by default. Avoid changing unless testing a new version.<br><br>
- **Threads**: Defaults to 4. Increase to allow more concurrent model execution during the run.

After saving, your deploy job will be scheduled or triggered based on your configuration. Each job run includes detailed logs, timing data, and status to support monitoring and debugging.

### Best Practices for dbt Scheduling 

#### Optimize concurrency Settings

Job concurrency is governed by the number of available run slots in your dbt Cloud plan. Each job execution consumes one slot. If all slots are occupied, additional jobs are queued until a slot is free. Teams should monitor queue times and increase run slots when needed to support high-throughput workflows.

For jobs running complex or large DAGs, optimize thread counts to parallelize execution within a job. The --threads argument controls the number of models dbt executes in parallel. A higher thread count can improve performance but may strain your data warehouse if it leads to excessive simultaneous queries. Monitor warehouse utilization and find a thread count that balances performance with resource limits.

Avoid scheduling multiple jobs at the same minute, especially at the top of the hour. This creates a spike in compute usage and increases the risk of overlapping runs. Instead, stagger job start times by a few minutes (e.g., 12:00, 12:05, 12:10) to spread the load more evenly across time.

#### Use Modular and Maintainable Job Definitions

Design jobs around functional groupings of models rather than trying to run the entire DAG in every job. For example, separate staging layer refreshes from data marts and reporting layers. This reduces run time and improves traceability. Smaller, modular jobs also enable better testing and faster iteration.

Each job should have a clear scope and limited number of dbt commands. Avoid chaining too many commands in a single job. For example, a deploy job might include dbt build and dbt docs generate.

Use job descriptions to document what each job does and why it exists. When team members revisit the project after months, these annotations help explain execution logic and reduce onboarding time for new contributors.

#### Leverage Version Control for Job Changes

Although dbt Cloud manages job configuration via its UI, treating job definitions as code ensures consistency and auditability. For example, use the dbt Cloud API or Terraform provider to define jobs declaratively. This allows for peer review of changes, history tracking, and rollback via version control.

When using Git-based deployment flows, avoid making changes directly in the dbt Cloud UI unless those changes are also reflected in version-controlled configurations. Discrepancies between Git and UI-defined behavior can lead to confusion and unpredictable deployments.

Standardize naming conventions for jobs and branches (e.g., deploy-prod, ci-feature, snapshot-daily) to improve clarity in logs and CI tooling.

#### Embrace Slim CI

Slim CI uses state comparison and deferral to focus validation runs on only the parts of the DAG affected by a pull request. This dramatically reduces CI run times and warehouse costs. It works by comparing the current PR’s manifest with the production manifest, identifying only the changed models and their downstream dependencies.

Enable slim CI by configuring the --defer and --state flags in your CI job, pointing them to the production environment or a stable reference job. Also, ensure that dbt\_artifacts or a comparable artifact store is enabled to track prior run states.

Slim CI is especially helpful in large projects where full DAG builds on every PR become impractical. It provides fast feedback to developers without overloading the environment or blocking other jobs.

#### Schedule Data Freshness and Snapshots Intentionally

Source freshness checks validate that upstream systems are providing timely data, but these checks can cause jobs to fail if thresholds are too strict. Schedule freshness validations to run outside peak hours and avoid coupling them with core model builds. For example, a daily 3am job can run dbt source freshness separately from deploy jobs.

Snapshots capture slowly changing dimensions by comparing current values against historical data. Because they query wide tables and write historical diffs, they can be expensive. Schedule snapshot jobs at low-traffic times (e.g., early morning) and avoid running them in parallel with large model builds to minimize contention.

Configure snapshot frequency based on how often the source data changes. Don’t snapshot hourly if data updates once per day. Use updated\_at fields and dbt’s check\_cols to optimize snapshot logic and avoid redundant processing.

### Dagster: Enterprise-Grade Alternative to dbt Schedule

Dagster is a general-purpose data orchestration platform that can be used as an alternative to dbt Cloud scheduling when [dbt is part of a broader data pipeline](https://dagster.io/learn/data-pipelines-examples). Rather than focusing only on running dbt commands on a schedule, [Dagster orchestrates end-to-end workflows](https://dagster.io/blog/orchestrating-dbt-with-dagster) that may include ingestion, dbt transformations, data quality checks, and downstream processing in a single system.

A key difference is [Dagster’s asset-based orchestration model.](https://dagster.io/platform-overview/data-orchestration) Pipelines are defined around data assets and their dependencies, allowing Dagster to schedule or trigger work based on upstream data changes, not just time. This enables more targeted recomputation, only affected downstream assets run, reducing unnecessary dbt executions compared to job-level scheduling.

[Dagster also supports event-driven execution](https://dagster.io/events/dagster-running-dagster-event-driven-pipelines) through sensors, making it possible to trigger dbt runs when new data arrives or when upstream systems complete, rather than relying solely on cron schedules. This is useful for pipelines where data availability is irregular or tightly coupled to external systems.

Dagster can be deployed as a managed service (Dagster Cloud) or self-hosted, and is commonly used alongside dbt rather than replacing it. In this setup, dbt remains responsible for SQL transformations, while [Dagster handles scheduling, dependency management, retries, and observability](https://dagster.io/blog/a-practical-guide-to-dagster-resources) across the wider data platform.

### FAQs about dbt Scheduler

#### How does the dbt job scheduler manage concurrent runs?

The scheduler manages concurrency using run slots, where each active job consumes one slot. If all available slots are occupied, additional jobs are placed in a queue. These queued tasks must wait until a slot becomes free before their temporary execution environment can be provisioned.

#### What happens when a scheduled job fails repeatedly?

If a scheduled job fails 100 times in a row, the scheduler automatically marks it as deactivated and stops it from running. This built-in protection mechanism helps maintain overall system stability. It also serves as a clear signal that manual intervention is required to resolve the underlying data or configuration issue.

#### How do continuous integration (CI) jobs differ from standard deployment jobs?

Continuous integration jobs are triggered by pull requests and can execute in parallel without consuming dedicated run slots. They build into temporary, isolated schemas rather than writing directly to production environments. This allows developers to run comprehensive checks safely without blocking production data pipelines or interfering with standard deployment jobs.

#### Why is it recommended to use version control for job definitions?

Treating job definitions as code ensures better consistency, auditability, and collaboration across data engineering teams. By using version control alongside your scheduler, organizations can seamlessly track historical changes and facilitate peer reviews. This practice also allows teams to quickly roll back to previous job configurations if unexpected issues occur during deployment.

---

## dbt with Databricks: The Basics and a Quick Tutorial

> Databricks is a cloud-based platform for large-scale data engineering, analytics, and machine learning.

Source: https://dagster.io/learn/dbt-databricks

### Why Use dbt with Databricks? 

Databricks is a cloud-based platform for large-scale data engineering, analytics, and machine learning. It is built on Apache Spark and offers a collaborative workspace where data engineers, analysts, and scientists can interact with data. 

[dbt (data build tool)](https://dagster.io/learn/data-build-tool) complements Databricks, helping manage data transformations using SQL and software engineering best practices. When combined, they create a scalable analytics stack that supports version-controlled, testable, and documented data pipelines:

- **SQL-based development**: dbt allows analysts and engineers to write transformations in pure SQL, aligning well with Databricks’ support for SQL and SQL-compatible interfaces.
- **Version control and reproducibility**: dbt integrates with Git for version control, enabling collaborative development and auditability across teams working in Databricks.
- **Automated testing and documentation**: dbt brings built-in testing and documentation tools, which improve the quality and transparency of transformations done in Databricks.
- **Environment management**: dbt enables clear separation between development, staging, and production environments, aligning with Databricks' workspace model and job orchestration features.
- **Scalable execution**: dbt models can be compiled into efficient SQL code and executed at scale using Databricks’ distributed compute engine.

**Incremental models**: dbt supports incremental processing out-of-the-box, helping optimize performance when used with large datasets in Databricks.

### Understanding the dbt-Databricks Adapter 

The` dbt-databricks` adapter is a dedicated integration that enables dbt to run efficiently on Databricks. It replaces the need for the more generic `dbt-spark` adapter when working in Databricks environments, offering deeper compatibility and better performance by leveraging Databricks-native features.

**Uses pure Python**

One of the key advantages of the adapter is its simplified setup. Unlike other spark-based adapters that require an ODBC driver, `dbt-databricks` uses pure Python APIs, making installation and configuration straightforward. Developers can get started quickly using standard Python tooling (`pip install dbt-databricks`), without extra system dependencies.

<strong><em>Learn more in our detailed guide to </em></strong>[<strong><em>dbt Python</em></strong>](https://dagster.io/guides/dbt-python-running-transformations-with-python-on-data-warehouses)<strong><em> </em></strong>

**How it works**

The adapter defaults to using the delta lake table format, which aligns with Databricks’ native storage model. This enables dbt to use Delta features such as `MERGE` for incremental materializations, improving efficiency when working with large datasets. Other formats are also available, including Parquet, CSV, JSON, and Avro, and can be selected through the model configuration.

The adapter also supports Unity Catalog, allowing users to define models using Databricks' three-level namespace (catalog, schema, and table). This improves organization, security, and access control in shared environments.

**Performance considerations**

From a performance standpoint, SQL generated by `dbt-databricks` is automatically accelerated by Databricks’ Photon engine—a vectorized query engine optimized for fast execution. This results in more efficient query processing compared to using standard spark-based adapters.

### Quick Tutorial: Using dbt with Databricks from Setup to Scheduled Models

This tutorial shows how to get started with dbt for Databricks. Instructions are adapted from the [dbt documentation](https://docs.getdbt.com/guides/databricks?step=1).

#### 1. Set Up a Databricks Workspace

Start by creating a Databricks account or logging into an existing one. Fill out the registration form and verify your email address. After verification, select your preferred cloud provider—AWS, Azure, or GCP. For this guide, AWS is used, but the process is similar across platforms. **Do not select the Community Edition**, as it lacks the necessary compute capabilities.

You’ll be directed to the setup page where you can choose a trial plan (Premium by default, or Enterprise if needed). Provide a name for your workspace and select an AWS region. Then click **Start Quickstart**. This triggers an AWS CloudFormation stack deployment.

If you encounter a session timeout or error, return to the Databricks console and manually create the workspace via the UI. When prompted, enter your Databricks password under **Databricks Account Credentials**, accept the terms, and click **Create stack**. Once the stack is created, return to the Databricks tab and open your new workspace.

#### 2. Load Source Data into Databricks

To begin working with data, switch to the SQL interface, select SQL Editor from the left hand side menu. In order to select **SQL Warehouses** browse down the left sidebar under the SQL heading. Start the default **Starter Warehouse**, which may take a few minutes to initialize.

Next, upload the sample data files:

- [`jaffle_shop_customers.csv`](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/jaffle_shop_customers.csv)
- [`jaffle_shop_orders.csv`](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/jaffle_shop_orders.csv)
- [`stripe_payments.csv`](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/stripe_payments.csv)

Click on **+New button**  and you will be redirected to the asset upload page. Please proceed to upload` jaffle_shop_customers.csv`. Set the table properties:

- Catalog: `hive_metastore`
- Database: `default`
- Table name: `jaffle_shop_customers`

Confirm column types:

- `id`: bigint
- `first_name`: string
- `last_name`: string

Click **Create** once the schema is validated. Repeat the upload and configuration steps for the other two files:` jaffle_shop_orders.csv` and `stripe_payments.csv`.

Once all tables are loaded, verify them by running SQL queries in the editor:

‍

`select * from default.jaffle_shop_customers;`

`select * from default.jaffle_shop_orders;`

`select * from default.stripe_payments;`

‍

To ensure access across users working in the workspace, execute:

‍

`grant all privileges on schema default to <DB-USER>;`

‍

#### 3. Connect dbt to Databricks

There are two main ways to connect dbt Cloud to Databricks: manually or using Marketplace. For simplicity and quicker setup, use the Marketplace route:

1. In the Databricks sidebar, click **Marketplace**
2. Search for the **dbt** tile.
3. You will find a product called dbt Cloud. Click on it and it will take you to the integration page. Click the ‘Connect’ button to start the integration
4. Choose a catalog (e.g., `hive_metastore`) and click **Next**.
5. Select your SQL warehouse. If one isn't available, create a new warehouse and return to this step.
6. Choose a schema (such as `default`) and click **Add**.

Partner Connect creates a Databricks service principal (`DBT_CLOUD_USER`) and access token. It also assigns necessary privileges for schema usage and SQL warehouse access. After configuration, click **Connect to dbt**. This opens dbt Cloud in a new browser tab where you can set up your account.

Next, in dbt Cloud Studio, initialize your project:

1. Click **Start developing in the Studio IDE**. This may take a few minutes.
2. Click **Initialize dbt project** to scaffold your directory with starter files.
3. Make an initial commit (`initial commit`) to your managed Git repo.
4. Create a new file with the query:

‍

`select * from default.jaffle_shop_customers;`

‍

Save the file and run `dbt run` in the terminal to verify the setup. You should see a successful run output.

#### 4. Build your First Model

Create a new branch (`add-customers-model`) using the **Version Control** tab. Then, in the models directory:

1. Click **Create file**, name it `customers.sql`, and paste the model query that joins customers and orders to calculate:

- `first_order_date`
- `most_recent_order_date`
- `number_of_orders`

2. Save the file.
3. In the terminal, run `dbt run`. The output should show model(s) (including intermediate steps) were built successfully.

#### 5. Control Materializations

By default, dbt materializes models as **views**. To change this to **tables**:

1. Open `dbt_project.yml`.
2. Update the configuration:

‍

`name: 'jaffle_shop'`

`‍`

`models:`

`  jaffle_shop:`

`+materialized: table`

`example:`

`  +materialized: view`

‍

1. Save and run `dbt run`.

‍

To override this for a specific model, add the following to the top of a `.sql` file:

‍

`{{ config(`

`    materialized = 'table'`

`) }}`

‍

After making changes, run `dbt run --full-refresh` to reapply the new materialization strategy.

#### 6. Add Tests to Validate Your Data

Create a new file, `models/schema.yml`, with the following content:

‍

`version: 2`

`‍`

`models:`

`  - name: customers`

`    columns:`

`      - name: customer_id`

`        tests:`

`          - unique`

`          - not_null`

‍

You can include tests like `accepted_values` for status and relationships for foreign key checks. To execute tests please run the following command:

‍

`dbt test`

‍

All tests should pass. Failing tests will return the number of invalid records.

#### 7. Commit, Merge, and Deploy Your changes

If you edited the protected main branch, dbt will prompt you to create a new one. Enter a commit message like "Add customers model, tests, docs", commit your changes, and merge the branch into main.

If you're already on a new branch, go to **Version Control**, commit your changes, and merge.

Use the **Deploy** section in dbt Cloud to schedule production builds.

1. Go to **Environments** and create a new one named `Production`.
2. Choose the latest dbt version and assign your deployment target schema.
3. Save the environment.

Then, go to **Jobs**:

1. Click **Create one**, name it (e.g., "Production run"), and select your new environment.
2. Under **Execution Settings**, add `dbt build` as a command.
3. Enable **Generate docs on run**.
4. Save and click **Run now**.

Monitor the job progress in **Run history**. Once complete, view your project documentation to confirm it reflects the deployed state.

You've now completed the full cycle of building, testing, and deploying a dbt project on Databricks using real data and transformations.

### Best Practices for Using dbt with Databricks

Here are some useful practices to consider when working with Databricks and dbt.

#### 1. Leverage Unity Catalog for Governance

Unity Catalog provides centralized data governance across Databricks workspaces. When used with dbt, it allows models to be defined with full three-level namespaces (catalog.schema.table), supporting clear data organization and consistent access control.

Use Unity Catalog to enforce fine-grained permissions on schemas and tables, manage lineage, and standardize naming conventions. Ensure that your dbt profiles and project configurations specify the correct catalog and schema to take advantage of this structure.

#### 2. Leverage Streaming Tables and Materialized Views

Databricks supports streaming tables and materialized views, which can simplify near-real-time data pipelines. While dbt doesn't natively support streaming semantics, you can integrate streaming data by creating external tables or views over streaming sources (e.g., auto loader or structured streaming outputs).

Materialized views can be managed outside of dbt but referenced within models. For use cases that require freshness and low-latency access, consider building and refreshing these views using Databricks SQL tasks and orchestrating them alongside dbt jobs.

#### 3. Optimize Delta Tables via Post-Hooks

Databricks Delta tables benefit from regular maintenance such as `OPTIMIZE`, `VACUUM`, and `ANALYZE` commands. In dbt, these can be automated using post-hook configurations.

For example:

‍

`models:`

`  my_project:`

`+post-hook:`

`- "OPTIMIZE {{ this }}"`

‍

Apply these hooks selectively to large or frequently queried tables to improve query performance and maintain healthy storage conditions.

#### 4. Use Incremental Models for Efficient Data Processing

Incremental models in dbt reduce compute costs by processing only new or changed data. This is especially important when working with large datasets in Databricks.

Leverage dbt’s `is_incremental()` function within models to define logic that handles inserts, updates, or merges. Use delta lake’s ACID capabilities and the `MERGE` operation to ensure safe, performant updates. Align your partitioning strategy with the most common query filters to improve read performance on these models.

#### 5. Use the databricks\_copy\_into Macro for Data Ingestion 

For loading external data into delta tables, use the `databricks_copy_into` macro provided in the `dbt-databricks` package. This macro wraps the `COPY INTO` SQL command, enabling efficient ingestion from cloud object stores (e.g., S3, ADLS, GCS).

The macro supports parameters for file format, schema inference, and incremental loads. It is particularly useful for loading semi-structured data like JSON or CSV, and for automating ingestion pipelines as part of dbt runs.

Example usage:

‍

`{{ databricks_copy_into(`

`table_name='raw_data',`

`path='s3://my-bucket/data/',`

`file_format='CSV',`

`format_options={'header': 'true'}`

`) }}`

‍

Use this macro in staging models or seed layers to bootstrap ingestion processes directly from external sources.

### Orchestrating Data Pipelines with dbt, Databricks, and Dagster

When teams combine dbt, Databricks, and Dagster, they gain an end-to-end data platform that is reliable, observable, and suitable for modern analytics workflows. Each tool plays a clear role in the overall architecture, and together they deliver strong guarantees around quality, governance, and operational control.

#### Why Use Dagster with dbt and Databricks

Dagster is a data orchestrator that manages the execution and coordination of work across your data systems. It introduces a shared definition of assets, strong observability features, and precise dependency management. When used alongside dbt and Databricks, Dagster becomes the control plane for your analytics and ML pipelines.

**Key advantages include:**

**Unified asset graph:** Dagster represents dbt models, Databricks SQL tasks, external data sources, and notebooks as assets. This allows teams to understand dependencies, view lineage, and track the impact of upstream or downstream changes from a single interface.

**Strong dbt integration:** Dagster can parse a dbt project and generate software-defined assets that correspond to dbt models and tests. This gives structure to dbt runs, improves modularity, and provides detailed runtime visibility in the Dagster UI.

**Native Databricks integration:** Dagster connects directly to Databricks clusters, jobs, and SQL warehouses. It can trigger Databricks tasks, coordinate notebook execution, and orchestrate SQL workloads that run on Delta Lake.

**Flexible automation and scheduling:** Dagster supports both time-based schedules and event-driven sensors. This enables everything from nightly dbt transformations to pipelines triggered by new files or updated Delta tables.

**Observability and governance:** Dagster records metadata for every asset, including materialization history, data quality outcomes, and execution logs. This improves operational insight across the combined Databricks and dbt ecosystem.

#### How They Work Together

A typical flow includes the following steps:

1. Data is ingested into Databricks using structured streaming or external ingestion services.
2. Dagster triggers dbt jobs that build staging, intermediate, and mart models using Delta Lake.
3. Downstream analytics or machine learning tasks in Databricks run after the relevant dbt models have been updated.
4. dbt tests run within Dagster, and their results determine whether downstream steps proceed.
5. Dagster schedules, monitors, and retries production runs, ensuring consistency and reliability across environments.

This structure creates a unified, well-governed, and fully observable pipeline that scales across teams and workloads.

---

## ETL (Extract Transform Load) in 2025: Process, Tooling and Practice

> ETL stands for extract, transform, load, and represents a process used to consolidate data from various sources into a unified data warehouse.

Source: https://dagster.io/learn/etl

### What Is ETL?

ETL stands for extract, transform, load, and represents a process used to consolidate data from various sources into a unified data repository:

- **The Extract phase** involves retrieving raw data from databases, APIs, and other data sources.
- **The Transform phase** cleans and prepares the data into a structured format, ensuring it is coherent and usable for analysis.
- **The Load phase** involves importing the refined data into a target system, such as a database, data warehouse, or data lake.

ETL processes have been foundational in data warehousing for decades, since they automate the integration of disparate data sources. They facilitate consistent data flow, enabling organizations to derive insights from diverse datasets. Through ETL, businesses can ensure the accuracy and accessibility of critical data, providing a framework for data analytics and decision-making.

### Importance of ETL in Data Integration

ETL processes are vital for data integration as they provide a structured approach to managing and analyzing complex data environments. They enable businesses to merge data from multiple sources into a single repository, ensuring easy access and analysis. This integration is crucial for generating business insights and improving strategic planning.

By automating the data handling process, ETL reduces the time and effort required to manage and analyze data. It ensures consistency and reliability by transforming data into a standardized format, which is critical for maintaining data integrity across systems. While new techniques have emerged, most notably Extract Load Transform (ELT), organizations continue to rely on structured data analytics for decision-making, so ETL remains an important component of data engineering.

### Evolution of ETL

#### Traditional ETL Processes

Traditional ETL processes were rigid, often requiring significant manual intervention and heavy infrastructure. These systems were initially designed for batch processing, which meant data was extracted, transformed, and loaded in bulk during scheduled intervals. While effective for periodic data needs, traditional ETL lacked the agility to handle real-time data updates and dynamic environments.

The limitations of legacy ETL systems prompted the need for innovation. As data volumes grew, these traditional methods struggled to scale, and their labor-intensive nature slowed down data processes. Organizations found it challenging to quickly adapt their data systems to changing business requirements.

#### Modern ETL and Cloud-Based Solutions

Modern ETL approaches have benefited from cloud technologies, addressing the limitations of traditional systems. Cloud-based ETL solutions offer flexibility, scalability, and real-time capabilities that were previously difficult to attain. By leveraging the cloud, organizations can dynamically scale resources to handle varying data needs while reducing infrastructure costs.

These contemporary ETL tools integrate with additional data sources, including IoT devices and social media, expanding the scope of ETL. They support real-time data processing, allowing for immediate insights and quicker reaction times. The evolution to cloud-based ETL platforms has facilitated innovation and business agility.

### Understanding the ETL Process

Let’s review the ETL process in more depth. We’ll also touch on modern use cases like extraction from NoSQL databases and loading into data lakes, which is not part of the traditional ETL repertoire, but is gradually becoming a common pattern in ETL processes.

#### Data Extraction

##### Types of Data Sources

Data extraction involves retrieving data from various sources, each offering unique structures and formats. Common data sources include relational databases, such as SQL servers, which store structured data in tables. Non-relational databases (also known as NoSQL databases), cater to unstructured data via collections or documents, providing flexibility for varied data types.

Cloud storage platforms, like AWS S3, are rapidly becoming essential data sources due to their scalability and accessibility. API endpoints are crucial for real-time data retrieval, facilitating connections between diverse applications and systems. Understanding data source diversity is vital for effective extraction and integration.

##### Extraction Methods

Extracting data effectively requires selecting the appropriate method for the source and the business need. Full extraction involves retrieving the entire dataset, suitable for initially populating the data warehouse. Incremental extraction, or delta extraction, captures only changes since the last extraction, minimizing data transfer load and speeding up the ETL process.

Real-time extraction is increasingly important in dynamic environments, enabling immediate access to data as it changes. This is achieved through webhooks or streaming platforms like Kafka. Each extraction method's suitability depends on the data's velocity, volume, and the specific analytical needs it serves.

#### Data Transformation

##### Data Cleaning

Data cleaning removes inaccuracies and inconsistencies to ensure high-quality, reliable data. This process addresses missing values, duplicate records, and incorrect entries, often caused by human error or faulty data collection methods. Cleaning is crucial for maintaining data integrity and ensuring accurate analysis.

Effective cleaning relies on sophisticated algorithms and tools capable of identifying anomalies and standardizing data formats. Automated cleaning reduces manual workload and enhances data processing speed.

##### Data Enrichment

Data enrichment enhances raw data by integrating additional context from external sources, increasing its value and potential for generating insights. This could involve appending geographic information to an address or social sentiments to user profiles. Enrichment enables more comprehensive analysis by adding depth to existing data.

The process leverages third-party data providers or open data sources to supplement internal datasets. Automated enrichment tools integrate with existing systems, updating datasets with relevant, timely information.

##### Data Normalization

Data normalization standardizes data formats, ensuring consistency across datasets. This involves adjusting values to a common scale, crucial when working with diverse units or measurement systems. Normalization enhances compatibility between datasets, simplifying data integration processes and improving analysis accuracy.

Normalization often involves complex transformations, such as converting currencies or standardizing date formats. By applying these transformations uniformly, organizations can integrate multiple data sources. Consistency in data preparation reduces errors in subsequent analyses, yielding more reliable insights.

##### Data Aggregation

Data aggregation summarizes or combines granular data pieces, facilitating easier analysis. This step involves summarizing or grouping data points to provide a high-level view of trends or patterns. Aggregation is essential for reducing data complexity and focusing on actionable insights rather than individual data points.

Advanced algorithms can automate aggregation processes, enabling dynamic reporting and visualization. By presenting data in aggregated forms, such as totals or averages, organizations can quickly discern overarching trends, which is particularly useful in dashboards and executive reports.

#### Data Loading

##### Loading into Data Warehouses

Loading data into data warehouses involves transferring processed data to centralized storage for analysis. Warehouses provide structured environments optimized for querying and reporting. This process typically includes scheduling batch jobs that ensure data consistency and integrity during the transfer.

Modern cloud-based data warehouses, such as Snowflake, Amazon Redshift or Google BigQuery, support complex queries and data analytics at scale. The loading process must be efficiently managed to avoid bottlenecks, ensuring timely data availability for business intelligence tools. This centralization supports unified data views across the organization.

##### Loading into Data Lakes

Data lakes offer a more flexible approach by storing raw or semi-processed data, accommodating both structured and unstructured formats. They are ideal for handling varied data sources and supporting exploratory analysis. Unlike warehouses, data lakes allow data scientists the freedom to experiment with diverse data sets.

Tools like Hadoop and Azure Data Lake make it easier to manage large data volumes without strict schemas. Loading into data lakes typically involves integration with data ingestion pipelines that are scalable and resilient. This model supports agile data exploration, crucial for innovation and developing new business insights.

Related content: Read our guide to ETL tools

### ETL vs. ELT

ETL (extract, transform, load) and ELT (extract, load, transform) are both data integration processes, but they differ in workflow and use cases.

**ETL** transforms data before loading it into a storage system, making it ideal for structured data warehouses. This approach prioritizes data quality and consistency before integration.

**ELT** loads raw data directly into a storage system, typically cloud-based, before transforming it. This method leverages the storage system's processing power, making it suitable for handling large volumes of unstructured data. Copying the data in its raw form provides the flexibility of running subsequent analyses that were not planned when the pipeline was run. This is in contrast to ETL pipelines, which transform the data at the beginning, and might not enable unplanned ad-hoc analyses.

Choosing between ETL and ELT depends on the data environment and specific business needs:

- **ETL is preferable** when dealing with highly structured data that requires rigorous validation and transformation before analysis. It is ideal for compliance-sensitive industries where data integrity is paramount.
- **ELT is suited** for scenarios involving massive datasets where rapid data loading and high scalability are crucial. This approach is beneficial in agile environments that prioritize speed over initial data formatting. Organizations leveraging cloud data platforms often opt for ELT to capitalize on scalable, on-demand processing capabilities.

### Challenges in ETL Processes

ETL was traditionally a very complex and labor intensive process. Over the years, it has become more streamlined with dedicated tooling and automation, but still involves several challenges:

#### Data Quality Issues

Ensuring data quality is a perennial challenge in ETL processes. Inaccuracies can arise from diverse data sources, leading to inconsistencies and errors. Data must be validated rigorously to maintain reliable outcomes in analysis, requiring sophisticated cleaning and validation routines. Poor quality data may distort analysis, resulting in misguided business decisions.

Implementing quality assurance measures and leveraging machine learning for anomaly detection can help mitigate these issues. Continuous monitoring and iterative refinement of ETL pipelines ensure data integrity is maintained over time. Addressing data quality upfront can significantly reduce downstream issues.

#### Scalability Challenges

Scalability is critical as data volumes and variety continue to rise. Traditional ETL processes often struggle with scaling, resulting in performance bottlenecks. Modern ETL solutions must accommodate significant growth in data without degradation in speed or efficiency, requiring scalable architectures and elastic resources.

Utilizing cloud-based platforms with scalable resources helps overcome these limitations. Implementing parallel processing and distributed systems enhances ETL throughput, ensuring timely data availability. Ensuring pipelines can dynamically adjust to varying data loads without manual reconfiguration is essential for maintaining efficiency in rapidly evolving environments.

#### Handling Real-Time Data

Handling real-time data presents unique challenges due to the constant inflow and time-sensitive nature of data. Traditional ETL processes, designed for batch processing, struggle with the demands of real-time environments. Adopting streaming technologies, such as Apache Kafka or Amazon Kinesis, helps manage continuous data streams effectively.

Real-time ETL requires low-latency processing and the ability to integrate and transform data on the fly. This enables instantaneous insights and decision-making, critical in industries where immediate actions are necessary, such as finance or e-commerce. Effective real-time ETL solutions must balance speed with data accuracy and consistency.

### Key Features of ETL Tools

ETL tools play a critical role in automating and streamlining the process of extracting, transforming, and loading data. These tools offer a range of features to handle large volumes of data efficiently, ensuring data quality, consistency, and security throughout the pipeline:

- **Data connectivity:** ETL tools offer broad connectivity to various data sources, including databases, cloud platforms, and APIs. This flexibility allows organizations to integrate data from diverse environments such as relational databases (e.g., MySQL, PostgreSQL), non-relational systems (e.g., MongoDB), cloud storage (e.g., AWS S3), and real-time data streams (e.g., Kafka). Robust connectivity ensures seamless data extraction from multiple sources, enhancing the overall integration process.
- **Scalability:** Modern ETL tools handle increasing data volumes without degrading performance. Cloud-based ETL solutions, for instance, can dynamically allocate resources based on demand, ensuring the system scales effectively as data grows. This scalability is crucial for businesses dealing with big data or operating in fast-moving environments, where data volumes and processing needs can rapidly increase.
- **Automation and scheduling:** ETL tools typically include automation capabilities to streamline data workflows. Scheduling features allow tasks like data extraction, transformation, and loading to run automatically at predefined intervals, reducing manual intervention. Automation ensures consistent and timely data processing, essential for maintaining up-to-date analytics and reports.
- **Error handling and monitoring:** Built-in logging and alerting mechanisms notify users of failures or anomalies in the ETL pipeline, enabling quick intervention. Advanced tools also provide detailed error reports, helping troubleshoot issues and maintain pipeline integrity.
- **Data transformation capabilities:** Effective ETL tools offer transformation features, such as data cleaning, aggregation, normalization, and enrichment. These tools often support complex business rules and advanced data processing logic, allowing users to modify data to meet their specific requirements. Transformation flexibility ensures that data is prepared in a format suited for analysis and reporting.
- **Security and compliance:** Many ETL tools include security features such as encryption, role-based access control (RBAC), and compliance with regulations like GDPR or HIPAA. These features ensure that sensitive data is protected during extraction, transformation, and loading, adhering to organizational and legal requirements.

### Best Practices for ETL Implementation

#### 1. Define Clear Objectives and Requirements

Defining clear objectives and requirements is crucial for successful ETL implementation. A thorough understanding of business goals and data needs guides the design of efficient ETL processes. This involves stakeholder collaboration to ensure the ETL pipeline aligns with strategic objectives and fulfills specific data processing requirements.

Documenting the scope, constraints, and expected outcomes of the ETL process facilitates better planning and execution. Clarity in objectives helps prioritize tasks and allocate resources effectively, reducing development time and increasing the likelihood of success. Regular reviews and adjustments ensure the ETL process continues to meet evolving business demands.

#### 2. Ensure Data Quality and Consistency

Maintaining data quality and consistency throughout the ETL process is essential for reliable analysis. Implementing data validation and cleaning routines ensures data integrity and accuracy. Standards for data formatting and normalization should be established early, promoting uniformity across integrated datasets.

Automated tools play a crucial role in enforcing quality controls, reducing the risk of human error. Consistent monitoring and auditing of data flows help detect anomalies and facilitate timely interventions. By prioritizing data quality, organizations can maximize their confidence in analytical results, supporting informed decision-making.

#### 3. Automate ETL Workflows

Automating ETL workflows enhances efficiency and reliability. Automation reduces manual interventions, minimizing errors and speeding up data handling processes. Scheduling tools can automate routine tasks, such as data extraction and loading, ensuring continuous operation without human involvement.

Orchestration platforms, like Dagster, help manage dependencies and streamline complex workflows. Automation not only improves efficiency but also provides greater consistency and repeatability in data processing. By automating ETL workflows, organizations can respond more quickly to changing data requirements and ensure timely availability of insights.

#### 4. Implement Robust Error Handling and Logging

Implementing robust error handling and logging mechanisms is critical for maintaining ETL pipeline integrity. Detailed logging provides insight into data processing activities, enabling quick identification and resolution of issues. Effective error handling ensures that data anomalies are captured and addressed without disrupting the entire process.

Monitoring tools can notify administrators of pipeline disruptions in real-time, facilitating prompt corrective actions. Comprehensive error logs assist in diagnosing persistent issues, supporting continuous improvement in ETL operations. These measures are vital for building resilient data architectures capable of withstanding unexpected challenges.

#### 5. Plan for Scalability and Future Growth

Planning for scalability and future growth ensures that ETL pipelines can accommodate expanding data environments. As data volumes increase, ETL systems must adapt without compromising performance. Scalable architectures enable the integration of additional data sources and increased throughput.

Leveraging cloud-based solutions with elastic resources supports dynamic scaling, meeting fluctuating data demands. Monitoring growth trends and forecasting future needs can inform proactive capacity planning. By anticipating future requirements, organizations can build resilient ETL solutions that sustain long-term success and data-driven innovation.\`\`\`

### Build Your ETL Pipeline with Dagster

As a data-centric data orchestrator, Dagster allows data teams to execute their ETL pipelines as a cohesive part of their wider data platform, enabling tighter integration between data ingestion and downstream products that depend on this data.

With a built-in toolkit for data ingestion thanks to our [Embedded ELT](https://docs.dagster.io/integrations/libraries/embedded-elt) feature and [integrations with leading providers like Fivetran](https://docs.dagster.io/integrations/libraries/fivetran), Dagster allows you to orchestrate your ETL workloads on your platform of choice.

Other features like [Dagster Pipes](https://docs.dagster.io/integrations/external-pipelines) and [Asset Checks](https://docs.dagster.io/guides/test/asset-checks) also help streamline some of the most challenging aspects of running ETL pipelines at scale, like setting up parallel processing on remote, distributed infrastructure and enforcing data quality checks at the point of ingestion.

To learn more about building ETL pipelines with Dagster, see this rundown of ELT Options in Dagster.

‍

---

## ML Pipelines: 5 Components and 5 Critical Best Practices

> A machine learning pipeline is a systematic process that automates the workflow for building machine learning models.

Source: https://dagster.io/learn/ml

### What Is a Machine Learning Pipeline?

A machine learning pipeline is a systematic process that automates the workflow for building machine learning models. It encompasses a series of computational steps that convert raw data into a deployable machine learning model. These steps include data collection, preprocessing, feature engineering, model training, and evaluation. By structuring these tasks in a pipeline, developers can manage the production and maintenance of data science projects.

Pipelines ensure consistency in process execution and are crucial in managing large-scale machine learning projects. They provide a modular structure where components can be reused, simplifying updates and enhancements. Additionally, pipelines help mitigate errors by incorporating validation and logging mechanisms at various stages. This modular approach allows for easier integration with other systems, boosting collaboration across teams.

This is part of a series of articles about MLOps pipelines

### Importance of Machine Learning Pipelines

[Machine learning pipelines](https://dagster.io/solutions/ai) enable the integration of machine learning models into production environments. This integration ensures that the models can scale, handling real-world data and use cases. Without pipelines, the transition from data science experimentation to production deployment can be laborious and error-prone. Pipelines also help automate data workflows and monitor model performance post-deployment.

Another benefit is improving the reproducibility and traceability of machine learning projects. By defining each processing step explicitly, pipelines make it easier to track changes and evaluate their impacts on model performance. This capability is essential for auditing purposes and for teams aiming to refine models over time. Using pipelines also enables teams to consistently implement best practices, ensuring a high standard of model reliability and accountability.

### Key Components of a Machine Learning Pipeline

#### 1. Data Collection and Ingestion

Data collection and ingestion involve gathering raw data from various sources and getting it ready for processing. This step requires connecting to databases, APIs, or potentially scraping data from web sources. Ingestion also entails transforming data into a consistent format to ensure compatibility with downstream processes. Automated systems can handle this task, ensuring that new data can be continuously fed into the pipeline with minimal human intervention.

Managing this data flow is crucial as data quality and consistency impact all subsequent steps in the pipeline. Effective data ingestion strategies account for data validation, cleaning, and initial exploratory analysis, which help identify anomalies early on. This step forms the foundation for building machine learning models as any discrepancies here can cascade through the pipeline, affecting model accuracy.

#### 2. Data Preprocessing and Transformation

Data preprocessing and transformation entail cleaning and organizing raw data, making it suitable for model training. This stage includes handling missing values, removing duplicates, and transforming categorical variables into suitable numerical formats. Scaling and normalization processes are also common to ensure data consistency. These steps are critical because they enhance the data's quality, directly influencing model outcomes.

Transformation often requires domain knowledge to ensure that the modifications align with practical implications. Pipelines automate these repetitive tasks, ensuring they are performed consistently across datasets. This automation helps maintain a standard preprocessing approach across projects, reducing the risk of human error. Properly preprocessed data contributes to faster training and more accurate models.

#### 3. Feature Engineering

Feature engineering is the process of selecting, modifying, or creating features from raw data that will enhance the predictive performance of a model. This step can significantly influence a model's ability to glean insights from data. Feature selection involves choosing the most relevant attributes, while transformation might incorporate domain insights to derive new features that capture underlying patterns in the data.

Effective feature engineering requires a thorough understanding of the data and its context. Pipelines facilitate experimentation with different feature sets, enabling automated testing to identify the most impactful features. This flexibility is invaluable in continuously refining models. Automated feature engineering tools can expedite this process, allowing data scientists to focus on interpreting results rather than tweaking features manually.

#### 4. Model Training

Model training involves creating a machine learning model by feeding it with cleaned data to learn underlying patterns. This step requires selecting appropriate algorithms that can generalize well on unseen data. Training involves adjusting model parameters to minimize errors in predictions, proceeding iteratively to optimize model performance. This process is computationally intensive, often necessitating efficient use of computational resources.

Pipelines allow integration of various tools and frameworks to facilitate training. They enable tracking different models and hyperparameters, and utilizing techniques like k-fold cross-validation to ensure model robustness. Automating training within pipelines speeds up the experimentation phase, allowing rapid iteration and comparison of different approaches to identify the most effective model for a particular task.

#### 5. Model Evaluation

Model evaluation uses validation data to assess the model's performance after training. Commonly used metrics include accuracy, precision, recall, and F1-score, which measure how well the model has captured patterns from the training data. Evaluation helps in identifying overfitting or underfitting issues, prompting further tuning or re-evaluation of the model and its parameters.

A structured evaluation process in a pipeline ensures consistent standards for assessing model performance. Tools within the pipeline automatically generate reports, enabling easy comparison between models. By standardizing evaluation metrics, pipelines facilitate an objective framework for selecting the most suitable model, ensuring that performance improvements are quantifiable and justifiable in a production context.

#### 6. Model Deployment

Model deployment involves integrating the machine learning model into a real-world environment where it can make predictions on new data. This step converts the trained model into a production-ready format, embedding it within larger systems or APIs to facilitate usage. Deployment needs to account for scalability, ensuring that the model can handle varying loads and maintain performance.

Pipelines automate the deployment process, ensuring smooth transitions from development to production environments. They incorporate monitoring and logging, alerting teams to issues in real-time. By standardizing deployment procedures, pipelines reduce the risk of errors, ensuring that models remain operational. Automated rollback mechanisms allow for quick restoration to previous states if new deployments encounter issues.

### How Machine Learning Pipelines Work: Sequential vs. Parallel

There are two main paradigms for running machine learning pipelines:

#### Sequential Processing

Sequential processing in machine learning pipelines involves executing tasks in a linear order, one after another. Each stage depends on the output of the previous one, creating a straightforward, predictable flow. This approach is intuitive and easier to debug since each task is completed before the next one begins. It suits smaller data processing workflows where dependencies between tasks are clear and manageable.

However, sequential processing can become a bottleneck when dealing with large datasets or complex tasks that benefit from parallel execution. Despite this, its simplicity and transparency make it a favorable approach for initial pipeline development or educational purposes where understanding each step's impact is important.

#### Parallel Processing

Parallel processing divides tasks into independent components that run simultaneously, leveraging multiple computing resources to increase efficiency. This method is ideal for large-scale data processing tasks, where splitting data enables simultaneous calculations. It reduces processing time significantly, making it a crucial strategy in modern pipeline frameworks.

The complexity of parallel processing requires careful orchestration to ensure data dependencies are adequately managed. Pipelines that implement parallel execution can handle larger volumes of data more efficiently, enabling real-time analytics and faster model training. By optimizing resource use, parallel processing supports scalability in machine learning operations, and is essential for big data applications.

### Challenges in Building Machine Learning Pipelines

Here are some of the key challenges involved in building modern ML pipelines.

#### Handling Large Data Sets

Handling large datasets presents challenges in terms of storage, processing power, and data transfer. Large data volumes can slow down training and increase infrastructure costs. Solutions require optimized data storage formats, such as columnar or compressed data, to minimize space requirements and accelerate read times. Implementing chunking strategies allows for processing data in manageable parts, keeping resource usage efficient.

Distributed computing frameworks (e.g., Apache Spark) help with parallel processing of large datasets. These approaches capitalize on cluster resources, enabling data to be processed faster. Ensuring data is efficiently partitioned while maintaining its integrity is crucial when using parallel processing. Moreover, implementing effective caching strategies can further enhance processing speeds and help manage large datasets effectively.

#### Ensuring Data Quality and Consistency

Ensuring data quality and consistency is crucial, as flawed data can undermine model accuracy. Data inconsistencies often arise from incomplete records, duplicates, or misformatted entries. Pipelines must incorporate validation steps to detect and manage these issues. Preprocessing techniques like imputation, normalization, and scaling are also employed to enhance data quality, ensuring uniformity across datasets.

Monitoring data quality continuously is essential, as is collaboration with domain experts to validate correctness, creating a mechanism to maintain high data standards. Implementing automated tools within pipelines can identify anomalies early, providing corrective measures before significant downstream effects occur. Regular audits of data sources, meanwhile, ensure continued consistency.

#### Integrating with Existing Systems

Integrating machine learning pipelines with existing systems is challenging due to compatibility issues between new technologies and legacy infrastructure. Such systems might lack the flexibility needed for modern data processing, necessitating adapters or middleware that facilitate smooth data exchange. Understanding existing IT structures and constraints is vital to designing integration paths.

APIs and standardized data formats can help facilitate interaction between old and new systems. However, careful testing is necessary to ensure data integrity and performance aren't compromised. Organizations may also need to upgrade or modify parts of their infrastructure to maximize compatibility and throughput, emphasizing the need for a well-planned integration strategy.

#### Scalability and Maintenance Issues

Scalability ensures that a pipeline accommodates growing data volumes and complexity. This requires efficient resource management and designing architectures that support distributed processing. Maintaining scalability involves anticipatory infrastructure planning and adopting flexible technologies that scale horizontally or vertically with demand.

Maintenance is intertwined with scalability. Implementing monitoring and logging tools helps quickly identify bottlenecks or failures. Ensuring modularity in pipeline design supports easy updates and repairs without disrupting workflows. Automation of routine tasks and consistent codebase management through CI/CD practices also aids in reducing maintenance overhead, ensuring robust and scalable operations.

### Best Practices for Building Effective Machine Learning Pipelines

#### 1. Automate Repetitive Tasks

Automating repetitive tasks within machine learning pipelines increases efficiency and reduces the likelihood of human error. Automation facilitates consistent operations such as data cleaning, feature extraction, and model validation, improving workflow reliability. Technologies like scripting and cron jobs or more advanced workflow automation tools can handle these tasks, reducing the need for manual intervention.

Automated monitoring and logging also provide real-time insights into pipeline performance, quickly identifying deviations. This allows data scientists to focus on model development and analysis rather than operational details. By systematizing these tasks, teams achieve higher consistency and quality across projects, contributing to swifter and more reliable deployments.

#### 2. Use Version Control Systems

Using version control systems (VCS) for machine learning pipelines is a best practice that ensures the tracking and management of changes in code and data configurations. Git is a widely-used VCS, enabling collaboration across teams by maintaining a comprehensive history of changes. It supports branching and merging, facilitating parallel development efforts without conflict.

Version control allows reverting to previous states, crucial for debugging and rollback during deployment failures. It also provides a clear audit trail for compliance and reproducibility, enabling an understanding of which changes led to specific outcomes. Incorporating these systems enhances collaborative efficiency and ensures continuity in machine learning projects.

#### 3. Implement Robust Error Handling and Logging

Robust error handling and logging are critical to maintaining operational stability in machine learning pipelines. Effective error handling anticipates common failure points, implementing retries, notifications, and fallbacks to maintain pipeline integrity. Logging provides a detailed record of pipeline activity and failures, essential for diagnosing and resolving issues swiftly.

Error and log management tools should be integrated within pipelines to centralize monitoring. Utilizing logging frameworks aids in standardizing records, improving efficiency in troubleshooting. Implementing these measures ensures that pipelines remain resilient despite errors and facilitates sustained operations by providing insights for continuous improvement.

#### 4. Monitor Pipeline Performance

Monitoring pipeline performance is essential for identifying inefficiencies and ensuring effective operations. Performance metrics such as processing time, resource usage, and model accuracy need constant evaluation to ensure alignment with business objectives. Tools like Prometheus or Grafana can visualize real-time performance data, helping teams quickly identify anomalies.

Regular performance reviews are necessary to adapt pipelines to changing requirements or increasing data volumes. Automating these reviews enhances efficiency by providing timely insights. Performance monitoring is integral to pipeline design and allows teams to maximize resource utilization, optimize operations, and ensure sustained performance levels.

#### 5. Ensure Security and Compliance Standards

Protecting sensitive data and adhering to regulatory requirements is crucial for machine learning projects. Pipelines must incorporate encryption, user authentication, and access controls to safeguard data. Regular audits ensure compliance with standards like GDPR or HIPAA, minimizing risks and legal liabilities.

Implementing secure coding practices and vulnerability assessments helps identify potential risks early. Continuous monitoring of security logs further fortifies defenses, with incident response plans ready for swift action if breaches occur. By embedding security and compliance protocols into their processes, organizations maintain trust and integrity in their machine learning products.

### Orchestrating ML Pipelines with Dagster

Machine Learning pipelines are still data pipelines, and as a best-in-class data orchestrator with first-class support for [partitioning](https://dagster.io/blog/partitioned-data-pipelines), [distributed and remote execution](https://dagster.io/blog/dagster-pipes), and logging, Dagster is the perfect place to build and deploy your machine learning pipelines.

Dagster’s emphasis on making local development as easy as possible makes it easier for data scientists to experiment locally, while remaining confident that their work will be reproducible in production environments.

Because Dagster is data asset-centric, it’s also easy to structure and visualize your ML pipelines in Dagster, while the flexibility offered by [Dagster Pipes](https://dagster.io/blog/dagster-pipes) and [our integrations](https://docs.dagster.io/integrations) with the leading cloud providers and container orchestration platforms like Kubernetes ensures you can run even your most complex machine learning workloads on your infrastructure of choice.

For an example of building an ML pipeline using Dagster, [check out this example](https://dagster.io/blog) in which we showcase how to build an end-to-end pipeline for fine-tuning an LLM to serve as a support chatbot and orchestrate the whole process using Dagster.

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## The 6 Dimensions of Data Quality and How to improve Them

> Data quality refers to the condition and usefulness of a set of values of qualitative or quantitative variables.

Source: https://dagster.io/learn/data-quality

### What Is Data Quality?

Data quality measures to what extent datasets are complete, consistent, accurate, and timely. These attributes ensure that the data meets the intended purpose in operations, decision-making, and planning processes. The assessment of data quality involves measuring these dimensions against established benchmarks, ensuring the reliability and sufficiency of data across various applications.

Monitoring data quality is crucial for individuals and organizations relying heavily on data-driven strategies. Poor data quality can lead to erroneous insights and misinformed decisions, ultimately impacting productivity and profitability. Organizations aim to implement data quality frameworks to meet the increasing demand for actionable, high-quality data that supports strategic goals and operational efficiencies.

This is part of a series of articles about data governance

### Importance of Data Quality

Data quality is essential for driving accurate decision-making and maximizing the value of data assets. High-quality data enables organizations to draw reliable insights, which directly influence operational effectiveness, customer satisfaction, and competitive advantage. In contrast, poor data quality can lead to erroneous conclusions, operational inefficiencies, and missed opportunities. For instance, inaccurate customer data may result in flawed marketing strategies, while incomplete financial data can distort budgeting and forecasting efforts.

Ensuring data quality is particularly crucial in industries like healthcare, finance, and logistics, where decisions based on faulty data can lead to severe consequences, such as regulatory fines or compromised patient safety. Moreover, maintaining high data quality helps organizations meet compliance requirements, ensuring that data handling aligns with legal standards like GDPR or HIPAA.

### 6 Key Dimensions of Data Quality

#### 1. Accuracy

Accuracy measures the closeness of data values to the true or verified values. Ensuring data accuracy is crucial because decisions based on incorrect data can lead to substantial business losses. Methods for improving accuracy include validation at the point of entry and conducting regular audits to identify and rectify inaccuracies. Maintaining accuracy involves ongoing efforts to verify that data reflects real-world entities and operations correctly.

Accurate data minimizes errors and enhances confidence in decision-making processes. Implementing accuracy checks can prevent costly mistakes, especially in industries like finance and healthcare, where incorrect data can have severe ramifications. Tools that ensure accuracy typically integrate checks and validations within data entry systems, ensuring that inaccuracies are caught early and amended before they impact business processes.

#### 2. Completeness

Completeness refers to the extent to which expected data is available. Complete datasets mean no vital information is missing, which is crucial for thorough analysis and decision-making. Data completeness can be measured by comparing the proportion of observed values against expected values. Ensuring completeness involves systematically capturing all relevant data, preventing information gaps that could skew analytic outputs.

Missing data can lead to misleading conclusions, making it critical to ensure completeness in data collection processes. Organizations often implement automated checks to identify gaps in datasets promptly. Rectifying incomplete data entails designing systems capable of capturing necessary data fields consistently—this reduces the likelihood of data loss and elevates overall data quality metrics.

#### 3. Consistency

Consistency ensures data uniformity across systems and databases, implying that data does not contradict itself within the dataset or when compared to other datasets across the organization. Organizations achieve consistency by standardizing data formats and definitions across platforms to prevent discrepancies. Consistent data allows seamless data integration from various sources, enhancing usability and analytical accuracy.

Ensuring data consistency is vital for businesses with complex data ecosystems involving multiple interactions across platforms. Implementing standardized protocols helps align data across applications, promoting coherent analytics and reporting. Consistency checks usually involve aligning data fields and correcting discrepancies, thus enabling unified data views critical for strategic insights and operational coherence.

#### 4. Timeliness

Timeliness refers to the availability of data within the time frame necessary for its intended use. Data should ideally be collected, stored, and accessible in real time to maximize its utility. The timeliness of data impacts its relevance and the ability to make informed decisions promptly. Ensuring timely data involves updating data continuously and ensuring that systems provide access to the most recent information.

The timeliness of data is particularly crucial in environments where real-time analytics are essential, such as eCommerce or financial markets. Systems need to be designed to minimize latency, ensuring swift data processing and distribution. Organizations often leverage automated and intelligent data pipelines to maintain up-to-date datasets reflective of current conditions and trends.

#### 5. Validity

Validity indicates that data conforms to the necessary standards and rules, appropriately representing the real-world constructs they describe. Organizations establish clear definitions and formats for data entries to ensure validity, which facilitates seamless integration and usability across processes. Tools like data validation scripts enforce these rules, ensuring entries fall within designated specifications.

Maintaining data validity involves creating environments where data inputs conform strictly to predefined criteria. These criteria often involve data types, range constraints, and formats, ensuring entries are meaningful and functional. Valid data enhances interoperability and clarity, which is essential for operational efficiencies and strategic alignment in complex data infrastructures.

#### 6. Uniqueness

Uniqueness ensures that data entries do not contain duplicates, maintaining data integrity and preventing skewed analytics. Duplicate data entries can lead to inaccurate reporting and analysis, impacting decision-making and operational processes. Implementing regular deduplication processes is crucial for preserving dataset originality and accuracy throughout data life cycles.

Duplicate data often arises from integration processes or inconsistent data entry practices. Ensuring uniqueness involves deploying effective data matching techniques to identify and eliminate duplicates. Unique datasets are foundational for precise analysis and reporting, supporting more accurate business insights and resource allocation strategies.

<strong><em>Related content: Read our guide to data observability (coming soon)</em></strong>

### Data Quality vs. Data Integrity vs. Data Profiling

While closely related, data quality, data integrity, and data profiling refer to distinct concepts within data management:

- **Data quality** focuses on ensuring that data is accurate, complete, consistent, timely, valid and unique, so it can be effectively used for its intended purpose. It encompasses a broad range of attributes that collectively determine whether data is fit for use.
- **Data integrity** refers to the overall correctness, trustworthiness, and security of data throughout its lifecycle. It ensures that data remains unchanged during storage, transfer, or retrieval unless modified by authorized processes. Maintaining data integrity involves safeguarding against corruption, unauthorized access, and unintentional changes.
- **Data profiling** is the process of analyzing datasets to understand their structure, content, and quality. This includes examining data for patterns, missing values, and inconsistencies, which helps identify issues that could compromise data quality. Profiling is often used as a preliminary step in data cleaning efforts, providing insights into potential quality problems that need to be addressed.

### Common Data Quality Challenges and How to Address Them

Here are some of the most common issues affecting data quality in modern organizations.

#### Data Silos and Inconsistencies

Data silos occur when data is isolated within different departments or applications, leading to inconsistencies and redundancy. These silos hinder comprehensive data analysis and contribute to conflicting information across the organization. Addressing this challenge involves integrating systems and promoting data-sharing practices.

<strong>How to address:</strong>Breaking down data silos requires a strategic approach to unify disparate data sources. Techniques such as data integration, centralized data repositories, and semantic layers help eliminate or logically integrate silos, fostering a unified data ecosystem. Organizations can employ middleware solutions and APIs for seamless data flow, ensuring data consistency and enhancing collaborative analytics.

#### Poor Data Entry Practices

Inconsistent or incorrect data entry practices can result in inaccurate datasets, affecting data quality levels. These practices often stem from a lack of standardized procedures or rigorous validation protocols, leading to erroneous data entries. Implementing structured data entry methodologies and training programs can significantly reduce these errors.

<strong>How to address:</strong>Enhancing data entry practices involves deploying intuitive interfaces and automated validation checkpoints during the entry process. Training personnel on best practices and standardizing data entry formats can also mitigate errors. Organizations benefit from regular reviews of data entry procedures, ensuring that quality standards are continually met and maintained.

#### Data Duplication

Data duplication poses significant challenges, leading to misreporting and increased storage costs. Duplicated records can stem from system migrations, inadequate data integration processes, or manual entry errors. Effective deduplication processes and software tools are essential for identifying and resolving duplicates, promoting data cleanliness and efficiency.

<strong>How to address:</strong>Addressing data duplication involves deploying effective data matching and merging techniques. Organizations may use automated tools and algorithms to identify duplicate entries, streamlining processes to merge and cleanse datasets efficiently. Regular deduplication tasks help maintain high data standards, reducing errors and optimizing data utilization for strategic initiatives.

#### Data Decay Over Time

Data decay refers to the degradation of data relevance or accuracy over time. This decay happens because data can become outdated, resulting in irrelevant insights and, subsequently, poor decision-making. Regular data audits and updates are necessary to combat decay, ensuring data remains timely and reflective of current realities.

<strong>How to address:</strong>Maintaining data accuracy requires implementing systems for continuous monitoring and updates. Automated notifications and refresh protocols help manage data lifecycles effectively, preventing decay. Organizations that prioritize regular data maintenance ensure their data assets remain valuable and supportive of strategic decision-making processes.

### Data Quality Management Techniques

Here are a few common tools and techniques that can be used to improve data quality.

#### Data Cleaning and Enrichment

Data cleaning involves processes to identify and correct inaccuracies, ensuring data reliability and usability. Enrichment adds value by supplementing incomplete datasets with additional context. Both processes are crucial for maintaining high-quality data, enabling accurate analyses and enhanced insights.

Organizations can use tools that automate data cleaning tasks, and use third-party data services or open source datasets to enrich data. Continuous data monitoring and validation loops support these processes, ensuring datasets remain complete and relevant. Incorporating error-correction and data-augmentation methods significantly bolsters data quality.

#### Data Matching and Deduplication

Data matching and deduplication ensure dataset uniqueness, removing redundant entries that could compromise accuracy. These processes leverage algorithms to identify and resolve duplicate records. Effective matching techniques are critical for maintaining dataset integrity, supporting reliable analytics.

Automated tools facilitate matching and deduplication, applying advanced algorithms to streamline corrections. It is a good idea to perform automated deduplication on a regular schedule, to prevent duplication from recurring. Deduplication enhances data reliability, reducing potential errors and enhancing data-driven decision support capabilities.

#### Master Data Management (MDM)

Master data management (MDM) centralizes data management, ensuring uniformity and accuracy across organizational systems. MDM provides a cohesive approach to managing critical data, supporting consistent records and reducing data silos. Effective MDM practices are fundamental for seamless data sharing and integration.

Organizations implementing MDM frameworks achieve superior data governance and reliability. MDM tools facilitate the unification of disparate data sources, promoting standardized data environments. This approach ensures data consistency and supports strategic decision-making by maintaining data integrity.

#### Data Governance Practices

Data governance encompasses policies and procedures that govern data use, ensuring compliance with quality standards and regulations. Effective governance aligns data management initiatives with organizational objectives, promoting responsible data handling and utilization.

Implementing governance frameworks involves establishing roles, responsibilities, and protocols for data management. These frameworks integrate with data quality processes and [modern data governance tools](http://secoda.co/), ensuring consistent adherence to quality standards. Governance practices prioritize data protection, compliance, and quality, emphasizing reliable and ethical data use.

### Best Practices for Maintaining High Data Quality

#### 1. Automate Data Entry Processes

Automating data entry processes minimizes human error, increasing accuracy and consistency. Automated systems ensure data entries adhere to predefined standards, reducing the likelihood of inaccuracies. Leveraging automation tools and technologies streamlines data collection, enhancing the quality, efficiency, and timeliness of data.

Automation tools support accurate data inputs, using protocols to validate and verify entries before integration. These tools facilitate efficient data acquisition, contributing to higher data quality expectations. Organizations benefit from integrating automation into their workflows, reducing errors and fostering data integrity.

#### 2. Regularly Audit and Cleanse Data

Regular data audits and cleaning processes sustain data quality by identifying and rectifying inaccuracies. Audits involve systematic reviews to ensure datasets align with quality benchmarks and organizational standards. Cleaning addresses identified discrepancies, maintaining data integrity and usability.

Implementing scheduled audits allows proactive identification of quality issues, supporting quick remediation. Data cleaning solutions automate verification and correction processes, ensuring consistent quality levels. Continuous auditing and cleaning fortify data quality efforts, enabling reliable analytics and reporting frameworks.

#### 3. Provide Training on Data Management

Providing training on data management best practices ensures a workforce capable of maintaining high data quality levels. Training programs educate staff on standards, techniques, and tools for effective data handling. Well-trained personnel contribute significantly to data accuracy and reliability, fostering a quality-focused organizational culture.

Organizations benefit from training initiatives that align with data quality goals. These programs encompass topics like data entry accuracy, validation tools, and data security, promoting holistic data management. Continuous learning opportunities support sustained data quality improvements, aligning workforce practices with organizational strategies.

#### 4. Implement Data Governance Frameworks

Implementing data governance frameworks establishes structured protocols for data management and quality assurance. Governance frameworks define standards, roles, and procedures that ensure compliant and ethical data handling. These structures align data quality initiatives with strategic objectives, enhancing reliability and trust.

Effective frameworks involve creating policies governing data usage, privacy, and compliance, ensuring organizational alignment. These guidelines promote consistent data management, supporting transparency and accountability. Governance frameworks strengthen data quality efforts by ensuring coherent and standardized approaches to data stewardship.

#### 5. Utilize Data Quality Tools and Technologies

Utilizing data quality tools and technologies enhances efficiency in detecting and addressing quality issues. These tools automate processes like validation, cleaning, and deduplication, ensuring high standards are maintained continuously. The integration of these technologies in data management processes supports precise and reliable data outputs.

Organizations employing data quality solutions achieve greater accuracy, faster processing, and streamlined workflows. The deployment of these technologies provides scalable data management capabilities, enhancing operational excellence and strategic decision-making. Tools like data profiling and master data management systems are essential for maintaining high-quality data environments efficiently.

#### 6. Using a Data Orchestrator Like Dagster to Improve Data Quality

Using a data orchestrator, such as Dagster, can significantly improve the quality of your data pipelines. Moving away from manual processes and automating the execution of your data systems can help to ensure that your data is up-to-date, valid, and accurate.

Dagster’s [Asset Checks](https://dagster.io/blog/dagster-asset-checks) feature also allows you to validate data assets as soon as they are materialized, proactively alerting you to any data quality issues and preventing malformed or erroneous data from flowing to downstream systems.

The [Dagster+ Catalog](https://dagster.io/blog/dagster-plus-calatog-a-new-built-in-asset-library), meanwhile, enables master data management and easier discovery of trusted data assets across your organization. Unlike traditional data catalogs, Dagster is also aware of the context and processes surrounding the materialization of these assets, resulting in built-in monitoring and observability capabilities that allow you to visualize the health of your data pipelines and perform root-cause analyses from within the same platform.

To learn more about how Dagster serves as the single source of truth for your data assets, helping your team unify orchestration and data cataloging, see this [deep dive into the Dagster+ Catalog](https://dagster.io/blog/data-catalog).

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## The Role of dbt in Data Engineering & 5 Critical Best Practices

> dbt (data build tool) is an open-source tool that plays a significant role in modern data engineering, particularly in the realm of data transformation. dbt empowers data analysts and engineers to define data transformations using familiar SQL queries.

Source: https://dagster.io/learn/dbt-data-engineering

### What Role Does dbt Play in Modern Data Engineering? 

dbt (data build tool) is an open-source tool that plays a significant role in modern [data engineering](https://dagster.io/learn), particularly in the field of data transformation. 

Here's how dbt fits into the data engineering landscape:

- **dbt excels at the transformation stage** within ELT (Extract, Load, Transform) [data pipelines](https://dagster.io/platform-overview/data-orchestration). It's not designed for data extraction or loading, which are typically handled by other specialized tools or scripts.
- **dbt defines data transformations using familiar SQL queries**. This approach lowers the learning curve and makes data engineering activities more accessible to individuals with strong SQL skills.
- **dbt supports ELT (extract, load, transform) workflows**, common in modern data engineering, by simplifying the transformation layer.
- **dbt promotes modularity**, enabling users to create reusable SQL models that can be referenced in downstream transformations. This reduces code duplication and improves maintainability.
- **dbt works with modern cloud data warehouses** like [Snowflake, BigQuery, and Redshift](https://dagster.io/integrations), and big data tools like Databricks and Spark. It uses the scalability and performance capabilities of these platforms to handle large datasets and complex transformations.

In addition to these capabilities, dbt brings [software engineering best practices](https://dagster.io/blog) to data transformation workflows:

- **Version control:** It seamlessly integrates with Git, allowing teams to track changes, collaborate, and revert to previous versions easily. 
- **Testing:** dbt provides a framework for testing data transformations, helping ensure data quality and reliability. 
- **Documentation:** dbt automatically generates documentation about [data models and lineage graphs](https://dagster.io/platform-overview/data-catalog), providing a clear understanding of data flow and relationships.

While dbt doesn't function as a standalone data warehouse and has a learning curve for new users, its modular design, version control, vendor support, automated documentation, and testing make it useful for building scalable, maintainable data [pipelines](https://dagster.io/platform-overview/data-orchestration).

### dbt in the Data Engineering Lifecycle 

Here is how dbt fits into each stage of the data engineering lifecycle. 

#### 1. Data Ingestion and Loading

dbt does not extract or load data from source systems. It assumes data has already been ingested into the warehouse by external tools. Common choices include managed connectors like Fivetran, open-source options like Airbyte, or custom pipelines built with Spark, Kafka, or Python scripts. 

Once the data lands in the warehouse, dbt provides the structure to **define a *staging layer***. Staging models typically mirror raw source tables but apply light transformations such as renaming columns, enforcing consistent data types, and applying basic filters. This ensures all downstream models start from a clean, uniform foundation. 

#### 2. Data Transformation, Modeling and Testing

Developers write SQL models that define how raw data should be reshaped into meaningful datasets. These models can be materialized as ephemeral tables, persistent tables, or views depending on performance needs. dbt uses a directed acyclic graph (DAG) to determine execution order, so dependencies are resolved automatically. This prevents broken pipelines when changes occur and ensures transformations are always executed in the correct sequence. 

**Reusability** is encouraged through macros and Jinja templating, which let teams define common logic once and apply it across many models. Transformations can also incorporate incremental logic, so dbt only processes new or updated records, reducing runtime and warehouse [cost](https://dagster.io/pricing)s.

dbt encourages a **layered modeling pattern**. At the base, *staging models* prepare raw data. *Intermediate models* combine, filter, and enrich staging tables to form subject-area datasets. *Mart models* or *business models* provide analyst- and business-facing outputs, often optimized for dashboards, reporting, or machine learning. 

**Testing** complements this layered design. Schema tests validate assumptions about data integrity, such as primary keys being unique or foreign keys matching across tables. Custom tests allow for business-specific rules, such as ensuring transaction amounts are non-negative or dates fall within expected ranges. 

#### 3. Deployment and Orchestration

dbt itself executes models but does not include a built-in orchestration engine (except in dbt Cloud). [Teams](https://dagster.io/customers) typically schedule and monitor dbt runs with [orchestration engines](https://dagster.io/platform-overview/data-orchestration) like Dagster or Airflow. These orchestrators handle dependencies across systems, retries on failure, and alerting. dbt integrates easily into these workflows since it can be run as a CLI command or API job. 

Deployment is managed through Git workflows, where every change to a model is reviewed, tested, and merged before being promoted to production. CI/CD pipelines validate dbt projects automatically by running lightweight checks, executing tests, and building temporary environments for validation.

#### 4. Discovery and Feedback

Documentation can be automatically generated from dbt projects. **Model descriptions, test definitions, sources, and lineage** are compiled into a browsable site that non-technical stakeholders can use. The lineage graph shows how each dataset is connected, making it easier to trace the impact of schema changes or failed models. 

Analysts can see the origin of every field, which builds confidence in metrics and calculations. Feedback loops are supported by this transparency—if downstream teams notice [anomalies](https://dagster.io/glossary/anomaly-detection), they can quickly identify upstream models and collaborate with engineers on fixes. Since dbt projects are stored in Git, feedback can also take the form of issues, pull requests, or in-code comments.

### Best Practices for Using dbt in Data Engineering 

Here are some useful practices to keep in mind when working with dbt for data engineering use cases.

#### 1. Designing Clear and Maintainable Project Structures

A dbt project benefits from a layered structure that makes data transformations predictable and easy to follow. The most common pattern is to organize models into three main layers: staging, intermediate, and marts. Staging models should provide a one-to-one mapping of raw source tables, applying only minimal transformations such as renaming columns, casting data types, and removing invalid rows. 

This creates a consistent foundation and makes it easier to update source logic in one place if schemas change. Intermediate models then perform heavier transformations like joins, calculations, or aggregations while remaining modular and reusable across different downstream datasets. Finally, mart models present clean, business-facing outputs tailored to analysts or machine learning pipelines.

Maintaining clear naming conventions helps avoid confusion in large projects. Prefixes like stg\_, int\_, and mart\_ or schema-based separation (staging, intermediate, analytics) allow teams to quickly identify the purpose of each model. Organizing models by functional domain, such as marketing, finance, or product, further improves maintainability by grouping related logic.

#### 2. Building Reliable Tests and Quality Gates

Testing in dbt ensures that datasets remain accurate and trustworthy as they evolve. Out-of-the-box schema tests cover foundational checks such as ensuring a primary key is unique, foreign keys correctly map across tables, or required fields are never null. These catch common data integrity issues before they propagate downstream. 

More advanced logic can be implemented with custom tests, written as SQL queries that return failing records. Examples include verifying that order amounts are always greater than zero, ensuring dates fall within an expected range, or checking that product SKUs exist in a reference table. 

Quality gates extend beyond local testing into CI/CD workflows. In practice, every pull request should trigger lightweight dbt tests on the developer branch. These catch issues early, preventing broken transformations from being merged into production. A second layer of testing can run in staging environments, where the full pipeline and complete test suite validate the entire dataset. 

#### 3. Managing Dependencies and Packages Effectively

dbt encourages modularity, but without proper dependency management, projects can become brittle or inconsistent. The ref() and source() functions should be used consistently to define dependencies between models. This ensures lineage is tracked in the DAG, making it possible to correctly manage dependencies and ensure models execute in the correct order. 

Hardcoding table references bypasses lineage tracking and creates hidden coupling, which often leads to errors when projects evolve. Following the principle “always use ref or source” enforces maintainability and prevents broken pipelines.

Dependencies also extend to packages. dbt supports external and internal packages through the packages.yml file, which can include both open-source libraries (like dbt-utils) and private, organization-specific packages. Pinning versions of packages is essential to avoid unexpected changes from upgrades. 

Teams often build internal packages to centralize macros for common logic, such as standard date handling, currency conversion, or business metric definitions. This ensures consistency across different projects and reduces duplication. Another best practice is to isolate reusable code in a macros or utils directory, keeping the transformation logic clean and focused.

#### 4. Optimizing dbt Performance at Scale

As dbt projects grow, performance challenges become more noticeable, especially in cloud data warehouses where compute time translates directly into cost. A key strategy is using incremental models, which process only new or updated data instead of rebuilding entire tables. This reduces runtime for large fact tables, such as clickstream events or transaction histories. 

Materialization strategies should be chosen carefully: views are efficient for small transformations, tables are better for heavy joins or aggregations, and incremental tables provide long-term scalability for growing datasets.

Warehouse-specific optimizations further improve performance. Partitioning and clustering tables in platforms like BigQuery or Snowflake accelerate queries by reducing scanned data. Teams should also review execution plans regularly to identify inefficient joins, unnecessary filters, or missing indexes. 

#### 5. Integrating dbt into Continuous Delivery Workflows

dbt projects benefit from the same CI/CD principles used in software engineering. Every code change should go through Git-based workflows with pull requests, code reviews, and automated validation. A typical CI pipeline includes compilation checks to verify SQL syntax, lightweight test runs to catch schema errors, and temporary schema builds to validate transformations. 

For example, a developer branch might spin up a temporary environment in Snowflake or BigQuery where models are executed against a subset of data. This prevents issues from reaching production while giving developers fast feedback.

On merge, CD pipelines take over. A staging environment runs the full dbt project, executing all tests and building production-like datasets for validation. Once these checks pass, the project is deployed to production automatically or with a controlled approval step. 

[Orchestration tools](https://dagster.io/platform-overview/data-orchestration) can trigger dbt runs as part of larger workflows, ensuring coordination with ingestion or downstream processes. dbt Cloud offers built-in scheduling and job management, but many teams prefer integrating dbt into their existing orchestration stack. 

### Orchestrating dbt Data Pipelines with Dagster

Dagster provides a modern orchestration layer that pairs naturally with dbt, allowing teams to manage transformations alongside ingestion, quality checks, and downstream processes. While dbt handles the transformation logic, Dagster coordinates how these models run within the broader data pipeline.

In Dagster, dbt models are represented as individual assets, giving you clear lineage, selective execution, and visibility into how upstream changes affect downstream outputs. The platform also captures dbt run results, tests, and metadata in one place, creating a unified operational view of the entire pipeline.

With [Dagster Components](https://dagster.io/platform-overview/components), teams can integrate a dbt project quickly using prebuilt building blocks. A dbt project configuration can be pulled in with minimal setup, automatically generating assets for models and snapshots while allowing extensions through components for ingestion tools, warehouses, or Python transformations. This speeds up onboarding and simplifies the path to production-ready workflows.

The orchestration layer also brings everything together in a single environment, enabling dbt runs to coexist with ingestion tasks, validation steps, and downstream analytics or machine learning workloads. Scheduling, logging, and observability are centralized, and partial or incremental execution happens intelligently based on asset dependencies.

Together, dbt and Dagster form a reliable and scalable foundation for modern data engineering. dbt ensures high-quality transformations, and Dagster ensures those transformations run consistently, efficiently, and as part of a well-orchestrated system.

### Frequently Asked Questions

#### What role does dbt play in modern data engineering?

dbt is an open-source tool focused on the transformation stage of ELT pipelines. It uses SQL to define transformations, integrates with Git for version control, and supports testing and documentation for data models. Teams leveling up may also use [webinars and live learning sessions](https://dagster.io/events) alongside hands-on practice.

#### How does dbt fit into ingestion and loading?

dbt does not extract or load data; it assumes data is already in the warehouse from tools like Fivetran, Airbyte, or custom pipelines. It then defines staging and downstream models on top of that data.

#### How do teams orchestrate dbt runs?

Outside of dbt Cloud's scheduling, teams commonly use orchestrators such as Dagster or Airflow to schedule runs, manage dependencies across systems, handle retries, and coordinate dbt with other pipeline steps.

#### What layered structure does dbt encourage?

A typical pattern uses staging models for light cleanup of raw data, intermediate models for joins and enrichments, and mart or business models for analyst- and business-facing outputs.

#### Why are ref() and source() important in dbt?

They declare dependencies explicitly so the DAG reflects lineage, models run in the correct order, and projects stay maintainable compared to hardcoded table names.

---

## Top 12 Data Quality Dimensions in 2026

> Data quality dimensions are the characteristics used to measure data fitness, commonly including accuracy, completeness, consistency, validity, timeliness, and uniqueness. These dimensions help organizations evaluate and manage data quality by providing a framework to assess whether data is correct.

Source: https://dagster.io/learn/data-quality-dimensions

### What Is Data Quality? 

[Data quality](https://dagster.io/learn/data-quality) dimensions are the characteristics used to measure data fitness, commonly including accuracy, completeness, consistency, validity, timeliness, and uniqueness. These dimensions help organizations evaluate and manage data quality by providing a framework to assess whether data is correct, whole, uniform, in the proper format, up-to-date, and without duplicates.

Core data quality dimensions include:

- **Accuracy:** The degree to which data correctly represents the real-world object or event it describes. 
- **Completeness:** The degree to which all required data is present. 
- **Consistency:** The extent to which data is uniform and free from contradictions across different systems or storage locations. 
- **Validity:** The degree to which data conforms to the pre-defined format, type, or rules it is expected to follow. 
- **Timeliness:** A measure of how up-to-date the data is and whether it is available when needed. 
- **Uniqueness:** The extent to which a dataset contains only one instance of an entity, ensuring there are no duplicate records.
- **Integrity:** The structural and relational quality of data, ensuring relationships between data elements are maintained. 

Other important dimensions include:

- **Accessibility:** How easily data can be acquired when needed. 
- **Relevance:** The degree to which the data is useful and pertinent to the task at hand.
- **Usability:** How easily users can understand, access, and apply data for their intended tasks, influenced by documentation, formats, and consistency.
- **Reliability:** The stability and dependability of data over time, despite system changes or failures.

**Lineage and traceability:** The ability to track the origin, transformations, and usage of data throughout its lifecycle for transparency, governance, and compliance.

### Why Data Quality Dimensions Matter 

Understanding data quality dimensions helps organizations define, measure, and manage the fitness of their data for use. These dimensions act as criteria for assessing data, making it easier to identify quality issues and apply targeted solutions. Without a clear framework, data quality efforts often lack direction and consistency.

- **Clarity and standardization**: Dimensions provide a common vocabulary for discussing data issues across teams, ensuring everyone evaluates quality using the same benchmarks.
- **Root cause analysis**: By mapping quality problems to specific dimensions (e.g., accuracy, completeness), teams can more easily diagnose and correct the source of issues.
- **Improved data governance**: Quality dimensions support data stewardship by linking business rules and data policies to measurable characteristics.
- **Impact assessment**: Measuring quality across dimensions helps quantify the business impact of bad data, making it easier to prioritize remediation efforts.
- **Quality monitoring**: Dimensions enable the design of automated checks and dashboards that continuously track quality metrics, catching issues before they spread.
- **Alignment with use cases**: Different applications require different levels and types of quality. Dimensions help tailor quality standards to match operational, analytical, or compliance needs.

### The Core Data Quality Dimensions Explained 

#### 1. Data Accuracy

Data accuracy measures how closely data represents the real-world objects or events it is intended to describe. inaccurate data can stem from manual entry errors, faulty integrations, or mismatched data sources. For example, if a customer address is mistyped or stored with incorrect postal codes, downstream processes such as order fulfillment and billing may be negatively affected. 

Poor data accuracy leads to operational challenges and increases costs due to rejected transactions or corrective work. As organizations integrate data from multiple environments, maintaining a single version of the truth becomes challenging. Accuracy is usually validated through cross-referencing, reconciliation with trusted sources, or implementing data governance policies that enforce periodic reviews. 

#### 2. Data Completeness

Data completeness refers to the presence of all required data elements needed for business processes or analytics. Missing fields, incomplete records, or omitted transactions are primary indicators of poor completeness. For example, if an employee database lacks emergency contact information for many entries, it limits the effectiveness of HR’s response during incidents. 

Data completeness is measured by defining required attributes for each dataset and monitoring population rates. Lack of completeness disrupts analysis, reporting, and operational efficiency. It can also be symptomatic of upstream issues such as faulty collection mechanisms or inconsistent requirements. To improve completeness, organizations typically set minimum field requirements in data schemas and automate checks for missing values. 

#### 3. Data Consistency

Data consistency addresses whether data values are uniform across different systems, datasets, or periods. A lack of consistency can occur when departments maintain separate records with conflicting information, such as two systems showing different birthdates for the same customer. Consistency issues often arise from merger activity, decentralized data entry, or differences in formats and standards between applications.

Ensuring consistent data involves standardizing formats, synchronizing updates, and establishing master data management (MDM) processes. Automated reconciliation across multiple sources can flag and resolve anomalies quickly. Consistency is foundational for integration projects and reliable analytics since mismatches across sources can undermine reports and business intelligence. Regular checks for referential integrity and alignment with business rules are essential preventative measures.

#### 4. Data Validity

Data validity assesses whether data values conform to acceptable formats, types, and predefined rules set by business or regulatory frameworks. invalid data can be as simple as an alphabetic character in a numeric field or as complex as an out-of-range insurance claim amount. Data validity is crucial to prevent process failures, compliance breaches, or analytical distortions.

Maintaining validity requires defining strict validation rules at data entry, during integration, and across all processing stages. Automated rules in ETL jobs and input forms can catch a large percentage of invalid data before it reaches core systems. Periodic validation checks, automated cleansing routines, and clear documentation of business standards all contribute to higher data validity over time. 

#### 5. Data Timeliness

Data timeliness is the degree to which information is up-to-date and available when needed. Stale or delayed data can significantly impact decision-making, especially in real-time analytics, dynamic reporting, or automated processes. For example, using yesterday’s inventory data for today’s stock order might lead to missed sales or overstocking. Timeliness depends on both the speed of data capture and the efficiency of integration with consuming systems.

Addressing timeliness means aligning data collection and distribution processes with business needs. This may involve deploying streaming architectures, batch processing optimizations, or real-time API integrations. Monitoring lags between data creation and availability can help diagnose bottlenecks. Organizations often define service-level agreements (SLAs) for refresh rates and set automatic alerts for delayed data.

#### 6. Data Uniqueness

Data uniqueness measures whether there are duplicate records or redundant entries within a dataset. Duplicates may result from improper data merges, inconsistent identifiers, or repeated data capture processes. In customer databases, for example, the presence of duplicated accounts can cause issues in reporting, personalized marketing, or customer support.

To ensure uniqueness, organizations employ strategies such as de-duplication routines, enforcing unique constraints in databases, and using master data management solutions. Regular scanning for duplicate values, especially for keys like email or account ID, is standard practice. Correcting uniqueness issues improves analytics alongside the user experience and operational efficiency.

#### 7. Data Integrity

Data integrity focuses on maintaining correct relationships among data elements and protecting data from unauthorized alteration or corruption. Integrity is ensured by validating foreign keys, maintaining referential constraints, and using checksums or audit trails to track changes. When integrity is compromised, downstream calculations or system processes may fail or produce unreliable results.

Strong data integrity is crucial for complex environments where dependencies span multiple tables, data models, or applications. Implementing controls such as transaction logging, access permissions, and rollback capabilities helps protect data from both accidental and malicious actions. 

### Additional Data Quality Dimensions Used in Modern Data Ecosystems 

#### 8. Data Accessibility

Data accessibility is the measure of how easily authorized users can retrieve and use data when required. Barriers to accessibility can include restrictive controls, siloed storage, archaic formats, or lack of discoverability. High accessibility empowers different business units to derive value from enterprise data without unnecessary delays or dependencies on specialized staff.

To enhance accessibility, organizations build user-friendly data catalogs, implement robust access management policies, and integrate datasets into central repositories or modern data platforms. Access privileges should balance protection from unauthorized use with the needs of workflow agility and innovation. 

#### 9. Data Relevance

Data relevance gauges whether data supports the goals of stakeholders or decisions being made. Information that is technically correct but irrelevant to the current context can distract from insights or mislead users. Relevance may change as business priorities shift, new compliance needs arise, or analytics evolve.

Ensuring relevance involves regular reviews of data collection processes, cataloging, and collaboration with stakeholders to align content with current objectives. Organizations should establish clear criteria for what data is pertinent to particular workflows, projects, or regulatory requirements. Archiving or removing outdated information helps reduce clutter and focuses analysis on materials that provide clear value.

#### 10. Data Usability

Data usability refers to the ease with which users can work with data for their tasks. Usability spans file formats, documentation quality, metadata availability, and clarity of definitions. Even high-quality data loses value if it is hard to understand, access, or manipulate. Usability issues often show up as friction in onboarding new users, inconsistencies across sources, or mismatches between documentation and actual content.

Improvements in usability are achieved with thorough data dictionaries, training materials, standardized formats, and consistent labeling practices. In self-service analytics environments, intuitive data presentation and discovery capabilities are key. Feedback from end-users should drive usability enhancements, ensuring that the data is practical and efficient for its audience.

#### 11. Data Reliability

[Data reliability](https://dagster.io/guides/data-reliability-challenges-measurement-best-practices) measures the likelihood that data remains accurate and available over time, regardless of disruptions or changes in the environment. Reliable data comes from robust sourcing, thorough validation, and proven operational processes. Reliability is a critical requirement for systems where delayed or missing data could have business or safety impacts, such as healthcare or financial services.

Achieving high reliability depends on architecture redundancies, failover mechanisms, and continuous validation pipelines. Monitoring solutions can help quickly detect data interruptions, corruption, or discrepancies so they can be rectified before affecting downstream outputs. In regulated industries, reliability is often audited and reported as part of compliance requirements.

#### 12. Lineage and Traceability

Lineage and traceability cover the ability to track data origins, transformations, and usage throughout its lifecycle. High-quality lineage provides visibility into where data came from, how it was processed, and where it was delivered. Traceable data is crucial for debugging errors, ensuring compliance, and establishing audit trails that meet regulatory or operational standards.

Lineage management uses metadata, automated logging, and visual mapping to document data flows end-to-end. This supports not only technical troubleshooting but also transparency in governance and reporting. By making traceability a fundamental part of the architecture, organizations can respond more quickly to incidents, such as data breaches or erroneous reports, and provide stakeholders with confidence in every step of the information chain.

### How to Measure Data Quality Dimensions 

Measuring data quality requires defining actionable metrics for each quality dimension. These metrics help quantify how well the data meets expectations and support monitoring, benchmarking, and improvement efforts across the organization.

#### 1. Accuracy Metrics

Accuracy is typically measured by comparing data against a trusted source or a known baseline. Common metrics include:

- **Error rate**: Percentage of records with incorrect values
- **Field-level accuracy**: Proportion of fields that match the reference data
- **Validation checks**: Results from rules that flag improbable or invalid entries

Manual reviews and third-party verification are often used to validate critical data elements.

#### 2. Completeness Metrics

Completeness is assessed by measuring the presence of required fields or records. Typical indicators include:

- **Missing values rate**: Percentage of null or empty fields
- **Population completeness**: Proportion of expected records that exist in the dataset
- **Attribute coverage**: Share of records with all mandatory attributes populated

Dashboards can be configured to monitor fill rates for key data fields over time.

#### 3. Consistency Metrics

Consistency is evaluated by identifying conflicts across datasets or within the same dataset over time:

- **Data conflict rate**: Percentage of records with mismatching values across sources
- **Cross-table consistency**: Ratio of conforming entries between linked systems
- **Standardization compliance**: Degree to which values adhere to prescribed formats

Automated data reconciliation and format validation scripts are often used here.

#### 4. Timeliness Metrics

Timeliness metrics focus on how current or delayed the data is:

- **Data latency**: Time difference between data generation and availability
- **Refresh rate**: Frequency of data updates
- **Staleness score**: Number or percentage of records that exceed the acceptable age

Monitoring tools should flag outdated records or lagging data feeds.

#### 5. Validity Metrics

Validity is measured by checking how well data conforms to predefined rules:

- **Rule violation rate**: Number of entries that break data validation rules
- **Format conformance rate**: Percentage of fields with correct data types and patterns
- **Business logic failure rate**: Instances where data doesn’t meet domain-specific constraints

ETL pipelines often embed these checks at ingestion and transformation stages.

#### 6. Uniqueness Metrics

To measure uniqueness, tools identify duplicates based on key identifiers:

- **Duplicate rate**: Number or percentage of non-unique records
- **Duplicate clusters**: Count of grouped records sharing the same key
- **Redundancy ratio**: Proportion of repeated values in fields expected to be distinct

These metrics are often computed using fingerprinting or fuzzy matching techniques.

#### 7. Reliability and Availability Metrics

These dimensions can be assessed with operational and system-level metrics:

- **System uptime**: Percentage of time data systems are operational
- **Failure rate**: Incidents of data processing or access failures
- **Data availability SLA adherence**: Percentage of time data is delivered within SLA windows

Monitoring tools and logs play a crucial role in tracking these metrics.

### Best Practices to Improve Data Quality

Here are some of the ways that organizations can use various metrics and dimensions to improve their data quality.

#### 1. Define Clear Data Standards and Policies

Establishing data standards and policies is foundational to achieving high data quality. Organizations need documented definitions for core data elements, consistent formatting rules, and business logic that must be applied across systems. These standards set expectations for everyone who interacts with data, from entrance points like data entry to integration processes and analytics.

Policies should also address data retention, consent management, and usage restrictions compliant with privacy laws or industry regulations. Periodic reviews and stakeholder input help keep standards current as workflows, technologies, and governance needs evolve. Training staff on these requirements ensures adoption and reduces divergence from approved practices.

#### 2. Implement Automated Data Quality Checks

Automated data quality checks embed validation and monitoring into data workflows, reducing the manual effort and human error inherent in manual processes. Using rules engines, scripts, or third-party tools, organizations can evaluate attributes like accuracy, completeness, and validity at ingestion or as data moves through pipelines. Automation enables quick identification of anomalies or inconsistencies, minimizing the time bad data spends in production systems.

Automation is especially crucial in large and complex environments, where manual reviews are infeasible. Regular updates to checking rules, based on observed issue patterns and shifting business needs, help maintain effectiveness. By incorporating automated alerts, corrective actions can be triggered before downstream failures occur.

#### 3. Embed Data Quality Into ETL and ELT Processes

Embedding data quality checkpoints directly into ETL (extract, transform, load) and ELT (extract, load, transform) workflows ensures issues are addressed as close to the source as possible. Quality checks may include validation for duplicates, missing values, out-of-range metrics, or conformance to reference data. Integrating these routines minimizes the risk of flawed data entering analytics models or business processes.

Tight integration with ETL/ELT also accelerates error handling and remediation. When quality rules are run automatically with every data movement or transformation, issues can be flagged and addressed without delay. Documentation and versioning of these checks support transparency and allow for adjustments as data structures or business requirements change.

#### 4. Create Feedback Loops Between Data Owners and Consumers

Establishing feedback loops between data owners (who manage and curate datasets) and consumers (who use data for analysis or operations) is critical for long-term quality improvement. Transparent channels for reporting data issues, suggesting enhancements, or updating quality requirements enable faster resolution of problems and adaptability to changing business needs. 

These loops encourage shared responsibility for data health across the organization. Feedback-driven processes can include ticketing systems, user surveys, regular cross-functional meetings, or embedded commenting tools within data catalogs. Collecting and analyzing feedback trends informs targeted training, technology investments, and process redesigns for persistent issues. 

#### 5. Monitor Data Quality Continuously with Observability Platforms

[Data observability](https://dagster.io/guides/data-observability-in-2025-pillars-pros-cons-best-practices) platforms provide real-time monitoring, anomaly detection, and root cause analysis for data quality across pipelines and repositories. These platforms use automated dashboards, alerting mechanisms, and historical trend analysis to surface quality degradations as they happen. Observability tools reduce the lag between issue occurrence and detection, allowing teams to respond proactively rather than reactively.

Continuous monitoring with observability platforms supports automated remediation, granular metrics tracking, and long-term governance reporting. Integration with incident management and workflow tools ensures quality issues are documented, routed, and addressed systematically. 

### Improving Data Quality with Dagster

Dagster provides a unified orchestration platform that helps organizations build, operate, and monitor data pipelines with consistent, reliable, and high quality outputs. Because data quality issues often originate in pipeline logic, dependency management, or a lack of visibility into runs, Dagster’s design directly supports the enforcement of quality dimensions throughout the data lifecycle.

Dagster improves data quality through several key capabilities:

<strong>Declarative, typed assets<br></strong>Dagster's software defined assets model allows teams to express data dependencies and validations as code. Typed inputs and outputs help enforce schema expectations, prevent invalid data from flowing downstream, and encourage clear ownership. This approach increases accuracy, completeness, and validity by catching problems before assets are materialized.

<strong>Built in observability and monitoring<br></strong>Dagster provides automated lineage tracking, run logs, event streams, and materialization history. This visibility supports reliability, timeliness, and traceability. Teams can quickly detect anomalies, identify the root cause of failures, and understand how upstream changes affect downstream assets. By surfacing metadata at each step, Dagster also supports auditing and compliance for regulated environments.

<strong>Asset checks for continuous quality enforcement<br></strong>Dagster asset checks allow data teams to define custom quality rules that execute automatically. Checks can validate uniqueness, detect schema drift, assess freshness, evaluate completeness, or compare values against reference data. Since checks run as part of orchestrated workflows, they ensure that quality holds steady as data evolves. Failed checks can trigger alerts, retries, or downstream protections.

<strong>Native support for cross team governance<br></strong>Dagster encourages reusable components, clear ownership boundaries, and standardized patterns. With asset definitions stored in version controlled repositories, teams can coordinate updates, enforce governance rules, and maintain documentation. These practices elevate usability, relevance, and accessibility by making datasets easier to discover and understand.

<strong>A foundation for proactive quality management<br></strong>By combining lineage, observability, automated checks, and typed asset definitions, Dagster enables organizations to shift from reactive cleanup to proactive prevention. Data quality becomes part of the development lifecycle instead of an after the fact concern. As a result, teams ship more dependable pipelines and deliver more trustworthy data products.

---

## Top 4 DBT Cloud Alternatives in 2026

> dbt Cloud is a managed service for data transformation built on dbt Core, designed to simplify the process of building, deploying, and managing data transformation pipelines.

Source: https://dagster.io/learn/dbt-cloud-alternatives

### What Is dbt Cloud?

[dbt ](https://dagster.io/learn/data-build-tool)Cloud is a managed service for data transformation built on dbt Core, designed to simplify the process of building, deploying, and managing data transformation pipelines. It offers a user-friendly interface and features like integrated version control, job scheduling, and rich documentation for data models. 

By automating many processes, it reduces the complexity involved in setting up and managing data transformation workflows, enabling teams to focus more on data modeling and analysis rather than infrastructure concerns. dbt Cloud enhances collaboration among data teams by providing tools that integrate seamlessly with existing data platforms. 

This integration allows for more efficient sharing and validation of models across teams. With features like debugging, model development, and testing environments, dbt Cloud supports iterative development and rapid prototyping. It allows teams to operationalize analytics quickly, ensuring data consistency and accuracy while optimizing model deployment and collaboration.

### Why Consider Alternatives to dbt Cloud? 

While dbt Cloud is a powerful tool for data transformation, it may not be the best fit for every organization. Several practical challenges lead businesses to explore alternatives:

- **Limited visual tools** – dbt primarily relies on SQL and code-based workflows, which can be a barrier for teams that prefer visual interfaces for designing and managing transformations.
- **Steep learning curve** – Mastering dbt requires familiarity with Git workflows, YAML configurations, and Jinja templating, which not all data professionals have.
- **Enterprise requirements** – Larger organizations may need features like role-based access control, audit logs, and priority support, which aren't fully covered by dbt’s open-source version.
- **Real-time processing** – dbt is optimized for batch transformations but lacks native support for real-time data processing.
- **Integration complexity** – Setting up dbt within an existing data stack can be challenging, especially if the data warehouse or tools are not natively supported.
- **Resource overhead** – Managing dbt effectively requires dedicated engineering resources, which some teams may not have.
- **Orchestration limitations** – While dbt handles transformations, it doesn’t provide full pipeline orchestration, requiring additional tools for scheduling and monitoring.
- **Debugging challenges** – Troubleshooting failed transformations often involves manually reviewing logs and code, which can slow down development.
- **Hybrid environment support** – Organizations operating in mixed cloud and on-premises environments may require more flexible deployment options.

Given these considerations, teams often seek alternatives that better match their workflow, technical expertise, and infrastructure requirements. Choosing the right tool depends on factors like ease of use, transformation capabilities, performance optimization, integration flexibility, and enterprise-grade features.

### Alternative Ways to Run dbt Without dbt Cloud Service 

#### 1. Dagster

![Dagster logo](https://dagster.io/webflow-assets/2948341b9c94babe480a.webp)

Dagster is a data orchestration platform designed to manage end-to-end data pipelines, with dbt commonly used as the transformation layer within a broader workflow. Rather than focusing solely on scheduling dbt commands, Dagster models pipelines around data assets and their dependencies, making it well suited for teams that need to coordinate dbt with ingestion, validation, and downstream processing.

Dagster emphasizes software-defined pipelines and strong observability. dbt runs orchestrated by Dagster are treated as first-class steps within a pipeline, allowing teams to monitor execution, capture metadata, and understand failures in the context of upstream and downstream dependencies. This makes debugging and impact analysis easier compared to job-level scheduling alone.

**Key features:**

- **Asset-based orchestration** – Model data pipelines as assets with explicit dependencies, enabling targeted re-runs and clearer lineage
- **Native dbt integration** – Orchestrate dbt Core projects while preserving dbt’s DAG and metadata
- **Event-driven execution** – Trigger dbt runs based on data arrival, upstream job completion, or external events
- **Strong observability** – Track runs, logs, and metadata across dbt and non-dbt steps in a single UI
- **Flexible deployment** – Available as a managed service (Dagster Cloud) or self-hosted

**How to run dbt with Dagster:**

- **Install Dagster and its dbt integration**: Add Dagster and the Dagster–dbt package to your environment
- **Load your dbt project**: Configure Dagster to reference your dbt project and profiles
- **Generate your assets**: Represent dbt models as Dagster assets automatically using Components

**Schedule or trigger runs**: Use schedules for time-based execution or sensors for event-driven runs

![Key features of Dagster](https://dagster.io/webflow-assets/0aae8c1956f775fff025.webp)

#### 2. Airflow

![Airflow logo](https://dagster.io/webflow-assets/bc7b3636a43b1fe6f7bc.webp)

Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring workflows. It can be used to orchestrate dbt runs by defining custom DAGs (Directed Acyclic Graphs) that control when and how dbt models are executed. Airflow offers fine-grained control over task dependencies.

<strong>Key features: </strong>

- **Flexible DAGs** – Define workflows as Python code for full customization of task dependencies
- **Extensible plugins** – Integrate with cloud services, databases, and monitoring tools through a rich plugin ecosystem
- **Scalability** – Run on a single machine or scale out with Kubernetes and Celery executors
- **Monitoring UI** – Visualize and monitor task execution through a built-in web interface
- **Retry and alerting** – Configure retry logic and failure notifications to improve reliability

**How to run dbt with Airflow:**

1. **Install Airflow and dbt:** Ensure both tools are installed in your environment.
2. **Define a DAG:** Create a Python file that defines a DAG representing your dbt workflow.
3. **Use operators:** <br>
   - **BashOperator:** Run dbt Core commands like dbt run, dbt test, or dbt seed via Bash.
   - **dbt Cloud Provider (if you still use dbt Cloud jobs):** Use DbtCloudRunJobOperator to trigger dbt Cloud jobs from within Airflow.

**Schedule and orchestrate:** Set schedules for your DAGs and manage upstream/downstream dependencies with Airflow’s scheduler.

![Key features of Airflow](https://dagster.io/webflow-assets/d9d123488cf41f783ee5.webp)

#### 3. Prefect

![Prefect logo](https://dagster.io/webflow-assets/a03b72a7b34196b38496.webp)

Prefect is a modern data workflow orchestration tool to simplify the management of complex data pipelines. It integrates smoothly with dbt and provides a Python-based interface for building, scheduling, and monitoring workflows. Prefect emphasizes observability and error handling, helping provide resilience and transparency in dbt pipelines.

<strong>Key features: </strong>

- **Python-native workflows** – Use Python code to define and manage dbt workflows
- **Hybrid execution** – Run locally or in the cloud with Prefect Cloud or Prefect Server
- **Automatic retries and caching** – Improve pipeline reliability and performance
- **Dynamic task mapping** – Create tasks at runtime for more flexible pipelines
- **Rich UI and logs** – Gain visibility into workflow runs with detailed logs and dashboards

**How to run dbt with Prefect:**

1. **Install Prefect and prefect-dbt:** Add Prefect and the optional dbt integration package.
2. **Create a flow:** In a Python script, define a Flow that wraps dbt commands as Prefect tasks.
3. **Call dbt CLI:** Inside tasks, invoke dbt Core commands (e.g., dbt run, dbt test) using Python subprocess or provided blocks.

**Schedule and monitor:** Use Prefect’s scheduler and UI to trigger runs, inspect logs, and handle retries.

![Key features of Prefect](https://dagster.io/webflow-assets/1ddf2c94d15b45684f94.webp)

#### 4. Orchestra 

![Orchestra logo](https://dagster.io/webflow-assets/5765cdfc74b19fdc31dc.webp)

Orchestra is a developer-first orchestration platform for analytics engineers and dbt workflows. It allows teams to run dbt projects with integrated scheduling, monitoring, and metadata tracking, without the complexity of managing traditional orchestration systems. Orchestra focuses on ease of use and dbt integration.

<strong>Key features: </strong>

- **Native dbt integration** – Automatically detects and runs dbt models with minimal setup
- **Simple scheduling** – Configure runs using a clean, code-based interface
- **Metadata insights** – Track model dependencies and performance metrics over time
- **Minimal setup** – Get started quickly without managing infrastructure
- **Cloud-native platform** – Designed for data teams working in cloud environments

**How to use Orchestra to run dbt:**

1. **Connect your code repository:** Authorize Orchestra to access your git repo (e.g., GitHub or Bitbucket) where your dbt project resides.
2. **Configure dbt tasks:** Use pre-built tasks or configuration in Orchestra to execute dbt Core commands (like dbt run, dbt test, etc.) as part of your workflow.
3. **Define schedules and dependencies:** Within Orchestra, set up schedules and orchestrate run sequences, ensuring dbt jobs execute at the right times and in the right order among other pipeline steps.

**Monitor and iterate:** Use Orchestra’s dashboards and logs to track run outcomes, lineage, and performance, allowing you to refine your dbt orchestration over time.

![Key features of Orchestra](https://dagster.io/webflow-assets/4c976de52661f683219d.webp)

### Conclusion

While dbt Cloud simplifies many aspects of managing dbt workflows, it's not the only way to run dbt in production. Tools like Dagster, Airflow, Prefect, and Orchestra provide alternative orchestration options with different strengths. Choosing the right one depends on the team’s technical expertise, infrastructure needs, and desired level of control over workflow execution.

---

## Top 5 Data Engineering Certifications in 2026

> Data engineer certifications validate the skills and knowledge needed to design, build, and manage data pipelines, systems, and infrastructure. They demonstrate expertise in areas like ETL processes, data warehousing, data modeling, and cloud technologies relevant to data engineering.

Source: https://dagster.io/learn/data-engineering-certification

### What Is a Data Engineering Certification? 

Data engineer certifications validate the skills and knowledge needed to design, build, and manage data pipelines, systems, and infrastructure. They demonstrate expertise in areas like ETL processes, data warehousing, data modeling, and cloud technologies relevant to [data engineering](https://dagster.io/learn/data-engineering). These certifications can be beneficial for both career advancement and staying current with industry best practices.

These certifications are typically offered by technology vendors, industry groups, and educational institutions to validate a candidate’s skills in extracting data from various sources, transforming it for analysis, and ensuring its availability for business intelligence or machine learning use. 

Certification exams often test a combination of practical abilities, such as writing efficient SQL queries, and conceptual knowledge about data architecture, security, and integration. Earning a data engineering certification usually involves passing one or more rigorous assessments that cover both theoretical and applied aspects of the discipline. Topics can range from fundamental programming abilities to complex concepts like distributed computing and real-time data processing.

### Why Should You Pursue a Data Engineering Certification? 

Earning a data engineering certification can lead to practical and strategic advantages for both individuals and organizations. It helps engineers validate their skills, stay current with evolving technologies, and signal competence to employers in a highly competitive job market.

Key benefits include:

- **Career advancement**: Certifications can qualify candidates for more advanced roles or specialized positions in data architecture, platform engineering, or analytics engineering.<br><br>
- **Skill validation**: A recognized credential proves your capability with essential tools and frameworks, such as SQL, Python, Spark, or cloud data services, without needing on-the-job proof.<br><br>
- **Higher earning potential**: Certified professionals often command higher salaries due to the demonstrated expertise and commitment to continued learning.<br><br>
- **Industry recognition**: Certifications from reputable organizations (like Google, AWS, or Microsoft) enhance credibility and visibility in the tech community.<br><br>
- **Up-to-date knowledge**: Preparing for certification requires learning current best practices, tools, and frameworks, helping engineers stay aligned with industry standards.<br><br>
- **Improved hiring prospects**: Recruiters and hiring managers may use certifications as a filter when shortlisting candidates, making them an asset in competitive hiring environments.<br><br>
- **Employer confidence**: For companies, certified employees reduce training costs and accelerate deployment of data initiatives due to proven competencies.<br>

### Popular Data Engineering Certifications 

#### 1. Google Cloud: Professional Data Engineer

The *Professional Data Engineer* certification from Google Cloud assesses a candidate’s ability to design, build, and maintain scalable data systems using Google Cloud technologies. The certification targets engineers who are responsible for making data usable and valuable through collection, transformation, analysis, and secure publication.

**The exam covers:**

1. **Data design considerations:** Candidates must understand how to design data processing systems with considerations for security, reliability, compliance, and portability. 
2. **Data ingestion and processing skills:** Candidates should be able to plan, build, and deploy pipelines using tools like Dataflow, Apache Beam, and BigQuery. Both batch and streaming architectures are covered, along with orchestration using Cloud Composer.
3. **Data storage knowledge:** Candidates must be able to select the appropriate storage solutions (e.g., Bigtable, Spanner, Cloud Storage) based on access patterns and performance needs, and design data models.
4. **Data preparation:** This area focuses on preparing data for analysis, including visualization, data sharing, and feature engineering.
5. **Maintaining and automating workloads:** This includes resource optimization, job scheduling, monitoring pipelines, and designing for fault tolerance using Google Cloud’s observability and automation services.

**Training options:**

- **Official learning path**: Google Cloud provides a structured learning path that includes online courses, labs, and hands-on resources specifically designed to cover exam topics.
- **Sample questions and exam guides**: Candidates can review publicly available sample questions and detailed exam guides to understand the exam format and scope.
- **Partner-led training**: Authorized Google Cloud training partners offer instructor-led courses to help reinforce concepts through interactive sessions.
- **Webinars and community resources**: Google also hosts exam preparation webinars and provides access to a learning community for peer support and shared resources.<br>

**Other details:**

This certification is valid for two years. It does not have formal prerequisites, but Google recommends at least three years of industry experience, including one year working with Google Cloud solutions. The exam consists of 40–50 multiple-choice and multiple-select questions and can be taken online or at a testing center. A renewal exam option is available for recertifying candidates.

#### 2. Microsoft: Data Engineering on Microsoft Azure

The *Data Engineering on Microsoft Azure* certification (DP-203) validates a candidate's ability to design and implement data solutions using Microsoft Azure's suite of services. It focuses on core data engineering responsibilities, including building scalable pipelines, transforming raw data, and enabling analytics through Azure-based platforms.

**The exam covers:**

- **Managing data lakes**: Focuses on storing and organizing large volumes of structured and unstructured data using Azure Data Lake Storage Gen2.
- **Building data warehouses**: Covers designing and implementing scalable relational data warehouses using Azure Synapse Analytics.
- **Orchestrating data workflows**: Assesses the ability to build and manage data pipelines using Synapse pipelines for batch and real-time processing.
- **Working with real-time data**: Tests the use of Azure Stream Analytics to capture, process, and aggregate data streams in real time.
- **Processing big data**: Validates skills in using Azure Databricks and Apache Spark for distributed data processing and transformation at scale.

**Training options:**

- **Instructor-led course**: A four-day classroom training (DP-203T00-A) is available for professionals who prefer structured, guided learning.
- **Modular self-paced paths**: Learners can follow curated online paths that focus on different workloads, such as Synapse SQL pools, Apache Spark, and end-to-end pipeline development.
- **Hands-on labs and exercises**: Training includes practical modules on orchestrating workflows, building data lakes, working with real-time data, and tracking data lineage.
- **Language and format options**: Content is available in multiple languages and formats to support global learners and flexible schedules.<br>

**Other details:**

The DP-203 certification has no mandatory prerequisites, but familiarity with Azure services and data solutions is recommended. The exam costs approximately $165 (varies by location), is 100–120 minutes long, and consists of multiple-choice questions. The certification remains valid unless Microsoft retires or updates the exam objectives.

#### 3. AWS: Certified Data Engineer

The *AWS Certified Data Engineer: Associate* certification verifies the ability to build and manage data pipelines using AWS services. It focuses on key tasks such as ingesting, transforming, and orchestrating data, while also validating skills in data modeling, lifecycle management, and maintaining data quality.

This certification targets individuals with 2–3 years of experience in data engineering or architecture and at least one year of hands-on AWS experience. It is designed for those building scalable, reliable data solutions in the cloud using AWS tools.

**The exam covers:**

- **Exam format and duration**: The exam consists of 65 multiple-choice or multiple-response questions and must be completed within 130 minutes.
- **Core AWS data services**: Tests knowledge of essential AWS services used for data engineering and how they fit into end-to-end data solutions.
- **Data ingestion and transformation**: Assesses the ability to ingest data from different sources, apply transformations, and prepare data for downstream use.
- **Pipeline orchestration**: Evaluates skills in orchestrating data pipelines while applying appropriate programming concepts.
- **Data modeling**: Covers designing data models that support analytics and efficient querying.
- **Data lifecycle management**: Tests understanding of managing data across its lifecycle, including storage, retention, and organization strategies.
- **Data quality assurance**: Validates the ability to ensure data accuracy, consistency, and reliability within data pipelines.

<strong>Training options: </strong>

- **Skill Builder exam plan**: AWS offers a structured four-step plan through its Skill Builder platform, including digital courses, labs, and practice exams.
- **Practice questions and pretest**: Official sample questions and a diagnostic pretest help candidates identify gaps and align their study focus.
- **Hands-on labs and AWS Jam**: Candidates can strengthen skills through interactive labs and challenge-based learning environments like AWS Cloud Quest and AWS Jam.
- **Exam readiness walkthroughs**: Instructors explain exam topics and strategies using walkthroughs of test-style questions and domain reviews.

**Other details:**

This associate-level exam is intended for those with 2–3 years of data engineering experience and at least one year using AWS services. The exam lasts 130 minutes and includes 65 multiple-choice or multiple-response questions. It costs $150 and is available in English, Japanese, Korean, and Simplified Chinese. Certification is valid for three years and serves as a foundation for advanced AWS certifications.

#### 4. Databricks Certified Data Engineer

The *Databricks Certified Data Engineer Associate* certification validates foundational skills in using the Databricks Data Intelligence Platform to perform key data engineering tasks. It is aimed at early-career engineers or practitioners looking to demonstrate proficiency with Databricks, Spark SQL, and workflow orchestration within a cloud-native environment.

**The exam covers:**

- **Databricks Intelligence Platform (10%)**: Tests understanding of the platform’s architecture, workspace structure, and core capabilities.
- **Development and ingestion (30%)**: Assesses the ability to ingest data into Databricks using various tools and interfaces, and to develop pipelines using Spark SQL or PySpark.
- **Data processing & transformations (31%)**: Focuses on transformation logic, handling complex data, and implementing user-defined functions (UDFs).
- **Productionizing data pipelines (18%)**: Evaluates skills in scheduling, deploying, and monitoring data workflows using Databricks Workflows and LakeFlow Jobs.
- **Data governance & quality (11%)**: Covers working with Unity Catalog for governance and applying quality controls in pipelines.<br>

**Training options:**

- **Self-paced Databricks Academy courses**: Includes modules like *Data Ingestion with Lakeflow Connect*, *Build Data Pipelines with Lakeflow Spark Declarative Pipelines*, and *Data Management with Unity Catalog*.
- **Instructor-led training**: Databricks offers a classroom-style course titled *Data Engineering With Databricks* for more structured learning.
- **Exam guide review**: The official exam guide outlines required skills and provides a roadmap for preparation and recertification.
- **Language and format flexibility**: Training and exams are available in English, Japanese, Portuguese (BR), and Korean, with both online and test center options.

**Other details:**

The certification exam includes 45 multiple-choice questions to be completed in 90 minutes. The cost is $200. While there are no official prerequisites, Databricks recommends at least six months of experience with the platform. The credential is valid for two years, and recertification requires passing the updated version of the exam.

#### 5. Datacamp Data Engineer Certification

The DataCamp Data Engineer Certification validates foundational skills in data engineering for entry-level and early-career professionals. It focuses on practical tasks across the data lifecycle, such as extracting, cleaning, validating, and transforming data using SQL and Python. The certification is designed to demonstrate that candidates can prepare data in formats suitable for analysis and business use.

**The exam process consists of two parts:**

Timed exams

- **Data management theory**: Understand relational databases, normalization, schema design, and data storage options
- **SQL data management**: Write queries to extract, join, aggregate, and clean data for analysis
- **Exploratory analysis theory**: Demonstrate the ability to assess data quality, validate datasets, and visualize key relationships
- **Cloud tools and pipeline operations**: Identify tools used for cloud-based data storage and pipeline maintenance<br>

Practical exam

- **SQL problem-solving**: Complete tasks to extract, join, aggregate, and validate data in response to a business scenario
- **Data preparation**: Clean and prepare data for downstream users while assessing its quality and integrity

#### **Training options:**

- **SQL track**: A self-paced course covering database design, data warehousing, and tools like PostgreSQL and Snowflake, totaling about 30 hours.
- **Python track**: Focuses on writing production-grade code, debugging, data ingestion, and pipeline management over approximately 40 hours.
- **Readiness quiz and sample exams**: Candidates can assess their preparation with quizzes and review sample tasks before taking the timed or practical exams.
- **Integrated feedback**: The platform provides instant feedback during the practical component, helping candidates learn from their results.

**Other details:**

DataCamp’s certification is geared toward entry-level professionals and includes both a timed theory exam and a hands-on practical exam. Candidates have 30 days from registration to complete all components and receive two attempts per exam. The certification does not expire and is included with a DataCamp Premium membership ($25/month at the time of writing).

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data engineering tools</em></strong>](https://dagster.io/guides/data-engineering-tools-types-features-and-10-essential-tools-xsde6)

### Tips for Success in Data Engineering Certifications 

#### 1. Understand the Exam Blueprint and Scope

Success in data engineering certification exams begins with a comprehensive understanding of the exam blueprint and scope. Each exam outline details specific domains, skill sets, and technologies to master. Reviewing official blueprints helps candidates identify focus areas, allocate study time strategically, and avoid spending excessive effort on less relevant topics. This structured approach reduces surprises during the actual assessment.

Familiarity with exam objectives also clarifies the expected balance between theoretical and practical knowledge. Many data engineering exams weigh hands-on tasks and scenario-based questions heavily, so understanding the skill emphasis helps set realistic preparation plans. Investing time in understanding the blueprint leads to more efficient, targeted study and higher chances of passing on the first attempt.

#### 2. Use Official Documentation and Study Resources

Official documentation and study guides are indispensable preparation tools for certification candidates. Vendor-provided resources, such as product manuals, reference architectures, and whitepapers, ensure that candidates are learning the technologies as intended by their creators. Accessing up-to-date documentation is especially vital in cloud platforms, where services and capabilities can change rapidly.

Supplementing documentation with curated courses, official sample questions, and instructor-led training aids in reinforcing key concepts. Many certification programs also offer learning paths and skill assessments tailored to the exam. Dedicating focused time to these core resources, rather than cobbling together disparate tutorials, streamlines preparation and improves familiarity with how exam questions are framed.

#### 3. Practice with Hands-On Labs and Cloud Sandboxes

Practical experience is essential for mastering the material covered in data engineering certifications. Most exams require demonstration of real-world skills in building, maintaining, and troubleshooting data solutions. Hands-on labs, where candidates interact directly with cloud consoles, big data frameworks, or pipeline tools, bridge the gap between theory and practical ability.

Cloud sandboxes provided by certification providers allow safe experimentation with various configurations, services, and workflow designs. Practicing within these environments exposes candidates to common pitfalls, resource limitations, and task automation. These exercises build confidence, speed, and a mental library of troubleshooting strategies essential for high performance during the live exam.

#### 4. Validate Knowledge with Capstone Projects

Completing capstone projects as part of exam preparation tests both depth and breadth of data engineering skills. Such projects involve architecting and deploying end-to-end pipelines, integrating multiple technologies, and addressing real business problems. Capstones typically require researching best practices, documenting designs, and evaluating trade-offs in tool selection or data modeling.

These comprehensive projects expose knowledge gaps and strengthen understanding by forcing engineers to apply concepts in practice. Reflection on project outcomes, challenges faced, and iterative improvements sharpens critical thinking. Showcasing completed capstone projects in portfolios can also provide tangible evidence of expertise for potential employers beyond certification credentials alone.

#### 5. Join Communities and Use Peer Feedback

Active participation in data engineering communities provides a wealth of informal learning opportunities. Forums, online study groups, and peer networks offer real-world exam insights, commonly overlooked topics, and shared resources. Engaging with others preparing for the same certifications helps clarify complex concepts and introduces varied problem-solving approaches.

Requesting feedback on sample solutions or discussing challenging practice questions provides valuable external perspective. Many community members share their recent exam experiences or updates, aiding in keeping preparation material current. This collaborative approach not only enhances individual understanding but also builds a professional network that can support ongoing career growth.

### Dagster for Certified Data Engineers

Dagster is an increasingly important part of the modern data engineering stack, especially for teams building reliable, production-grade data pipelines. As organizations move beyond simple ETL tools toward more observable, testable, and maintainable data platforms, Dagster’s software-defined approach to orchestration has become a key skill for certified data engineers.

<strong>Why Dagster Matters for Certified Engineers<br></strong>Data engineering certifications often validate knowledge of storage engines, processing frameworks, and cloud services. Dagster builds on that foundation by focusing on the operational layer:

- **Production-grade orchestration:** Define pipelines in Python with clear dependencies, schedules, sensors, and retries.
- **Data asset–centric modeling:** Manage pipelines around data assets rather than just tasks, aligning more closely with analytics and ML workflows.
- **Observability and debugging:** Built-in lineage, logging, and asset health monitoring help engineers diagnose failures faster.
- **Testing and reliability:** Native support for unit tests and local development environments improves pipeline quality before deployment.
- **Team scalability:** Dagster enables collaboration through code reviews, modular pipeline design, and environment parity across dev, staging, and production.

These capabilities are especially relevant for engineers who already hold certifications from AWS, GCP, Azure, or Databricks and want to demonstrate practical, real-world pipeline ownership.

<strong>Dagster Certification Program<br></strong>Dagster offers an official certification through its training platform: <strong>Dagster University</strong> (<https://courses.dagster.io/>). The Dagster certification is designed to validate hands-on proficiency with Dagster concepts and workflows, rather than abstract theory.

The certification focuses on:

- Core Dagster concepts: assets, jobs, schedules, and sensors
- Asset-based data modeling and dependency management
- Building, testing, and deploying pipelines using Dagster
- Observability, logging, and debugging in Dagster UI
- Best practices for structuring production data platforms

<strong>Training Format and Preparation<br></strong>Dagster University provides self-paced, hands-on courses created by the Dagster team. Training emphasizes practical application, with real examples that mirror production use cases rather than toy pipelines. Engineers learn how to integrate Dagster into existing stacks that include cloud warehouses, transformation tools, and compute engines.

Because the certification is tool-specific and workflow-driven, it pairs well with broader vendor certifications. For example, an AWS or GCP certified data engineer can use Dagster certification to show they know how to orchestrate and operate pipelines on top of those services in a maintainable way.

<strong>Who Should Consider Dagster Certification<br></strong>The Dagster certification is particularly valuable for:

- Data engineers responsible for owning production pipelines end to end
- Analytics engineers transitioning into more platform-oriented roles
- Platform teams standardizing orchestration across multiple tools
- Certified cloud data engineers looking to deepen operational expertise

<strong>How Dagster Complements Other Certifications<br></strong>While cloud and platform certifications validate <em>what</em> services to use, Dagster certification demonstrates <em>how</em> to reliably connect, operate, and scale them in practice. Together, they signal a well-rounded data engineer who understands both infrastructure and day-to-day pipeline reliability.

For engineers aiming to stand out, combining a major cloud certification with Dagster certification highlights a critical, in-demand skill set: building data platforms that actually work in production.

---

## Top 8 Data Quality Issues & 4 Ways to Fix Them

> Common data quality issues include inaccurate, incomplete, and duplicate data, as well as outdated, inconsistent, and irrelevant information. These problems can result from human error, poor data governance, and a lack of standardized processes, leading to unreliable analysis.

Source: https://dagster.io/learn/data-quality-issues

### What Is Data Quality? 

[Data quality](https://dagster.io/learn/data-quality) refers to the degree to which data serves its intended purpose in a given context. Data quality issues can result from human error, poor data governance, and a lack of standardized processes, leading to unreliable analysis and poor decision-making.

Common types of data quality issues include:

1. **Inaccurate data:** Information that is incorrect or doesn't properly represent the real-world situation. 
2. **Incomplete data:** Missing essential records, attributes, or fields. 
3. **Duplicate data:** The same information is recorded more than once, skewing analysis. 
4. **Outdated data:** Information that is no longer current or accurate because it hasn't been updated. 
5. **Inconsistent data:** Data that is not standardized in format, naming conventions, or values across different sources. 
6. **Irrelevant data:** Data that does not contribute to the specific analysis being conducted. 
7. **Unstructured data:** Data that is not organized in a predefined format. Fraudulent Data: Intentionally falsified data.
8. **Orphaned or dark data:** Data that is detached from the context necessary to understand it.

Causes of data quality issues include:

- **Human error:** Mistakes made during data entry or processing. 
- **Poor data governance:** A lack of clear ownership, rules, and standards for data management. 
- **Data integration challenges:** Problems integrating data from different, disconnected systems. 
- **Siloed systems:** Issues related to fragmentation.
- **Metadata and lineage tracking issues:** Missing or incomplete information about the data.
- **Insufficient validation:** Outdated technology may not have robust data validation capabilities.

**Volume overload:** Too much data makes it difficult to monitor and maintain quality.

### The Business and Operational Impact of Poor Data Quality 

#### Financial Losses and Misreporting

Poor data quality directly leads to financial losses. For example, inaccurate billing data or incorrect customer information can result in payment disputes, delayed revenue, or undetected fraud. Erroneous financial or operational data can compromise forecasting, budgeting, and reporting, causing management to make decisions based on flawed assumptions.

Additionally, these errors can cascade throughout the organization. When reports or analytics are based on low-quality data, stakeholders are at risk of making strategic mistakes, such as pursuing the wrong market, overinvesting in unprofitable areas, or underestimating operational costs. The combined effect can quickly translate into reduced profitability, diminished shareholder trust, and long-term damage to business performance.

#### Operational Inefficiencies

When data quality is poor, daily operations become less efficient. Employees spend excessive time searching for correct data, reconciling records, or validating conflicting sources. This manual overhead reduces productivity and can introduce further errors, amplifying the original issue throughout processes such as supply chain management, order fulfillment, and customer service.

Operational inefficiencies can also increase internal costs and negatively affect customer satisfaction. Inconsistent or missing data may lead to shipping errors, delayed orders, and poor experiences that erode brand loyalty. Over time, organizations find themselves allocating increasing resources to firefighting data issues instead of innovating or scaling operations.

#### Compliance and Regulatory Risks

Regulatory compliance hinges on accurate data reporting. In industries such as finance, healthcare, or consumer services, providing erroneous data can lead to fines, audits, or legal action from regulatory authorities. This risk is exacerbated when systems cannot reliably trace data lineage or track changes, making it difficult to demonstrate compliance during investigations.

Beyond the immediate threat of penalties, compliance violations can tarnish a company’s reputation. Public disclosure of data management failures often shakes customer and stakeholder confidence, impacting market value and complicating partnerships. For regulated industries, persistent data quality issues may result in restricted operations or revoked licenses.

#### Erosion of Trust in Analytics and AI

Analytics and AI require high-quality data inputs. If underlying datasets contain errors, biases, or inconsistencies, the outputs of models and dashboards become unreliable or even harmful. Decision-makers may lose trust in analytics results, causing them to revert to intuition or manual processes.

This erosion of trust extends to external partners and customers. When organizations launch data-driven services built on poor-quality input, product recommendations, personalization, and predictions can go awry, undermining user satisfaction. In critical scenarios, such as fraud detection or medical diagnosis, these failures carry significant operational and ethical consequences.

### Common Types of Data Quality Issues 

#### 1. Inaccurate or Outdated Data

Inaccurate data deviates from the real-world state it is supposed to represent, often due to manual input errors, system glitches, or flawed data migration processes. Outdated data occurs when records are not updated in a timely fashion to reflect changes such as customer contact details, product availability, or compliance requirements.

Both inaccuracy and staleness undermine trust in enterprise data assets. They can result in failed outreach, misinformed decisions, and operational mistakes, such as shipping products to the wrong addresses or approving loans for ineligible applicants. Systematic updating, validation, and timely synchronization are essential to reduce these risks.

#### 2. Incomplete or Missing Data

Incomplete data refers to records that lack required fields or essential details. Missing information can occur at the point of capture, such as customers skipping form fields, or through system integrations that lose data due to mismatched schemas. This lack compromises the utility of datasets, whether for operational needs or analytical insights.

The downstream effects are substantial. Incomplete records limit segmentation, hamper personalization efforts, and force teams to rely on guesswork or costly manual enrichment tasks. In regulated environments, missing required data elements could also result in compliance breaches or reporting gaps.

#### 3. Duplicate Data

Duplicate data occurs when the same record, entity, or transaction appears multiple times within a dataset, often with slight variations or conflicting details. Duplicates can arise from user error, inconsistent data entry standards, system integration mishaps, or batch imports. If not addressed, duplication leads to inflated metrics, skewed analyses, and wasteful resource allocation.

Moreover, duplicate records complicate downstream processes such as deduplication during email marketing, loyalty program management, or customer support case handling. Merging or deleting duplicates is often labor-intensive and risky, potentially resulting in the accidental loss of important information or the persistence of unresolved conflicts.

#### 4. Outdated Data

Outdated data refers to information that has not been refreshed or updated to reflect current conditions. This often includes expired customer records, obsolete pricing, or legacy configurations still active in production. The risk increases when systems lack triggers for time-based updates or dependencies on external data sources go unmonitored.

Stale data undermines time-sensitive processes such as compliance reporting, marketing segmentation, or real-time decision-making. For instance, using outdated regulatory codes can result in audit failures, while outdated customer preferences may lead to irrelevant recommendations or communication errors. Scheduled refresh cycles, timestamp tracking, and data expiration policies are essential safeguards.

#### 5. Inconsistent or Conflicting Data

Inconsistency occurs when the same attribute or entity has different values in different systems or records. Conflicts arise due to varied data entry conventions, synchronization failures across databases, or different integration points applying their own business logic. As a result, teams waste time resolving discrepancies and cannot confidently rely on reports or analytics.

Such inconsistencies impede data-driven initiatives by creating uncertainty and confusion. For example, conflicting customer addresses or product codes can obstruct unified views, disrupt supply chains, or result in duplicate communications, degrading both internal efficiency and customer experience.

#### 6. Irrelevant Data

Irrelevant data includes information that does not serve a current business purpose or analytical objective. Even if technically accurate, such data clutters systems, distracts analysis, and increases processing overhead. For example, collecting browser type for a process that only requires transactional data can introduce unnecessary complexity without adding business value.

The presence of irrelevant data often stems from unclear data collection goals or overly broad integrations. Regular reviews of data usage help identify and retire unused fields, improving performance, simplifying models, and enhancing the signal-to-noise ratio in analytics.

#### 7. Ambiguous or Unstructured Data

Ambiguous data denotes fields that lack clear meaning, definition, or standardized formatting, such as notes fields, free-text comments, or codes without documentation. Unstructured data (text, images, PDFs, etc.) can be even harder to interpret, categorize, or leverage without advanced processing.

The challenges with ambiguity or unstructured formats are twofold: analysis becomes difficult, and automated systems struggle to integrate or validate such inputs. Without clear reference, mistakes multiply as data is misused across analytics, compliance, or automation projects, leading to unreliable results and wasted effort.

#### 8. Orphaned or Dark Data

Orphaned data consists of records that have lost their contextual relationship, such as transactions without associated customers or logs whose owners have left the organization. Dark data refers to stored but unused information that remains unindexed or hidden, clogging up storage and masking compliance risks.

Both types represent liabilities as well as missed opportunities. Orphaned data may confuse analytics or operational processes, while dark data increases storage costs, security exposure, and potential for regulatory violations. Proper archiving, cataloging, and regular review are needed to reclaim value and mitigate risks.

### Root Causes of Data Quality Problems 

#### Human Error and Manual Entry Issues

Manual data entry remains a leading source of data quality problems. Typographical mistakes, incomplete fields, misclassifications, and subjective judgment calls can introduce wide-ranging errors, especially where users are not adequately trained or processes lack validation and review.

High rates of manual intervention slow down operations and are prone to cumulative errors across complex workflows. The risks multiply as organizations scale, underscoring the need for automation, better user interface design, and clear data ownership to reduce dependency on manual data handling.

#### Poor Data Governance and Stewardship

Weak data governance leads directly to problems with quality. Without policies, roles, and procedures for managing data assets, organizations struggle to maintain consistent standards. This causes data to be entered, handled, and interpreted in ad hoc ways that amplify risk and ambiguity.

Effective governance requires active stewardship by professionals who own processes for data creation, validation, monitoring, and correction. When stewardship is lacking, data quality issues persist, propagate, and eventually become embedded in systems and analytic outputs.

#### Data Integration Challenges and Flawed ETL Processes

Extract, transform, load (ETL) and other integration processes are sources of data quality troubles if poorly designed or inadequately monitored. Mistakes in data extraction, incorrect transformations, and unvalidated loads corrupt downstream tables. Overly complex workflows can introduce hard-to-detect errors that snowball through aggregated datasets.

Regular auditing, clear mappings, and robust error handling are necessary to limit this risk. Without such controls, flawed ETL pipelines spread inaccuracies quickly, undermining confidence in the very systems meant to organize and mature organizational data resources.

#### Siloed Systems and Fragmented Architecture

Siloed systems, where data lives in isolated platforms or departmental databases, create fragmented architecture. These silos make it difficult to reconcile records, enforce standardized definitions, and maintain consistency across the enterprise. As integrations proliferate, mapping between structures or resolving conflicts becomes more complex.

Such fragmentation hinders efforts to build comprehensive data views, complicates analytics, and slows down root cause investigation for errors. Enterprises must prioritize integration strategies, shared architecture, and centralized oversight to combat the proliferation of disconnected data silos.

#### Lack of Metadata and Lineage Tracking

Metadata (information describing data assets, transformations, and context) aids in understanding and governing data. When metadata is absent or incomplete, users cannot assess data provenance, meaning, or validity. This also makes it hard to troubleshoot issues, interpret analytics, or ensure regulatory compliance.

Likewise, tracking data lineage is essential for tracing discrepancies back to source systems or processes. Absent or deficient lineage hinders root cause analysis, making data quality problems harder to fix and increasing the chances of repeated errors or oversight in critical systems.

#### Insufficient Validation Rules or Schema Drift

Insufficient or outdated validation rules allow incorrect data to enter and persist in business systems. Without constraints, fields may accept invalid, incomplete, or out-of-range values, passing problems along the pipeline instead of flagging them for review. Schema drift, where data structures change over time without coordinated control, introduces unpredictability and misalignments.

Both issues lead to unreliable results and require proactive management. Automated validation at ingestion, coupled with rigorous change management and schema governance, helps ensure that evolution in data systems does not create hidden gaps in quality or weaken trust in outputs.

#### Volume Overload

Volume overload occurs when the sheer quantity of data surpasses an organization’s ability to manage, process, or validate it effectively. As data volumes grow exponentially, teams may struggle to enforce quality checks at scale. Traditional validation methods often become too slow or resource-intensive, allowing poor-quality data to accumulate undetected. 

The result is degraded trust in data assets, slower analytics pipelines, and growing storage costs with diminishing value. High-volume environments also increase the likelihood of data drift, anomalies, and inconsistencies, especially when data is ingested from multiple sources without centralized oversight. 

### 4 Approaches to Detecting and Measuring Data Quality 

#### 1. Quality Metrics and Indicators

Key metrics for data quality include accuracy, completeness, consistency, timeliness, uniqueness, and validity. Organizations often monitor error rates, missing values, duplication rates, and deviation from defined standards as leading indicators. Establishing measurable thresholds allows teams to spot trouble quickly and quantify improvements over time.

For effective measurement, it’s critical to align these metrics with business priorities. For instance, missing contact numbers may be more critical in customer service contexts, while outdated compliance flags may be significant in regulated industries. This targeted approach ensures that resources are allocated to areas of greatest risk.

#### 2. Data Profiling and Auditing Techniques

Data profiling involves systematically analyzing datasets for patterns, anomalies, and potential quality flaws. Tools for profiling generate insight into value ranges, format deviations, duplication, and distribution, making it easier to spot errors before they propagate. Auditing involves periodic review of data lineage, validation logs, and system change histories to ensure compliance and accuracy.

Routine profiling and auditing form the backbone of a strong data quality assessment program. Automated tools can scan large datasets, identify trends or outliers, and present actionable summaries, while manual audits help bridge contextual gaps or validate automated findings for critical business operations.

#### 3. Automated Quality Tests and Freshness Alerts

Automated data quality tests can validate schema conformity, detect duplicates, flag missing values, and check for out-of-range or illogical entries. These tests can be embedded in ETL pipelines or triggered at regular intervals, rapidly surfacing issues for correction. Automated freshness alerts monitor when data assets were last updated, ensuring timely awareness of potential staleness or pipeline breakage.

These mechanisms reduce reliance on manual oversight and allow for immediate remediation. By catching issues early, teams prevent propagation into downstream analytics, operational processes, or regulatory reporting. This automation increases both the scale and [reliability of data](https://dagster.io/guides/data-reliability-challenges-measurement-best-practices) quality management.

#### 4. Using Observability Tools for Real-Time Monitoring

[Data observability](https://dagster.io/guides/data-observability-in-2025-pillars-pros-cons-best-practices) tools provide real-time measurement of data pipeline health, quality, and lineage. They offer dashboards, anomaly detection, and notifications when quality metrics deviate from defined norms. Observability goes beyond monitoring static datasets, capturing operational events, pipeline latency, and transformation errors as they occur.

Such tools allow DevOps teams and data engineers to react quickly to emerging issues and identify systemic weaknesses in architecture or process. Real-time observability is essential for mission-critical environments where delays in detection could result in erroneous business outcomes or regulatory violations.

### Best Practices for Preventing and Resolving Data Quality Issues

Here are some of the ways that organizations can better address data quality issues.

#### 1. Establish Clear Data Ownership and Governance

Defining data ownership is foundational to building accountability. Assigning responsibility for specific data assets, processes, and domains ensures that quality issues have clear escalation paths and stewardship. Good governance frameworks specify roles, outline policies for access and quality control, and formalize review procedures.

This approach fosters a culture where data quality is a shared responsibility, not an afterthought or relegated to IT alone. By aligning incentives and establishing formal governance bodies or data councils, organizations can sustainably elevate the maturity of their data management practices.

#### 2. Implement Continuous Data Validation

Continuous data validation applies rules and checks at every ingestion, transformation, and consumption point in data flows. Automated validation engines ensure that only conformant data enters production environments. Regular re-validation, especially for master or reference data, identifies emergent issues caused by changes in source systems or external dependencies.

This process reduces the chance of data drift, prevents the accumulation of technical debt, and shortens the feedback loop for error correction. Continuous validation supports scaling efforts since it minimizes manual workload and maintains trust as data volumes and velocity increase.

#### 3. Build Automated Quality Pipelines in ETL/ELT

Embedding automated quality checks in ETL (extract, transform, load) or ELT (extract, load, transform) pipelines ensures that data is assessed throughout its journey. These pipelines can validate structure, check referential integrity, and log issues for later resolution. Automated cleansing operations, such as deduplication or standardization, can also be executed inline.

Automated quality pipelines not only prevent “garbage in, garbage out” scenarios but also enable root cause analysis when anomalies are detected. Secure, repeatable, and auditable pipelines help organizations scale data operations without losing visibility or oversight.

#### 4. Integrate Data Lineage and Catalog Tools

Data lineage tools trace the journey of data throughout organizational systems, allowing teams to track provenance, transformations, and usage. Catalog tools augment this by making data assets discoverable, annotated, and enriched with business context and ownership information. Together, these solutions improve transparency and trust.

Integration with analytics, ETL, and monitoring systems empowers users to quickly find issues, understand data context, and implement fixes efficiently. Data lineage and cataloging accelerate compliance initiatives by offering a clear audit trail and simplifying impact analysis during change management or incident resolution.

#### 5. Foster a Culture of Data Literacy and Accountability

Promoting data literacy throughout the organization ensures that employees understand the significance, context, and uses of the data they work with. Training and resources should address both the “why” and “how” of quality: from recognizing suspicious entries to participating in continuous improvement initiatives.

Accountability is reinforced by clear roles, visible metrics, and transparent reporting. Building this culture encourages proactive reporting of issues, open dialogue between teams, and cross-functional participation in quality assurance processes.

### Improving Data Quality with Dagster

Dagster helps teams improve data quality by making quality checks, validation, and observability a core part of how data pipelines are built and operated. Instead of treating data quality as a downstream concern, Dagster encourages teams to define expectations, tests, and ownership directly alongside the pipelines that produce data. This shifts quality management earlier in the lifecycle, where issues are cheaper and easier to fix.

With Dagster, data quality checks can be implemented as first-class logic within pipelines. Teams can validate schemas, enforce business rules, check freshness, and detect anomalies at every stage of ingestion and transformation. These checks run automatically as part of pipeline execution, ensuring that bad or incomplete data is caught before it reaches analytics, machine learning models, or operational systems.

Dagster’s asset-based model makes ownership and lineage explicit, which is critical for diagnosing and resolving quality issues. Each data asset clearly defines its upstream dependencies and downstream consumers, allowing teams to quickly understand the impact of failures or inconsistencies. When a quality check fails, Dagster surfaces rich metadata, logs, and context that help engineers trace problems back to their source instead of relying on manual investigation.

Operational visibility is another key advantage. Dagster provides built-in observability for data pipelines, including execution history, freshness tracking, and alerting. Teams can monitor whether data assets are updating on time, identify recurring quality failures, and receive notifications when pipelines drift from expected behavior. This supports proactive data quality management rather than reactive firefighting.

By combining automation, lineage, and observability, Dagster enables organizations to scale data quality practices alongside their data platforms. Teams can move beyond ad hoc validation scripts and manual checks to build reliable, testable, and auditable quality pipelines. The result is greater trust in data, faster issue resolution, and a stronger foundation for analytics, AI, and operational decision-making.

---

## Top 8 Data Quality Metrics in 2026

> Data quality metrics are quantifiable measures used to assess the condition of data within an organization. They help quantify data quality dimensions including accuracy, completeness, consistency, timeliness, uniqueness, and validity.

Source: https://dagster.io/learn/data-quality-metrics

### What Are Data Quality Metrics? 

[Data quality](https://dagster.io/learn/data-quality) metrics are quantifiable measures used to assess the condition of data within an organization. These metrics help determine how well data supports business operations, analytics, and decision-making processes. They help quantify data quality dimensions including accuracy, completeness, consistency, timeliness, uniqueness, and validity.

Here are some of the most important data quality metrics organizations should track:

1. **Data downtime**: Measures the total time data is delayed, unavailable, or untrustworthy due to pipeline or system failures. Frequent or prolonged downtime disrupts analytics and erodes trust in data-driven processes.
2. **Table health and uptime**: Tracks the operational status of key datasets by monitoring schema stability, partition completeness, and load success rates. Healthy tables support consistent analytics and reduce the risk of silent data failures.
3. **Duplicate record percentage**: Calculates the proportion of duplicate rows in a dataset. High duplication impacts reporting accuracy and operational processes like billing or customer communication.
4. **Data freshness**: Indicates how recently data has been updated. This metric ensures datasets reflect the latest real-world events, which is critical for time-sensitive decisions.
5. **Data transformation errors**: Counts failures in the data processing pipeline, such as type mismatches or logic errors. This metric helps identify fragile or poorly maintained transformation logic.
6. **Cost of poor data quality**: Estimates the financial impact of bad data, including remediation costs, lost revenue, and compliance penalties. It helps prioritize investment in quality improvement.
7. **Data time-to-value**: Measures the time it takes for new data to become available for business use. Shorter times improve agility and decision-making.

**Data pipeline incidents**: Tallies unplanned issues in data workflows that cause data delays or quality drops. Frequent incidents highlight instability and signal the need for engineering improvements.

### Why Data Quality Metrics Matter for Modern Data Teams 

As organizations scale their data infrastructure, the complexity and volume of data grow rapidly. Without structured ways to assess data quality, teams risk relying on flawed or inconsistent data, which can lead to costly decisions. Data quality metrics give modern data teams a practical framework to evaluate and improve the datasets they work with daily.

The importance of data quality metrics includes:

- **Ensure trust in analytics**: Metrics help confirm that dashboards and reports are based on accurate and consistent data, reducing the risk of misleading conclusions.
- **Improve operational efficiency**: By identifying data issues early, teams can prevent downstream errors and reduce the time spent on manual data correction.
- **Support regulatory compliance**: Many industries require data accuracy and traceability. Quality metrics help demonstrate compliance with data standards and audits.
- **Enable scalable data governance**: Metrics create a standardized way to monitor and enforce data policies across departments, systems, and teams.
- **Align technical and business teams**: With measurable criteria, both data engineers and business stakeholders can discuss data problems and expectations more clearly.
- **Support continuous improvement:** Tracking data quality over time reveals trends, helping teams prioritize improvements and measure the impact of data initiatives.

### Core Dimensions of Data Quality 

#### Accuracy

Accuracy measures how closely data values align with the true or accepted real-world values they are intended to represent. This dimension is typically verified through data validation methods, cross-referencing with trusted sources, or manual verification in critical cases. When data is accurate, it reliably reflects actual events, objects, measurements, or transactions, reducing the risk of misleading analysis or incorrect business decisions. Errors in accuracy can arise from manual entry, faulty integrations, or outdated data sources.

Inaccurate data leads to operational mistakes, skewed analytics, and erosion of stakeholder trust. For modern systems, maintaining high accuracy often requires integration with authoritative data feeds, automated checks, and periodic audits. Establishing accuracy thresholds, such as allowable error rates, informs teams when corrective action is required and helps set realistic expectations for analytics consumers.

#### Completeness

Completeness assesses whether all required data elements are present within a dataset. A record is considered complete if it contains all the mandatory fields, parameters, or attributes necessary for business processes or analytics. Completeness is particularly important for compliance, reporting, and transactional integrity, where missing values can invalidate entire workflows. 

Measuring completeness typically involves calculating the percentage of missing or null values for each critical field. Incomplete data can disrupt business operations, hinder analytical accuracy, and introduce bias into decision-making. For example, missing customer contact details affect sales follow-ups, while absent timestamps can invalidate time series analysis. 

#### Consistency

Consistency evaluates whether data is uniform and compatible across different systems, datasets, or records. Inconsistent data usually arises from integration errors, conflicting data standards, or uncoordinated data sources. This dimension is measured by identifying contradictions such as mismatched addresses for a single customer across multiple applications. 

Consistent data supports seamless information sharing between departments, accurate reporting, and smooth operation of automated workflows. To maintain consistency, organizations often use master data management solutions, enforce standard formats, and align business definitions. 

#### Timeliness

Timeliness measures how current and up-to-date data is with respect to its intended use. Outdated data can undermine decision-making, analysis, and operational tasks, particularly in fast-changing environments like financial markets or real-time supply chains. Timeliness is usually tracked by recording data arrival, ingestion, or refresh timestamps, and comparing these against predefined thresholds.

Data that fails timeliness checks might still be accurate and complete, but it lacks relevance due to staleness. This is especially problematic for use cases such as fraud detection or marketing personalization, where decisions rely on real-time input. Ensuring timeliness typically involves simplifying data pipelines, automating refresh cycles, and monitoring latency.

#### Uniqueness

Uniqueness refers to the absence of duplicate records or values in a dataset. Duplicate entries can skew reporting, inflate performance metrics, and cause confusion in operations such as order processing or customer support. Measuring uniqueness usually involves using key fields (like IDs) to identify occurrences of duplicate data and quantifying their prevalence within a table or dataset.

Maintaining high uniqueness is critical in environments with frequent data ingestion from multiple sources, as the risk of duplication increases. Deduplication tools, constraint enforcement at the database level, and regular health assessments can help ensure that datasets retain this core quality dimension. 

#### Validity

Validity assesses whether data values conform to predefined formats, rules, or business logic. Examples include ensuring a date field contains dates in the correct format or that a product code falls within an accepted range. Validity checks are enforced through validation rules at the point of entry or ingestion, and by applying ongoing monitoring via automated scripts or data quality tools.

Invalid data can result in failed transactions, application crashes, and misleading analytics. Data teams monitor validity by running regular tests and flagging records that fail to meet established criteria. Maintaining high validity is a foundational aspect of data quality, enabling organizations to process, analyze, and rely on their data without introducing unexpected downstream errors.

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>data quality checks</em></strong>](https://dagster.io/guides/data-quality-checks-how-to-test-6-data-quality-dimensions)

### Data Quality Metrics vs. Dimensions vs. KPIs 

While closely related, data quality metrics, dimensions, and key performance indicators (KPIs) serve different purposes in assessing and managing data quality.

- **Data quality dimensions** are qualitative categories that describe different aspects of data health, such as accuracy, completeness, and timeliness. These are conceptual groupings used to define what “good” data looks like.
- **Data quality metrics** are the specific, quantifiable measures derived from these dimensions. For instance, completeness as a dimension might be measured by the percentage of non-null values in a critical field. Metrics provide the numeric evidence needed to evaluate whether data meets quality standards.
- **KPIs** are business-aligned indicators that use data quality metrics to track the effectiveness of data quality initiatives over time. Unlike raw metrics, KPIs are tied to strategic goals. For example, a KPI might track whether customer record completeness exceeds 98% across all systems, a threshold determined by business needs.

In short, dimensions define *what* to measure, metrics show *how much*, and KPIs indicate *whether it's good enough*. Aligning these three helps organizations prioritize quality improvements and measure progress in a way that’s meaningful to both technical and business stakeholders.

### Key Data Quality Metrics Every Organization Should Track 

#### 1. Data Downtime

Data downtime refers to periods when data is unavailable, delayed, or untrustworthy due to outages, pipeline failures, or quality issues. Tracking the duration and frequency of data downtime enables organizations to quantify and minimize the business impact of unreliable data. Only by measuring downtime can teams understand the scope of disruption and prioritize initiatives that increase uptime and trust in business dashboards and analytics.

Mitigating data downtime requires instrumenting data pipelines with active monitoring and alerting systems that detect issues in real time. Logs, incident reports, and automated tests provide the inputs necessary for accurate reporting. 

#### 2. Table Health and Uptime

Table health and uptime metrics measure the operational availability and consistency of specific data tables or datasets. This includes checking for schema changes, missing partitions, or irregular load intervals that could affect reliability. Consistently monitoring these metrics allows teams to track infrastructure performance, maintain reliable analytics, and quickly respond to issues before stakeholders are affected.

Health checks typically run on a scheduled basis, providing early indicators of data degradation or source failures. Maintaining high uptime ensures that downstream applications and users can access needed information when they need it. 

#### 3. Duplicate Record Percentage

Duplicate record percentage quantifies the proportion of duplicate entries within a data set, often calculated as a percentage of total records. High duplication rates indicate problems with data ingestion, integration processes, or weak uniqueness constraints, leading to inaccurate reporting and operational inefficiencies. Regular monitoring helps detect new or recurring sources of duplication, allowing quick intervention.

To manage duplicate records, organizations use deduplication algorithms, enforce unique constraints, and design robust data entry protocols. Analyzing this metric over time can uncover underlying systemic issues, inform better integration strategies, and support better decision-making by ensuring consolidated, single-source views of business entities.

#### 4. Data Freshness

Data freshness measures how up-to-date a dataset is relative to real-world events or business needs. It's often expressed as the interval between the most recent data update and the current time, or the percentage of records updated within a target window. Freshness is especially important for environments with real-time analytics, operational dashboards, or compliance requirements.

Stale data can result in missed business opportunities or outdated insights. Automated freshness monitoring and alert systems can help maintain relevance, ensuring that teams make decisions based on the most recent available information. These systems provide a quantitative basis for evaluating the update frequency of business-critical datasets.

#### 5. Data Transformation Errors

Data transformation errors track the frequency and types of issues that arise during the data pipeline ETL (extract, transform, load) processes. Errors might include failed joins, schema mismatches, misapplied business logic, or corrupted files, all of which can compromise the reliability of analytics or downstream applications. Monitoring error rates and patterns provides visibility into pipeline stability and engineering debt.

Reducing transformation errors often requires robust data validation routines, thorough unit and integration testing, and continuous improvement of ETL scripts or data transformation logic. When transformation errors spike, root cause analysis can prioritize fixes and prevent similar issues in the future, resulting in more reliable and accurate data outputs.

#### 6. Cost of Poor Data Quality

The cost of poor data quality measures the direct and indirect expenses caused by incomplete, inaccurate, or unreliable data. This includes the cost of manual rework, lost opportunities, regulatory fines, and erosion of customer trust. By quantifying these impacts, organizations can make the business case to invest in data quality initiatives and monitor the return on those investments over time.

Calculating this metric requires cross-functional input, including IT costs, operational metrics, and business outcomes. Tracking these costs helps prioritize remediation and prevention efforts, ensuring that resource allocation aligns with the most critical quality issues. 

#### 7. Data Time-to-Value

Data time-to-value measures the elapsed time between data ingestion and when the data delivers actionable insights or business value. This metric captures delays in data preparation, transformation, and downstream consumption, highlighting process bottlenecks or inefficiencies. Shorter time-to-value accelerates innovation and allows organizations to react quickly to market or operational changes.

Improving this metric often involves automating manual steps, optimizing data pipelines, and aligning data delivery schedules with business priorities. Monitoring time-to-value helps reveal patterns in project delivery delays and fosters continuous process refinement. It also makes it easier to quantify the impact of data infrastructure investments.

#### 8. Data Pipeline Incidents

Data pipeline incidents count the number of unplanned disruptions, failures, or significant quality issues occurring in data processing workflows over a given period. Each incident can delay analytics delivery, undermine stakeholder confidence, or propagate erroneous data downstream. Tracking this metric provides insight into overall data pipeline health and reliability.

Frequent incidents signal the need for pipeline refactoring, improved monitoring, or better incident response processes. Documenting and analyzing incident patterns enables continuous learning and prevention, while helping data teams advocate for necessary tooling or engineering resources. 

### Best Practices for Managing Data Quality Metrics 

Here are some of the ways that organizations can make better use of various metrics to ensure the quality of their data.

#### 1. Define Clear Ownership and Accountability

Assigning clear data ownership and responsibility is fundamental to managing data quality metrics effectively. Each critical dataset, pipeline, or metric should have a designated owner accountable for monitoring and remediating quality issues. Clear accountability ensures swift resolution when problems arise and strengthens the culture of data stewardship across teams.

Ownership should be documented and communicated across the organization, with roles integrated into governance frameworks and performance expectations. Regular reviews with data owners, stakeholders, and leadership reinforce the importance of maintaining high standards and encourage proactive engagement in quality improvement initiatives.

#### 2. Embed Data Quality in Governance Policies

Integrating data quality standards into formal governance policies ensures consistent handling of quality metrics across the data lifecycle. Governance frameworks should define minimum acceptable thresholds for core quality dimensions, such as accuracy or timeliness, and embed monitoring procedures in data management processes.

Embedding these standards in policies aligns technology, process, and people, making enforcement consistent and scalable. Governance policies also clarify escalation paths and remediation steps when metrics fall below thresholds, ensuring that problems are addressed systematically rather than reactively.

#### 3. Automate Data Quality Monitoring at Scale

Scaling data quality monitoring requires automating metric collection, validation, and alerting using modern tooling and platforms. Automated monitoring reduces the burden on engineers, eliminates manual errors, and provides near real-time visibility into key metrics. Integration with CI/CD pipelines and data orchestration tools ensures checks are applied consistently from ingestion to consumption.

Automation enables rapid detection and resolution of emerging quality issues before they impact end users or downstream systems. By leveraging rule-based checks, anomaly detection, and automated reporting, organizations can monitor a far broader range of datasets, supporting data-driven growth and reliability goals.

#### 4. Integrate Quality Metrics into Business KPIs

Connecting data quality metrics to top-level business KPIs ensures that quality management directly supports enterprise objectives. For example, integrating data accuracy metrics into customer satisfaction tracking makes quality improvements visible to business stakeholders and aligns priorities between IT and business teams.

Regular joint reviews between business and data teams help align metric definitions, share progress, and identify emerging risks. This integration strengthens business cases for quality investments and ensures that improvements in data quality translate to measurable business value, such as reduced client churn or increased revenue.

#### 5. Continuously Refine and Evolve Quality Thresholds

Quality thresholds and targets should not remain static; they must evolve as business needs, technical capabilities, and data volumes change. Continuous refinement ensures that metrics remain relevant, actionable, and achievable. 

Regularly reviewing historical performance data, soliciting stakeholder feedback, and benchmarking against industry standards supports effective threshold management. This approach helps organizations avoid arbitrary targets and drive real improvement. Proactive threshold refinement sharpens focus on the most impactful metrics and ensures resource allocation stays aligned to organizational priorities.

### Improving Data Quality with Dagster

Dagster helps teams operationalize data quality metrics by embedding them directly into how data pipelines are defined, executed, and monitored. Instead of treating metrics like freshness, downtime, or incident counts as after-the-fact reports, Dagster enables teams to measure and enforce these signals as part of day-to-day data operations. This makes data quality metrics actionable rather than purely observational.

Using Dagster’s asset-based model, organizations can define expectations around table health, freshness, and transformation correctness at the level of individual datasets. Metrics such as data freshness, table uptime, and duplicate record checks can be evaluated automatically whenever assets are materialized. When expectations are not met, Dagster surfaces failures immediately with rich context, including logs, metadata, and upstream dependencies, so teams can diagnose issues quickly.

Dagster also supports tracking operational metrics like data downtime and pipeline incidents through built-in observability. Execution history, failure rates, and run-level metadata provide a clear record of how often pipelines break, how long data is unavailable, and where instability is concentrated. This visibility allows teams to trend reliability metrics over time and prioritize engineering work that reduces incidents and improves overall data trust.

For metrics like data time-to-value and cost of poor data quality, Dagster’s lineage and dependency graph help teams understand where delays and failures originate. By tracing how long data takes to move from ingestion through transformation to downstream consumers, teams can identify bottlenecks and quantify the operational impact of pipeline inefficiencies. This makes it easier to tie technical quality metrics back to business outcomes and KPIs.

By unifying orchestration, quality checks, lineage, and monitoring in a single platform, Dagster gives organizations a scalable way to manage data quality metrics across their entire data stack. Teams gain consistent measurement, faster incident response, and clearer accountability for data health, enabling them to build durable trust in the data that powers analytics, AI, and critical business decisions.

---

## Using dbt with Snowflake: Key Considerations & Quick Tutorial

> dbt connects directly to Snowflake and compiles transformation logic written in SQL and Jinja into executable SQL statements.

Source: https://dagster.io/learn/dbt-vs-snowflake

### How Is dbt Used with Snowflake? 

[dbt](https://dagster.io/learn/data-build-tool) connects directly to Snowflake and compiles transformation logic written in SQL and Jinja into executable SQL statements. These statements are then run on Snowflake, where the actual computation takes place. dbt uses Snowflake's compute resources to create and manage views or tables according to the project configuration.

A typical dbt project defines models as `.sql` files. When executed, dbt creates these models in a specified Snowflake schema, often organized by layers such as `staging`, `intermediate`, and `mart`. Developers can configure whether models are materialized as views, tables, or incrementally loaded tables, depending on the needs of the workflow and the performance characteristics of the data.

dbt also leverages Snowflake-specific features like `MERGE` statements for incremental models, `GRANT` statements to manage access, and Snowflake's query tags for better observability. Users can include custom SQL hooks to run pre- or post-model operations, integrate with Snowflake Tasks for lightweight orchestration, and use Snowflake’s role-based access control to manage security at the model level.

In practice, dbt serves as the transformation engine, while Snowflake acts as the execution layer and storage platform. This separation allows teams to write modular, testable, and version-controlled transformation logic in dbt, while taking advantage of Snowflake’s scalability, performance, and enterprise features.

### Understanding the Role of dbt and Snowflake in Your Data Pipeline 

#### Role of dbt

dbt acts as the transformation layer in the modern data stack, enabling teams to define, organize, and execute SQL-based transformations in a modular and version-controlled way. Each dbt project defines models, which are essentially SQL SELECT statements that build upon raw or previously transformed data. These models are grouped into logical layers such as staging, intermediate, and mart to promote clean architecture and maintainability.

Beyond SQL modeling, dbt provides functionality for testing, documentation, dependency management, and deployments. Developers can define assertions using built-in test macros, generate lineage graphs to visualize dependencies, and produce documentation directly from model code. This makes dbt both a transformation engine and a framework for managing analytics engineering workflows.

#### Role of Snowflake

Snowflake serves as the compute engine and storage layer for dbt transformations. All compiled SQL generated by dbt is executed directly on Snowflake, leveraging its cloud-native architecture. Snowflake handles the execution of queries, materialization of models, and storage of both intermediate and final datasets across its scalable compute clusters and persistent storage layers.

Snowflake offers key features that complement dbt's approach, including automatic scaling, zero-copy cloning, time travel, and fine-grained access control. These capabilities support high-performance execution, ease of testing, and secure collaboration, especially in complex or regulated environments.

#### How They Work Together

dbt and Snowflake integrate through a direct connection, where dbt compiles transformation logic into executable SQL and pushes it to Snowflake for processing. dbt leverages Snowflake’s compute resources to create or update datasets, while Snowflake ensures high availability, scalability, and consistent performance during execution. The combination allows teams to abstract transformation logic in dbt and delegate heavy lifting to Snowflake.

This collaboration also enables streamlined CI/CD workflows for analytics. Developers can test dbt models against isolated Snowflake environments (e.g., using clones), implement Git-based versioning, and automate deployments via orchestrators or dbt Cloud. The result is a robust, scalable, and testable transformation stack that aligns with software engineering practices.

### Key Considerations for Working with dbt in Snowflake 

#### Performance

A core technique is using incremental models to process only new or changed data rather than reprocessing entire datasets. This significantly reduces query runtime and compute usage, especially for large fact tables. Properly configured `unique_key` and `incremental_filter` logic is essential to ensure accuracy and efficiency.

Another key factor is warehouse sizing and concurrency scaling. Snowflake allows configuring different virtual warehouses for different dbt tasks. Lightweight transformations can run on small warehouses, while resource-intensive jobs (such as full refreshes or large joins) benefit from larger or multi-cluster warehouses. Enabling Snowflake’s automatic concurrency scaling ensures that multiple dbt models can run in parallel without queuing.

Model materialization choices also affect performance. Views are fast to create but compute-intensive at query time. Tables can improve performance by persisting results but consume storage. Ephemeral models reduce intermediate table overhead by compiling directly into downstream SQL, but may increase query complexity and execution time. Choose materializations based on data size, reusability, and performance tradeoffs.

#### Scalability

Scalability also depends on efficient SQL practices. Avoid unnecessary CTE nesting, use selective joins and filters early in queries, and leverage Snowflake’s clustering keys where appropriate for large tables with predictable access patterns.

It’s important to monitor and optimize regularly. Use dbt artifacts and Snowflake’s query history to analyze slow-running models. Combine dbt’s `query_tag` feature with monitoring tools to profile model execution over time and identify bottlenecks as data grows.

#### Cost Management for Transformations

Efficient scheduling in dbt projects involves aligning model runs with data availability and minimizing compute costs. Teams typically schedule dbt runs using orchestrators like Airflow, dbt Cloud, or Snowflake Tasks.

Warehouse sizing should match the workload: smaller warehouses for lightweight transformations and larger ones for compute-intensive operations. Snowflake’s ability to auto-scale and auto-suspend helps reduce idle time and cost.

To cut down on redundant computation, dbt encourages designing models that avoid reprocessing data unnecessarily. Incremental models are the primary mechanism for this, allowing only new or changed data to be processed.

Using ephemeral models in place of intermediate tables can also reduce storage I/O. Developers should review model dependencies regularly and remove unused or outdated models from the DAG.

#### Access Control

dbt itself doesn’t manage access control but integrates tightly with Snowflake’s role-based access control (RBAC). Access to data and models is governed by Snowflake roles and privileges, which can be configured to restrict who can run dbt models, access schemas, or view specific datasets.

dbt users often assign different roles to development and production environments. For example, developers may only have access to dev schemas, while production deployments use a service account with elevated privileges.

GRANT statements can be automated using dbt’s `post-hook` configurations, ensuring proper access is applied when models are created or updated.

#### Sensitive Data Handling

When working with sensitive data, teams must ensure that dbt models don't expose PII or confidential information unintentionally. This includes:

- Masking or redacting sensitive fields in staging or intermediate layers
- Ensuring models with sensitive data are only materialized in secure schemas
- Using Snowflake’s dynamic data masking and row access policies
- Excluding sensitive fields from documentation or exposures

dbt supports column-level documentation, allowing teams to tag fields that contain sensitive data and implement review processes to ensure compliance with data governance policies.

### Working with Snowflake Dynamic Tables in dbt Projects 

#### Why Use Snowflake Dynamic Tables for dbt?

Performance tuning with dbt on Snowflake involves matching model execution with the compute environment’s characteristics. In dev and staging environments, smaller warehouses are typically sufficient for testing and debugging. However, in production, larger warehouses, or multi-cluster warehouses, may be necessary to meet throughput and SLA requirements, especially for large fact models or concurrent workloads.

To improve scalability, teams should adopt modular modeling practices to keep individual queries manageable. Breaking down complex logic into layered models (e.g., staging → intermediate → mart) allows better caching, faster compilation, and easier debugging. dbt’s tags and resource\_type selectors enable targeted runs, helping teams isolate changes and avoid full DAG rebuilds.

#### Setting Up Dynamic Tables

To use Snowflake Dynamic Tables in dbt, users can define a custom materialization or use community packages that abstract the setup. Dynamic Tables require specification of a target lag (e.g., 5 minutes) and a warehouse for continuous refresh.

Configuration involves setting the materialization to `dynamic_table`, defining the `target_lag`, and ensuring the correct compute resources are allocated. Because dbt doesn't yet natively support Dynamic Tables, developers often use `pre-hook` and `post-hook` SQL to manage creation and refresh behavior.

This setup enables near real-time data processing, making it suitable for event-driven architectures and low-latency analytics workflows.

#### Common Challenges to Anticipate

Teams working with dbt and Snowflake may face challenges such as managing dependencies in large DAGs, controlling warehouse costs, and ensuring consistent testing across environments.

Other common issues include:

- Slow performance due to suboptimal query patterns or model design
- Difficulty in debugging failed runs in large, interdependent projects
- Managing schema changes in upstream data sources
- Coordinating data refresh timing with downstream reporting tools

Adopting naming conventions, limiting DAG depth, using dbt tests, and monitoring warehouse usage can help mitigate these problems.

### Step-by-Step: Building dbt on Snowflake

This walkthrough outlines the complete flow for setting up and running a dbt project with Snowflake, from account preparation to building and testing models.

#### 1. Set up your Snowflake environment

Before connecting dbt, prepare the required Snowflake resources:

- **Create a warehouse**: Provision a virtual warehouse that dbt will use to execute transformations. Configure auto-suspend and auto-resume to control costs.<br><br>
- **Create a database and schemas**: Define a database for analytics and separate schemas for raw, staging, and transformed data layers.<br><br>
- **Create a role for dbt**: Set up a dedicated role with permissions to create schemas, tables, and views.<br><br>
- **Grant privileges**: Grant the role usage on the warehouse and database, and create privileges on relevant schemas.

This ensures dbt has the correct permissions and isolated compute resources for transformations.

#### 2. Install dbt and the Snowflake adapter

Install dbt along with the Snowflake adapter:

`pip install dbt-snowflake`

![](https://dagster.io/webflow-assets/ee3c099b4e8e0e10e51d.avif)

Confirm installation:

`dbt --version`

![](https://dagster.io/webflow-assets/ac1bfcc45f31ed13a989.avif)

This verifies that dbt Core and the Snowflake plugin are correctly installed.

#### 3. Configure your dbt profile

dbt connects to Snowflake using a profile configuration file (`profiles.yml`), typically located in your home directory under `.dbt/`.

Define:

- Account identifier
- Username and authentication method (password, key pair, SSO, etc.)
- Role
- Warehouse
- Database
- Schema
- Thread count

Example structure:

`your_project_name:`

`  target: dev`

`  outputs:`

`    dev:`

`      type: snowflake`

`      account: <account_identifier>`

`      user: <username>`

`      password: <password>`

`      role: <role_name>`

`      database: <database_name>`

`      warehouse: <warehouse_name>`

`      schema: <schema_name>`

`      threads: 4`

‍

Test the connection:

`dbt debug`

![](https://dagster.io/webflow-assets/21d2d18ccfe6cc667f50.avif)

A successful output confirms connectivity and credentials are configured correctly.

#### 4. Initialize a dbt project

Create a new project:

`dbt init analytics_project`

‍

This generates:

- `dbt_project.yml`
- A `models/` directory
- Example model files
- Project-level configurations

Navigate into the project directory to begin development.

#### 5. Configure project settings

In `dbt_project.yml`, define:

- Default materializations
- Model folder structure
- Target schemas
- Tags or custom configurations

Example configuration:

`models:`

`  analytics_project:`

`    staging:`

`      +schema: STAGING`

`      +materialized: view`

`‍`

`    marts:`

`      +schema: MARTS`

`      +materialized: table`

This enables layered architecture such as staging → intermediate → mart.

#### 6. Define sources

Declare raw data sources in a YAML file inside the `models/` directory:

`version: 2`

`‍`

`sources:`

`  - name: app_raw`

`    database: ANALYTICS_DB`

`    schema: RAW_APP`

`    tables:`

`      - name: app_users`

`      - name: app_orders`

‍

Defining sources allows dbt to:

- Track lineage
- Test source freshness
- Reference tables using `source()`

#### 7. Create your first model

Add following  SQL model file inside `models/staging/`, for example:

`select`

`id as user_id,`

`first_name,`

`last_name,`

`email,`

`created_at`

`from {{ source('app_raw', 'app_users') }}`

‍

Add following in `models/staging/stg_app_orders.sql`

`select`

`    order_id,`

`    user_id,`

`    order_total,`

`    order_date,`

`    updated_at`

`from {{ source('app_raw', 'app_orders') }}`

‍

dbt models are SELECT statements. dbt handles creation of views or tables based on materialization settings.

#### 8. Run the models

Execute:

`dbt run`

![](https://dagster.io/webflow-assets/c9d3c6c3c1472c02bfd1.avif)

dbt will:

1. Compile SQL using Jinja templating
2. Resolve dependencies
3. Execute compiled SQL in Snowflake
4. Create models in the configured schema

Verify the created objects in Snowflake.

You can verify using the following queries:<br><br>`use role DBT_ROLE;`

`use database ANALYTICS_DB;`

`‍`

`show schemas like '%STAGING%' in database ANALYTICS_DB;`

`show schemas like '%MARTS%'   in database ANALYTICS_DB;`

`‍`

`-- list objects created by dbt in the schema dbt used`

`show tables in schema ANALYTICS_DB.STAGING;`

`show views  in schema ANALYTICS_DB.STAGING;`

`‍`

`-- if dbt created a prefixed schema like staging_STAGING, check that too:`

`show tables in schema ANALYTICS_DB.STAGING_STAGING;`

`show views  in schema ANALYTICS_DB.STAGING_STAGING;`

![](https://dagster.io/webflow-assets/282d47287ea450c1612b.avif)

#### 9. Add tests

Define tests in YAML:<br><br>Add following test in `models/staging/staging_tests.yml`

`version: 2`

`‍`

`models:`

`  - name: stg_app_users`

`    columns:`

`      - name: user_id`

`        tests:`

`          - not_null`

`          - unique`

`‍`

`  - name: stg_app_orders`

`    columns:`

`      - name: order_id`

`        tests:`

`          - not_null`

`          - unique`

‍

Run tests:

`dbt test`

‍

dbt executes validation queries in Snowflake and reports failures.

![](https://dagster.io/webflow-assets/46c1f266033444e54b42.avif)

#### 10. Create downstream models

Build higher-level models referencing upstream ones:

`-- models/marts/fct_orders.sql`

`‍`

`select`

`o.order_id,`

`u.user_id,`

`u.email,`

`o.order_date,`

`o.order_total`

`from {{ ref('stg_app_orders') }} o`

`join {{ ref('stg_app_users') }} u`

`on o.user_id = u.user_id`

‍

`ref()` creates dependency tracking and ensures correct execution order. You can run this using the following command:

`	dbt run --select fct_orders`

![](https://dagster.io/webflow-assets/f66f5a1f50f7cd87deac.webp)

#### 11. Generate documentation

Generate project documentation:

`dbt docs generate`

`dbt docs serve`

![](https://dagster.io/webflow-assets/f772eb114e0703bfb106.avif)

This provides:

- Lineage graphs
- Model descriptions
- Column metadata
- Test visibility

#### 12. Implement incremental models (optional)

For large datasets, configure incremental logic:

`-- models/marts/fct_app_orders_inc.sql`

`{{ config(materialized='incremental', unique_key='order_id') }}`

`‍`

`select *`

`from {{ source('app_raw', 'app_orders') }}`

`‍`

`{% if is_incremental() %}`

`where updated_at > (select max(updated_at) from {{ this }})`

`{% endif %}`

‍

You can run this using this command:

`dbt run --select fct_app_orders_inc`

![](https://dagster.io/webflow-assets/d11fa1bdaac0dcf07972.avif)

Snowflake executes optimized MERGE logic behind the scenes when configured properly.

#### 13. Deploy and orchestrate

In production:

- Use separate dev and prod targets<br><br>
- Run dbt via dbt Cloud, Airflow, or Snowflake Tasks<br><br>
- Use CI workflows for pull requests<br><br>
- Leverage Snowflake clones for safe testing

This supports version control, repeatable deployments, and scalable execution.

<strong><em>Related content: Read our guide to </em></strong>[<strong><em>dbt cloud alternatives</em></strong>](https://dagster.io/learn/dbt-cloud-alternatives)

### Best Practices for dbt with Snowflake 

Here are a few best practices that can help you get the most our of dbt in a Snowflake data warehouse.

#### 1. Take Advantage Of Snowflake's Query Profiler To Help Optimize

Use the Snowflake Query Profile to analyze the performance of dbt-generated queries. After executing a model, review the query profile to inspect execution steps, time spent per operation, and scan statistics. Focus on identifying bottlenecks such as large table scans, inefficient joins, or skewed partitions.

Look for long-running steps like table scans or distributed joins, and investigate whether filters are being applied early in the plan. If not, consider rewriting the model to filter rows or select columns earlier. Use this insight to refine model logic, adjust materialization strategies, or apply clustering to improve performance on large datasets.

#### 2. How To Use Micro Partitions Correctly

Snowflake automatically organizes data into micro partitions, but query performance depends on how well filters align with partition boundaries. In dbt, you can improve partition pruning by ensuring that frequently queried filter columns—such as dates or IDs—are used early in WHERE clauses.

Avoid unnecessary transformations on filter columns (like casting or functions) that prevent Snowflake from leveraging partition metadata. When designing incremental models, ensure new data appends cleanly in order based on partitioned fields to maintain micro partition efficiency. Also, monitor the average bytes scanned per query to confirm effective pruning.

#### 3. Using Separate Warehouses For Different Environments

Use different Snowflake warehouses for dev, staging, and prod environments to isolate workloads and avoid resource contention. In dbt, define these warehouses in your `profiles.yml `and use environment-specific targets for deployments.

This separation allows developers to test models without impacting production jobs and supports tuning warehouse size independently per environment. For example, small dev warehouses reduce cost, while larger prod warehouses ensure SLAs are met for critical data loads. This practice also helps trace costs and query patterns by environment.

#### 4. Using Snowflake Clones For Test Environments

Leverage Snowflake’s zero-copy cloning to quickly create isolated environments for testing without duplicating data. You can clone databases, schemas, or individual tables to test model changes, CI/CD runs, or schema evolution plans.

In dbt, point your development targets to these cloned environments. This allows full testing of transformation logic on production-like data without risk to live systems. Clones are especially useful for regression testing or validating model refactoring before merging changes to main branches.

#### 5. Routing Expensive Queries To Differently Sized Warehouses

Assign resource-intensive dbt models to larger warehouses by configuring warehouse overrides at the model level. Use the `config(warehouse=...)` setting in individual models or groups of models that require higher compute.

Reserve smaller warehouses for lightweight staging or ephemeral models. For larger fact tables, incremental models, or late-stage transformations, route execution to more powerful warehouses that can process data faster with lower latency.

This strategy balances cost and performance, allowing teams to allocate compute efficiently and avoid overprovisioning across all workloads.

### Managing Snowflake Data Pipelines with Dagster

As teams scale their use of dbt and Snowflake, orchestration and operational visibility become just as important as transformation logic. Dagster provides a modern way to manage dbt and Snowflake together as a cohesive, production-grade data platform.

With Dagster’s dbt integration, your dbt project is loaded directly into Dagster and represented as software-defined assets. Each dbt model becomes an asset in a unified asset graph, giving you automatic lineage, dependency tracking, and selective execution. Instead of triggering full `dbt run` jobs, you can materialize only the models impacted by upstream changes — helping reduce unnecessary Snowflake compute usage.

Dagster also integrates cleanly with Snowflake through configurable resources. You can define environment-specific connections (dev, staging, prod), route different assets to different warehouses, and safely test changes against Snowflake zero-copy clones. This makes it easier to manage environments and control costs without duplicating pipeline logic.

Beyond orchestration, Dagster adds observability and reliability. dbt tests can surface as asset checks, runs are fully logged, and failures can trigger alerts. You gain visibility not just into whether a job ran, but which data assets were updated, how long they took, and what downstream systems are affected.

Together, Snowflake handles scalable compute, dbt defines transformation logic, and Dagster ensures everything runs reliably, efficiently, and with full operational insight.

---

## What Is ELT, Pros/Cons & 7 Steps to Build Your Pipeline

> ELT is a data integration process that extracts raw data, loads it into a data warehouse, and transforms it within the warehouse for large data sets.

Source: https://dagster.io/learn/elt

### What Is Extract, Load, Transform (ELT)?

Extract, Load, Transform (ELT) is a data integration pattern used to handle large data sets in modern data warehousing. Unlike ETL (Extract, Transform, Load), the ELT process involves first extracting raw data from various sources, loading it directly into a data lake or data warehouse, and then performing transformations within the data warehouse environment.

This approach leverages the processing power of modern data warehouses and modern data platforms capable of transforming data directly within a data lake, making it well-suited for massive and complex datasets. ELT supports scalability, allowing organizations to manage growing volumes of data without inhibiting performance.

ELT is particularly advantageous when handling unstructured and semi-structured data due to its flexibility and adaptability. It is increasingly popular in big data ecosystems and cloud computing environments, where computational resources are abundant and transformation processes can be optimized within the data warehouse or data lake.

### ELT vs ETL

These two processes are closely related but have several important differences.

#### What Is the Difference?

The key difference between ELT and ETL lies in the order of operations, particularly where the transformation takes place. In ETL, data is extracted from source systems, transformed according to specific business rules outside the data warehouse, and then loaded into the target warehouse. ELT loads raw data directly into the data warehouse before transformations are applied within the warehouse itself.

This distinction makes ETL more suitable for traditional data systems that may have limited processing capabilities, as it offloads transformations to external engines. ELT leverages the powerful, scalable processing capabilities of data warehouses, enabling efficient handling of large datasets.

Another difference is flexibility: ELT offers more adaptability when dealing with unstructured or semi-structured data, as the transformations can be customized post-load. In ETL, transformations need to be well-defined upfront, which can limit flexibility in handling evolving data sources.

#### When to Use Each Technique

ETL is often the preferred approach in legacy systems or when working with smaller, well-structured datasets where the transformation logic is straightforward and can be handled outside the warehouse. It's also useful when compliance or regulatory requirements demand data cleaning and transformation before storage.

ELT is best suited for modern big data environments where the volume and variety of data are high. It works well with cloud-based data warehouses like Snowflake or Google BigQuery, which are optimized for in-database transformations. ELT is especially beneficial when dealing with unstructured data sources such as logs or social media data.

### How ELT Works

Here’s an overview of the ELT process.

#### Extract

The extract phase involves pulling data from various heterogeneous sources. These can include transactional databases, CRM systems, sensors, cloud services, and more. During extraction, data is gathered in its raw form without alteration, ensuring that all required data is loaded into the data warehouse.

Data extraction must be carefully managed to ensure data integrity and consistency. The process often involves scheduling regular extracts to keep data current and using techniques like change data capture (CDC) to obtain only altered data. Ensuring data quality at this stage is essential, as errors or inconsistencies can propagate through the entire pipeline.

#### Load

Loading refers to the process of moving extracted data into a data warehouse or data lake environment. This step sets the foundation for subsequent data analysis and transformations. It allows raw data to reside in a centralized repository where it can be accessed and processed efficiently. The process must ensure that data loads correctly without causing discrepancies in data quality or reliability.

Efficient loading requires optimizing data transfer to handle data volume, velocity, and variability. Bulk load operations are typically used to manage large datasets, while incremental loads are essential to maintain up-to-date data without overwhelming system resources. Considerations such as indexing, error handling, and transactional integrity ensure that data is correctly loaded and prepared for transformation.

#### Transform

Transformation in ELT happens after data is loaded into the data lake or warehouse, offering flexibility in data processing. By performing this step within the data lake or data warehouse, larger datasets can be transformed quicker due to the scalable computational resources available. Transformations include sorting, aggregating, joining, and cleaning data to align it with business requirements.

This phase allows organizations to tailor data structures to meet analytical needs, utilizing the data warehouse’s built-in functions and features. It also supports complex transformation rules and logic that can be applied uniformly across whole datasets. By leveraging the data platform’s capabilities, ELT ensures transformation processes are efficient and scalable.

### Key Features of ELT Tools

#### Source Connectivity

Source connectivity in ELT tools refers to the ability to connect to, and retrieve data from, diverse data sources across an organization's IT landscape. This allows organizations to collect rich data from databases, cloud services, ERP systems, and third-party APIs with minimal friction. ELT tools should provide a wide array of connectors to sync data across platforms.

A source connectivity framework in ELT tools enables scalable and adaptable integration strategies, accommodating changing business needs without extensive reconfigurations. It ensures that all necessary data is accessible for loading into the warehouse or data lake.

#### Data Loading Capabilities

The effectiveness of ELT processes heavily relies on data loading capabilities, which ensure that data is transferred efficiently into the data warehouse or data lake. This involves optimizing data transfer speeds to handle high-volume data loads without overwhelming the network or system resources.

Effective ELT tools use batch loading, incremental loading, and real-time streaming capabilities. Advanced data loading features enable organizations to handle variations in data velocity and volume, accommodating spikes in data generation without bottlenecks. Some tools also offer support for managing data schema evolution, error handling mechanisms, and checkpoint capabilities to ensure data integrity during transfers.

#### In-Database Transformations

In-database transformations enable ELT tools to leverage the processing capacity of the data warehouse by executing transformations directly within the database environment. This capability reduces data movement and minimizes latency, allowing for quicker turnaround times on data processing tasks.

ELT tools that support in-database transformations enable complex operations such as joins, aggregations, and conditional transformations. By utilizing the native computing capacity of modern data warehouses, organizations can scale their transformation processes efficiently without needing additional middleware.

#### Data Governance and Security

Data governance and security in ELT tools requires maintaining data integrity, confidentiality, and availability throughout the data lifecycle. This is typically achieved through features such as role-based access controls (RABC), encryption during data transfer, and audit logging for all data activities.

ELT tools must comply with data protection regulations by providing mechanisms to manage data privacy and maintain accurate records of data movements and transformations. Effective data governance frameworks integrated into ELT tools ensure that data usage policies are enforced across the organization, preventing unauthorized access while supporting compliance.

#### Self-Service Interface

A self-service interface empowers users to independently manage and configure data processes without requiring deep technical knowledge. It supports non-technical staff in accessing, retrieving, and transforming data as needed.

Such interfaces typically include drag-and-drop capabilities, intuitive dashboards, and step-by-step guidance for managing ELT tasks. This democratization of data handling allows for faster decision-making and iterative analysis.

### Pros and Cons of ELT Pipelines

**Advantages of ELT pipelines include:**

#### Scalability

As organizations accumulate more data, ELT can scale by leveraging cloud storage and processing capabilities, bypassing the bottlenecks typically encountered in data transformation operations external to the warehouse. This scalability is essential for companies experiencing rapid data growth and diverse data streams.

#### Faster Data Loading

The ELT process enables faster data loading by immediately transferring raw data into the data warehouse, bypassing the transformation stage at the outset. This significantly reduces bottlenecks traditionally caused by data transformation processes. By allowing raw data to reside in the warehouse, organizations can expedite data availability.

#### Data Warehouse Capabilities

ELT leverages the capabilities of modern data warehouses, which offer high-efficiency, in-database processing. This integration allows organizations to utilize warehouse features for data transformation processes. As data warehouses are optimized for handling large-scale operations with minimal infrastructure constraints, ELT maximizes their capabilities for faster transformations.

#### Support for Unstructured and Semi-Structured Data

Traditional ETL processes often struggle with varying data formats, but ELT excels by using the data warehouse as a repository for raw, unstructured data. This makes ELT suitable for integrating data from sources like social media, IoT devices, and web logs that do not conform to conventional tabular formats.

**Disadvantages of ELT pipelines include:**

#### Data Quality and Consistency

In ELT pipelines, raw data is loaded into the warehouse before transformations occur. This can lead to issues with data quality if errors, duplicates, or inconsistencies are present in the source data. Since transformations happen later, these issues can propagate throughout the warehouse, potentially affecting analytics and reporting accuracy.

#### Handling Large Amounts of Data

While ELT is designed to handle large data volumes, managing these datasets efficiently is complex. Loading massive amounts of raw data can place a significant strain on network bandwidth, storage, and computational resources. Dataset may also be duplicated or unnecessarily accrued over time without proper data management and governance. Additionally, the transformation stage, which takes place within the warehouse, requires substantial processing power.

#### Security and Compliance Gaps

A significant concern with ELT pipelines is the security and compliance risks associated with loading raw, untransformed data into the data warehouse. Sensitive information, such as PII, may be exposed before necessary protections like masking or encryption are applied. This can result in compliance violations, especially in industries subject to regulations like GDPR or HIPAA.

#### Resource Bloating

ELT pipelines can lead to resource inefficiencies, particularly when storing large amounts of raw data in the warehouse. Since unprocessed data is loaded first, storage costs can quickly escalate, especially in cloud environments where charges are based on usage. Additionally, in-warehouse transformations require significant computational resources, which can strain system performance and increase operational expenses if not managed properly.

#### Managing Dependencies and Orchestration

Managing dependencies in an ELT pipeline is more complex than in ETL processes, because transformations occur after loading. Coordinating extraction, loading, and transformation stages is essential to ensuring timely data availability. Without effective orchestration, delays in one stage can cause bottlenecks or inconsistencies in downstream transformations.

### Dealing with the Cons of ELT

Here are some of the ways that organizations can ensure effective ELT processes.

#### Ensuring Data Quality: Validation, Testing, and Data Lineage

To mitigate the data quality issues in ELT pipelines, organizations should implement validation and testing frameworks during the loading and transformation phases. Initial validation can be introduced by checking for anomalies, missing values, or duplicates as data is ingested. This helps catch obvious errors early and ensures that flawed data doesn’t propagate throughout the warehouse.

Data lineage tools can also be used to track data sources, transformations, and usage across the pipeline. This transparency allows teams to pinpoint where issues arise and maintain consistency. Automated testing suites, such as unit tests for transformation scripts, can further enhance data quality by verifying that each transformation yields the expected outcomes.

#### Supporting Volume: Optimizing Extraction and Loading, Integrating with Distributed Computing Frameworks

Managing large volumes of data in an ELT pipeline requires strategies to optimize both extraction and loading processes. To minimize bottlenecks, organizations can implement parallelized data extraction, ensuring data is pulled from multiple sources simultaneously. Batch loading can further enhance performance, reducing the strain on network bandwidth.

In addition, utilizing the scalable infrastructure of modern data warehouses or integrating distributed computing frameworks like Apache Spark or Hadoop with the ELT process allows for more efficient handling of large datasets during the transformation stage. These frameworks provide scalable computation power, enabling faster processing times and reducing the risk of performance slowdowns.

#### Security: Access Controls, Encryption, Integration with Cloud Providers

Implementing strong access controls ensures that only authorized users can access raw data, reducing the risk of unauthorized exposure of sensitive information like personally identifiable information (PII). Role-based access control (RBAC) models can be used to restrict access to specific data sets, ensuring compliance with regulations such as GDPR and HIPAA.

Encryption is another essential element of security. Data should be encrypted both in transit and at rest, ensuring that it remains protected throughout the pipeline. Most modern cloud providers offer built-in encryption services, which can be integrated into the ELT workflow. These cloud platforms also often provide compliance certifications, making it easier to meet regulations.

#### Managing Dependencies and Orchestration

To effectively manage dependencies and orchestrate ELT pipelines, teams should leverage automated orchestration tools such as Dagster, Apache Airflow, or Prefect. These tools provide scheduling capabilities, ensuring that each step of the pipeline—extraction, loading, and transformation—occurs in the correct sequence.

These tools also enable monitoring and alerting for pipeline failures or delays, making it easier to troubleshoot issues. Orchestration platforms help break down complex pipelines into modular components, simplifying maintenance.

### How to Build an ELT Pipeline

Here’s an overview of how to establish an ELT process.

#### 1. Defining the Data Flow

The first step in building an ELT pipeline is to define a clear data flow that outlines how data will move through the pipeline. This includes identifying the sources of data, such as databases, APIs, or cloud services, and determining how the data will be extracted, loaded into the warehouse, and transformed.

Mapping out the data flow in advance helps ensure that all necessary data points are captured. Consideration must also be given to the structure of the data, including how unstructured, semi-structured, and structured data will be handled. Establishing the flow early enables the organization to create efficient extraction and transformation strategies.

#### 2. Incorporate Data Governance and Quality from the Start

Integrating data governance and quality control measures early in the planning process ensures that data entering the pipeline is secure, compliant, and reliable. This includes implementing role-based access controls, encryption, and comprehensive logging to meet security and compliance standards, such as GDPR or HIPAA.

For data quality, ensure that data validation and cleaning mechanisms are in place. Using data quality frameworks that can automatically detect and flag anomalies ensures that only high-quality data is transformed and analyzed in the warehouse.

#### 3. Choosing the Right Tools and Technologies

Selecting the appropriate tools for each stage of the ELT pipeline is crucial for its efficiency and scalability. Modern cloud-based data warehouses like Snowflake, Amazon Redshift, and Google BigQuery are well-suited for ELT processes due to their in-database transformation capabilities and flexible storage options.

Transformations can be handled using SQL-based tools like dbt (data build tool) that enable in-database transformations. Orchestration tools such as Dagster can automate and monitor the scheduling of extraction, loading, and transformation tasks.

#### 4. Modularizing the Pipeline

Breaking down the ELT pipeline into modular components allows for easier development, testing, and troubleshooting. Modular pipelines also make it possible to reuse components across different data processes, improving scalability and maintainability.

Each phase—extraction, loading, and transformation—should be treated as a separate module with clear input and output definitions. This modularity also supports incremental updates, where only new or changed data is processed, reducing resource usage and improving performance.

#### 5. Optimizing Data Loading

Efficient data loading is essential for keeping the ELT pipeline responsive and cost-effective. Batch processing can be used for large datasets, while real-time or incremental loads are better suited for systems requiring continuous data updates. Employing bulk load operations where possible reduces latency and network congestion, improving overall load times.

Using tools that support automatic schema evolution can also ensure that new fields or data types are handled seamlessly without manual intervention, minimizing downtime and errors.

#### 6. Using Staging Areas for Raw Data

Establishing a staging area within the data warehouse allows for more flexible and efficient data transformations. Raw data is first loaded into staging tables, where it can be validated, cleaned, and transformed as needed. This reduces the risk of affecting production data and provides a safe environment for testing transformations.

Staging areas enable the normalization and standardization of data, ensuring that data enters the warehouse in a well-prepared state. This organization is particularly beneficial for handling unstructured data, enabling systematic parsing and formatting.

#### 7. Implementing Version Control for Transformations

Version control systems keep track of changes in transformation scripts and configurations, allowing teams to monitor and rollback changes when necessary. This capability is invaluable for debugging, collaborating on new changes, and auditing data handling practices.

Version control supports systematic management and improvement processes, encouraging a disciplined approach to data transformations. It supports collaborative efforts among data engineers by offering a clear history of changes and promoting accountability. Consistent versioning practices also enhance compliance with data governance policies.

### Orchestrating ELT Pipelines with Dagster

As a data-centric data orchestrator, Dagster allows data teams to execute their ELT pipelines as a cohesive part of their wider data platform, enabling tighter integration between data ingestion and downstream products that depend on this data.

With a built-in toolkit for data ingestion thanks to our [Embedded ELT](https://docs.dagster.io/integrations/libraries/embedded-elt) feature and [integrations with leading providers like Fivetran](https://docs.dagster.io/integrations/libraries/fivetran), Dagster allows you to orchestrate your ELT workloads on your platform of choice.

Other features like [Dagster Pipes](https://docs.dagster.io/integrations/external-pipelines) and [Asset Checks](https://docs.dagster.io/guides/test/asset-checks) also help streamline some of the most challenging aspects of running ELT pipelines at scale, like setting up parallel processing on remote, distributed infrastructure and enforcing data quality checks at the point of ingestion.

To learn more about building ELT pipelines with Dagster, see this rundown of ELT Options in Dagster and this [demo of an example project using Embedded ELT](https://www.youtube.com/watch?v=gAqOLgG2iYY).

‍


# Glossary

## ACID Properties

> The set of properties of database transactions intended to guarantee validity even in the event of errors or failures, encompassing Atomicity, Consistency, Isolation, and Durability.

Source: https://dagster.io/glossary/acid-properties
Further reading: https://en.wikipedia.org/wiki/ACID

---

## Aggregate

> Combine data from multiple sources into a single dataset.

Source: https://dagster.io/glossary/data-aggregation

### Aggregation definition:

Data aggregation is one of the most common tasks in data analytics. Data aggregation involves combining or summarizing multiple data points or observations into a single entity or a smaller set of entities. It enables us to transform raw data into more structured, manageable, and useful formats.

In the context of modern data orchestration, data aggregation is often used to summarize large datasets and make them more accessible for downstream analysis, reporting, or visualization. This process involves grouping data by one or more variables and applying aggregate functions, such as *mean*, *sum*, *count*, or *min/max*, to calculate statistics or metrics for each group.

Data aggregation is commonly used in [ETL (Extract, Transform, Load)](https://dagster.io/glossary/etl-extract-transform-load) pipelines to transform data from multiple sources into a single, consolidated dataset. For instance, in a marketing analytics pipeline, data from multiple sources, such as social media, email campaigns, and website traffic, may be aggregated to generate a comprehensive view of marketing performance across channels.

Data aggregation is also a fundamental technique in data warehousing, where large volumes of data are processed and transformed to create a centralized repository of structured data. Data aggregation helps to reduce the size of these datasets by summarizing them into smaller, more manageable datasets that can be queried and analyzed more efficiently.

Overall, data aggregation is an important technique for modern data orchestration because it enables us to process, transform, and manage large volumes of data more effectively, leading to better insights and more informed decision-making.

### Data aggregation best practices

When running an aggregation, there are several best practices to keep in mind.

**Use a well-defined schema**: Define a schema for the input data and output data. You can use Python libraries like Pandas, PySpark, or Dask to define schemas for structured data. The schema ensures that the data is correctly merged and avoids errors due to inconsistent data types or field names.

**Use appropriate aggregation functions**: Use appropriate functions to aggregate the data based on the business requirements. Python has built-in functions like sum(), min(), max(), and mean() for basic aggregation, and libraries like Pandas and PySpark have more advanced functions for complex aggregations.

**Optimize data storage and processing**: To optimize storage and processing, consider using libraries like Pandas, PySpark, or Dask, which have built-in optimization features like partitioning, indexing, and caching.

**Monitor performance**: Use Python libraries like PySpark or Dask to monitor the performance of the aggregation process. Metrics like processing time, data volume, and memory usage can be tracked to identify potential bottlenecks and optimize performance.

**Handle errors and exceptions**: Python provides exception handling to handle errors and exceptions, such as missing data, invalid data types, or failed data sources. Ensure that the aggregation process can handle these cases gracefully and provide appropriate error handling and logging.

**Test and validate**: Use Python libraries like *unittest* or *Pytest* to write automated tests to validate the output data against expected results or business rules. Ensure that the data pipeline is tested and validated to ensure that it is producing the expected results.

### Aggregation vs. Pre-aggregation

Given our definition for "Aggregation", you might be curious what data engineers mean by "pre-aggregation". Pre-aggregation are steps taken specifically to improve query performance and reduce computational overhead when querying the data later on (whereas "aggregation" are just steps to get to a final analysis, and not specifically optimized for query performance or storage).

Pre-aggregation involves creating summary tables or views that contain pre-computed aggregated values at different levels of granularity, such as daily, weekly, or monthly summaries. These pre-aggregated tables can be used to accelerate analytical queries by providing faster access to summarized data, as opposed to querying the raw, detailed data.

When dealing with large volumes of data, complex queries or analytical operations can be time-consuming and resource-intensive. By pre-aggregating the data, these operations can be performed on smaller, more manageable subsets of the data, resulting in faster response times and improved overall system performance.

Pre-aggregation involves selecting relevant attributes and applying aggregation functions such as sum, count, average, minimum, or maximum to generate aggregated values. This process creates summary tables that contain aggregated data at various levels of granularity, such as daily, weekly, monthly, or even at a higher level of abstraction.

There are several benefits to pre-aggregation (click to expand):

Improved query performanceAggregating data in advance reduces the amount of data that needs to be processed when executing queries. By querying pre-aggregated tables instead of raw data, queries can be executed faster, enabling quicker insights and analysis.Reduced resource consumption

Pre-aggregation reduces the computational resources required to process data-intensive operations. Aggregating data upfront decreases the complexity of queries, allowing organizations to optimize their hardware and software infrastructure.

Simplified data modeling

Pre-aggregated tables simplify the data model by providing summarized views of the data. This can make it easier for analysts and data scientists to explore and analyze the data without needing to perform complex aggregations on the fly.

But pre-aggregation can be overused. Over-aggregation can result in the loss of granularity, so you need to carefully evaluate the trade-offs between query performance and the level of detail necessary for specific analytical use cases.

#### Example of data aggregation using Python

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import pandas as pd

# create sample data
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
        'Department': ['Marketing', 'Engineering', 'Marketing', 'Sales', 'Engineering'],
        'Salary': [50000, 70000, 60000, 80000, 75000],
        'Bonus': [1000, 2000, 1500, 2500, 1800]}

df = pd.DataFrame(data)

# group data by department and calculate mean salary and total bonus
agg_data = df.groupby('Department').agg({'Salary': 'mean', 'Bonus': 'sum'})

print(agg_data)
```

In this example, we start by creating a sample dataset with columns for `Name`, `Department`, `Salary`, and `Bonus`. We then create a Pandas DataFrame from this data.

Next, we group the data by department using the `groupby()` method, and specify that we want to calculate the mean salary and sum of bonuses for each department using the `agg()` method. Finally, we print the resulting aggregated data.

The result of this aggregation is printed out to the screen:

```plaintext trailing-newline
                 Salary  Bonus
Department
Engineering     72500.0   3800
Marketing       55000.0   2500
Sales           80000.0   2500
```

### Example of data pre-aggregation using Python

Again, the process used in pre-aggregation is similar to that used in aggregation, but the end goal is different, optimizing for query performance and storage optimization.  But here is a simple example of how we might pre-aggregate data, using Pandas. Please note that you need to [install Pandas in your Python environment](https://dagster.io/glossary/packages) to run this code.

```python
import pandas as pd

# Raw data
data = {
    'date': ['2023-06-01', '2023-06-01', '2023-06-01', '2023-06-01', '2023-06-02', '2023-06-02', '2023-06-03', '2023-06-03', '2023-06-03', '2023-06-03'],
    'product': ['A','A', 'B', 'B', 'A', 'B', 'A', 'A', 'C', 'C'],
    'sales': [100,300,200,50,150,250,180,120,60,60]
}

# Create a DataFrame from the raw data
df = pd.DataFrame(data)

# Perform pre-aggregation
aggregated_df = df.groupby(['date', 'product']).sum().reset_index()

# Display the pre-aggregated DataFrame
print(f"Raw table:\n{df}")
print(f"Pre-aggregated table:\n{aggregated_df}")
```

In this example, we have raw sales data for different products on different dates. The pre-aggregation is performed by grouping the data by the 'date' and 'product' columns using the `groupby` function. Then, the `sum` function is applied to calculate the total sales for each combination of date and product. The resulting pre-aggregated DataFrame contains summarized data. Note the loss of granularity in the output:

```plaintext
Raw table:
         date product  sales
0  2023-06-01       A    100
1  2023-06-01       A    300
2  2023-06-01       B    200
3  2023-06-01       B     50
4  2023-06-02       A    150
5  2023-06-02       B    250
6  2023-06-03       A    180
7  2023-06-03       A    120
8  2023-06-03       C     60
9  2023-06-03       C     60
Pre-aggregated table:
         date product  sales
0  2023-06-01       A    400
1  2023-06-01       B    250
2  2023-06-02       A    150
3  2023-06-02       B    250
4  2023-06-03       A    300
5  2023-06-03       C    120
```

In this case, the pre-aggregated DataFrame provides a summary of the sales data by date and product, making it easier to perform queries and analysis on the aggregated data instead of the raw, detailed data.  This simple example illustrates how, in many cases, you can optimize queries, reduce storage requirements, and also avoid downstream re-interpretation of how to calculate things like roll-ups.

---

## Agile Methodology

> An iterative approach to software development and project management that prioritizes flexibility and customer satisfaction, often used by data engineering teams to manage projects.

Source: https://dagster.io/glossary/agile-methodology-56798
Further reading: https://en.wikipedia.org/wiki/Agile_software_development

---

## Alation

> A machine learning data catalog that helps people find, understand, and trust the data.

Source: https://dagster.io/glossary/alation
Further reading: https://www.alation.com/

---

## Align

> Aligning data can mean one of three things: aligning datasets, meeting business rules, or arranging data elements in memory.

Source: https://dagster.io/glossary/data-alignment

When we talk about "data alignment" in data engineering, we might be referring to one of three things.

- [Arranging data elements in memory for more efficient execution](#)
- [Aligning or realigning datasets to maintain a coherent data structure](#)
- [Transforming datasets to 'align' with specific logical or business rules](#)

### Data alignment definition 1: arranging data elements in memory

The first use of the term "data alignment" refers to **the arrangement of data elements in memory to optimize performance and memory usage**. It ensures that data is stored in memory in a manner that is suitable for efficient access and processing, so this is a very valuable practice in data engineering and optimizing data pipelines, especially at scale.

In Python, data alignment typically applies to data *structures* such as arrays, structures, or objects. The alignment rules specify how individual data elements within these structures are positioned in memory. The alignment can affect the size and layout of the data, as well as the speed of accessing and manipulating it.

Alignment is often dictated by the hardware architecture and the specific data types being used. For example, some processors might require data to be aligned to specific byte boundaries—such as 4- or 8-byte alignments—for optimal performance.

Python provides certain tools and libraries, such as the `struct` module, to control the alignment of data structures. These tools allow developers to define and manipulate data structures with specific alignment requirements, ensuring compatibility with the underlying hardware and optimizing performance as needed.

In a high-level language like Python, data alignment isn't directly addressed in the code, but it's a principle that the language design, underlying libraries, and hardware may utilize to optimize performance and memory usage. Nevertheless, we can illustrate the concept of data alignment with a contrived example, specifically by considering how to use Python's NumPy library, which is frequently employed in data engineering tasks.

NumPy is known to organize arrays in a highly efficient manner for vectorized operations. This is due to the data alignment of the underlying C data structure used in NumPy's implementation. Therefore, understanding and leveraging NumPy's data arrangement can lead to more efficient memory usage and performance in Python.

Here's a simple example of how you might create and manipulate data in a NumPy array, illustrating a performance enhancement due to efficient data alignment:

#### A (somewhat contrived) example of the benefits of data alignment using NumPy:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import numpy as np
import time

# Create a large list with a million elements
list_data = [i for i in range(10**6)]

# Create a NumPy array from the list
np_data = np.array(list_data)

# Measure the time to square all elements in the list
start_time_list = time.time()
list_data_squared = [i**2 for i in list_data]
end_time_list = time.time()

# Measure the time to square all elements in the NumPy array
start_time_np = time.time()
np_data_squared = np_data**2
end_time_np = time.time()

print(f"Time taken to square a list: {end_time_list - start_time_list} seconds")
print(f"Time taken to square a numpy array: {end_time_np - start_time_np} seconds")
```

When you run this code, you'll find that squaring each element in the NumPy array is considerably faster than squaring each element in the list. This difference is partly due to how the data in the NumPy array is aligned in memory, making it more efficient for this type of operation.

Here is the output on my machine (an M1 Mac) - an improvement of 147 X:

```plaintext
Time taken to square a list: 0.20438814163208008 seconds
Time taken to square a numpy array: 0.0013937950134277344 seconds
```

Again, it's not possible to directly control data alignment in Python code. Still, it's crucial to understand how the language and libraries you use handle data alignment so you can design your code to take advantage of it. That's an integral part of designing efficient data pipelines and other data engineering tasks and is valuable for your depth of knowledge as a data engineer.

### Data alignment definition 2: ensuring merged datasets have consistent dimensions.

When working with data, we are often required to merge two datasets, but these may have different dimensions.

The term "Dataset dimensions" typically refers to the structure or shape of the dataset. In most contexts, it refers to the number of rows and columns that the dataset has. But in multi-dimensional datasets (such as those used in machine learning or complex statistical analysis), "dimensions" can also refer to the number of features or variables that each observation has, even if these don't correspond to literal columns in a two-dimensional table.

So in this specific context, the act of *aligning the data* might involve one of several things.

It might mean ensuring that each data frame has the same columns. In this case, you would add missing columns to the data frames that don't have them, likely filling in null or default values. Alternatively, you could remove extra columns from data frames that have more than the ones you're aligning to.

Or it might require ensuring that each data frame has the same number of rows. This might involve appending rows with null or default values, or it might mean sampling a subset of rows from a larger data frame. If the rows represent different observations, you'd need to ensure that the same observations are present in each data frame, which could involve a complex process of data integration.

#### The benefits of aligning your data:

Investing time in data alignment ensures that the data is high quality, fit for purpose, and trusted.  On a more simple level, you often will run into blockers if you don't align your data prior to analysis (as in, your analysis won't work).

- **Establishing clear data lineage**: Use Python's logging module to log the source and destination of data at each stage of the pipeline. This helps to establish clear data lineage and provides traceability in case of errors or inconsistencies.  A tool like Dagster provides rich metadata and [data lineage information](https://docs.dagster.io/guides/build/assets).
- **Ensuring data consistency**: You will run into lots of frustrations working with inconsistent data. Use Python's Pandas library to ensure that the data is consistent in terms of data types, formats, and naming conventions. For example, you can use the [pandas.dataframe.astype](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.astype.html#pandas-dataframe-astype) function to convert columns to the appropriate data type, and the "rename" function to standardize column names.
- **Normalizing data**: Use Python's Pandas library to normalize data into a consistent and standardized format. For example, you can use the [`groupby`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html) function to group data by a specific column and the [`agg`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.agg.html) function to perform aggregation functions on the grouped data.
- **Cleaning and preprocessing data**: Use Python's Pandas library to clean and preprocess data. For example, you can use the [`fillna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html) function to fill missing values, and the [`apply`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html) function to perform custom transformations on the data.
- **Validating data**: Use Python's assert statement to validate data. For example, you can use the [assert statement](https://docs.python.org/3/reference/simple_stmts.html#grammar-token-python-grammar-assert_stmt) to ensure that certain columns have specific values or that certain conditions are met.
- **Using appropriate alignment techniques**: Use Python's Pandas library to align data using appropriate techniques such as merging, joining, and concatenating. For example, you can use the [`merge`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html) function to merge two dataframes based on a specific column and the [`concat`](https://pandas.pydata.org/docs/reference/api/pandas.concat.html) function to concatenate two dataframes.
- **Automating the alignment process**: Use Python's scripting capabilities to automate the alignment process. For example, you can create a Python script that reads in data from a source, performs the necessary transformations and alignments, and writes the results to a destination.

#### Example of data alignment (definition 2) in Python using Pandas

Data alignment is intrinsic, which means that it's inherent to the operations you perform. So when you perform an operation between two DataFrame or Series objects, Pandas will align data in them by their labels (index for Series and DataFrame, and columns for DataFrame), **not their position**.

For example, consider the case where we have two dataframes `df1` and `df2`, and we want to add them together. Even if the rows in `df1` and `df2` are in different order, Pandas will take care of aligning them according to their **labels** (i.e., index and column names) when performing the addition operation. If there are labels present in one dataframe but not in the other, the resulting dataframe will contain those labels with a value of `NaN` ("Not a Number").

Here's an example of such data alignment in Python using the Pandas library.

##### About the Pandas `align()` function:

The `align()` function in Pandas is explicitly used to align two data objects with each other according to their labels. The function `align()` can be used on both Series and DataFrame objects and returns a new object of the same type with labels compared and aligned. Read the [Pandas docs on the `align` function here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.align.html).

The basic syntax is:        

```javascript
s1, s2 = s1.align(s2, join='type_of_join')
```

The `join` parameter accepts one of four possible options: 'outer', 'inner', 'left', 'right'. The 'outer' join type, for instance, would ensure that the resultant data includes the union of all labels from both inputs, filling with NaN for missing data on either side.

##### Our `align()` example:

Here's an example of how the `align()` function can be used.  Suppose we have two data frames, `df1` and `df2` , with different shapes and some overlapping columns:

```python
import pandas as pd
import numpy as np

df1 = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

df2 = pd.DataFrame({
    'A': [10, 11],
    'B': [12, 13],
    'D': [14, 15]
})
```

We can align the data frames along their index using the `align()` function as follows:

```python
df1_aligned, df2_aligned = df1.align(df2, fill_value=np.nan)

print(df1_aligned)
print(df2_aligned)
```

This will create two new data frames, `df1_aligned` and `df2_aligned`, with aligned indexes and matching columns:

```python
df1_aligned:
     A    B    C    D
0  1.0  4.0  7.0  NaN
1  2.0  5.0  8.0  NaN
2  3.0  6.0  9.0  NaN

df2_aligned:
      A     B   C   D
0  10.0  12.0 NaN NaN
1  11.0  13.0 NaN NaN
```

Now the data frames have matching shapes and `NaN` values in the columns that didn't exist in the original data frames. This now allows us to perform operations on the aligned data frames, such as adding them together:

```python
df_sum = df1_aligned + df2_aligned
print(df_sum)
```

This will give us a new data frame, df\_sum, with the sum of the aligned values:

```plaintext
df_sum:
      A     B   C   D
0  11.0  16.0 NaN NaN
1  13.0  18.0 NaN NaN
2   NaN   NaN NaN NaN
```

As you can see, the aligned data frames allow us to perform operations on data sets that might not have originally had the same shapes or column names.  As always, this is a simplistic example, but hopefully illustrative of the process.

### Data alignment definition 3: meeting business rules

Aligning data can also be defined as **adjusting data to conform to business rules, policies, and regulations**. In this context, "data alignment" refers to the process of ensuring that the data collected, stored, and processed within an organization adheres to the specific requirements and guidelines set by the business or governing bodies.

This type of data alignment involves validating, cleansing, and transforming data to meet the desired standards and comply with relevant regulations. It may include tasks such as data normalization, data cleansing to remove inconsistencies or errors, and applying data transformation rules to ensure consistency and compatibility across different systems or processes.

Data alignment in the context of business rules, policies, and regulations is crucial for maintaining data integrity, accuracy, and compliance. It ensures that the data used for decision-making, analysis, reporting, and other business operations is reliable, consistent, and meets the necessary requirements defined by the organization or external entities.

---

## Aligning

> Aligning data can mean one of three things: aligning datasets, meeting business rules, or arranging data elements in memory.

Source: https://dagster.io/glossary/aligning

---

## Anomaly Detection

> Identify data points or events that deviate significantly from expected patterns or behaviors.

Source: https://dagster.io/glossary/anomaly-detection

### Anomaly detection definition:

Anomaly detection helps to identify outliers and anomalies in your data that could skew results or indicate underlying issues in the data. Here are some best practices for implementing anomaly detection in Python:

- **Understand the data**: Before implementing any anomaly detection techniques, it's important to have a thorough understanding of the data being analyzed. This includes understanding the distribution of the data, any trends or patterns, and any known anomalies. Python libraries such as Pandas, NumPy, and Matplotlib can be used to perform exploratory data analysis and gain insights into the data.
- **Choose appropriate algorithms**: There are many different algorithms available for anomaly detection, and the choice of algorithm will depend on the specific use case and the characteristics of the data being analyzed. Some common algorithms for anomaly detection include Isolation Forest, Local Outlier Factor (LOF), and One-class SVM. Python libraries such as Scikit-learn and PyOD provide implementations of these algorithms.
- **Evaluate performance**: It's important to evaluate the performance of any anomaly detection algorithm to ensure that it is accurately identifying anomalies without generating too many false positives. This can be done by calculating metrics such as precision, recall, and F1 score. Python libraries such as Scikit-learn provide functions for calculating these metrics.
- **Incorporate into your data pipeline**: Anomaly detection should be incorporated into the overall data pipeline, ideally as close to the data source as possible. This can be done using Python libraries such as Apache Kafka or AWS Kinesis to stream data to an anomaly detection model. Once an anomaly is detected, an alert can be generated to notify data engineers or data scientists.

Here are some specific Python functions that can be used for anomaly detection using the [Scikit-learn library](https://scikit-learn.org/stable/). Install Scikit-learn with `pip install -U scikit-learn`:

- **Matplotlib** installation instructions [are found here](https://matplotlib.org/stable/users/installing/index.html) but basically just involves the command `python -m pip install -U matplotlib` .

**IsolationForest**:  A class that implements the Isolation Forest algorithm for anomaly detection. It works by creating random partitions of the data and identifying outliers based on the number of partitions required to isolate the data point.

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run this code.*

```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest

# Generate some sample data with anomalies
np.random.seed(42)
X = 0.3 * np.random.randn(100, 2)
X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
X = np.vstack([X, X_outliers])

# Fit the isolation forest model
clf = IsolationForest(random_state=42)
clf.fit(X)

# Predict the anomaly scores for each point in the data
scores = clf.decision_function(X)

# Visualize the results
plt.scatter(X[:, 0], X[:, 1], c=scores, cmap='coolwarm')
plt.colorbar()
plt.title('Anomaly Scores')
plt.show()
```

Which will produce the following graph:

![](https://dagster.io/images/glossary/681399f654933b29e12fbbc4_glossary-2.png)

**LocalOutlierFactor**:  A class in the Scikit-learn library that implements the LOF algorithm for anomaly detection. It works by comparing the local density of a data point to the density of its neighbors, and identifying points with significantly lower density as anomalies.

```python
from sklearn.neighbors import LocalOutlierFactor
from sklearn.datasets import load_iris

# Load the iris dataset
X, y = load_iris(return_X_y=True)

# Fit the local outlier factor model
clf = LocalOutlierFactor(n_neighbors=20, contamination=0.1)
y_pred = clf.fit_predict(X)

# Print the predicted labels for each point
print(y_pred)
```

With the output looking like this:

`[ 1  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1 -1  1  1  1  1  1  1 -1 -1  1  1  1  1  1  1  1  1  1  1 -1 -1  1  1  1 -1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1]`

**Precision, Recall, F1 score**: Functions in the Scikit-learn library that can be used to evaluate the performance of an anomaly detection algorithm.        

```python
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.datasets import make_classification
import numpy as np

# Generate some sample data
X, y_true = make_classification(n_samples=1000, n_features=10, n_classes=2)

# Generate some random predictions
y_pred = np.random.randint(0, 2, size=len(y_true))

# Calculate precision, recall, and F1 score
precision = precision_score(y_true, y_pred, average='binary')
recall = recall_score(y_true, y_pred, average='binary')
f1 = f1_score(y_true, y_pred, average='binary')

# Print the results
print('Precision:', precision)
print('Recall:', recall)
print('F1 score:', f1)
```

which yields:

```plaintext
Precision: 0.5077220077220077
Recall: 0.526
F1 score: 0.5166994106090373
```

By following these best practices and using appropriate Python functions for anomaly detection, data engineers can ensure that their data pipelines are effectively detecting and handling anomalies in the data.

### Anomaly detection in Dagster

With Dagster, you can use [asset checks](https://docs.dagster.io/guides/test/asset-checks) to compare current data against historical materializations to detect anomalies. For example, you can write an asset check that compares the number of null values in a column against historical values and fails the check if the current number of nulls appears anomalous.

Here's an example of how you might implement such an asset check in Dagster:

```python
from dagster import asset, asset_check, AssetCheckResult, MaterializeResult, EventRecordsFilter, DagsterEventType, AssetKey
import statistics
import random

random.seed()

@asset
def asset1():
    """
    A test asset for asset checks
    """
    num_rows = 0
    num_rows = random.random()*100
    return MaterializeResult(metadata={"num_rows": num_rows})

@asset_check(asset=asset1)
def num_rows_is_within_standard_deviations(context):

    min_materialization = 3

    records = context.instance.get_event_records(
        EventRecordsFilter(DagsterEventType.ASSET_MATERIALIZATION, asset_key=AssetKey("asset1")),
        limit=1000,
    )
    # Count the number of materializations
    materialization_count = len(records)


    if materialization_count >= min_materialization:
        num_rows_values = [
            record.asset_materialization.metadata["num_rows"].value for record in records
        ]
        mean = statistics.mean(num_rows_values[:-1])
        stdev = statistics.stdev(num_rows_values[:-1])

        return AssetCheckResult(
            passed=abs(num_rows_values[0] - mean) <= stdev,
            metadata={"Note": f"num_rows is {round(num_rows_values[0],2)}. Mean is {round(mean,2)}. Standard deviation is {round(stdev,2)}."}
        )

    else:
        # The check does not make sense until we get to the 3rd materialization of the asset
        return AssetCheckResult(
            passed=True,
            metadata={"Note": "This check can only execute on the 3rd materialization onwards."}
        )
```

`‍`This code snippet defines an asset and an asset check that uses historical materialization metadata to determine if the current number of rows is within one standard deviation of the mean of the previous materializations. If it's not, the check fails, indicating a potential anomaly.

![](https://dagster.io/images/glossary/681399f654933b29e12fbc60_glossary-asset-check-dagster-min.png)

   A screenshot of the Dagster UI showing our test asset the result of the tests defined above.

---

## Anonymize

> Remove personal or identifying information from data.

Source: https://dagster.io/glossary/data-anonymization

### Data anonymization definition:

Data anonymization is the process of removing personal or identifying information from data to protect the privacy of individuals. In the context of modern data pipelines, anonymization is an important technique used to protect sensitive data, especially in industries such as healthcare, finance, and government.

There are several techniques used in anonymization, such as:

- **Removing personal information:** This involves removing information such as names, addresses, social security numbers, and any other identifying information from the dataset. This is the most basic form of anonymization.
- **Pseudonymization**: This involves replacing the identifying information with pseudonyms or codes. For example, replacing names with unique IDs or codes. This technique is reversible, and the original data can be reconstructed using the code.
- **Generalization**: This involves replacing specific values with a more general value. For example, replacing specific ages with age ranges or replacing specific zip codes with broader geographical regions.
- **Noise addition**: This involves adding random noise to the data to mask the original values. For example, adding random numbers to ages or incomes to obscure the original values.

Here's an example of anonymizing a dataset using the Pandas library in Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Assuming `data.csv` is as follows:

```plaintext
| Patient ID | Age | Name         | ZIP/Postal Code | Address              | Social Security Number | Income |
| ---------- | --- | ------------ | --------------- | -------------------- | ---------------------- | ------ |
| 12         | 46  | James Bond   | BD74AD          | 27 Ardmore Terrace   | CN142371               | 1000   |
| 13         | 32  | William Tell | 21209           | 23 Archery Road      | 321-65-6278            | 1000   |
| 14         | 61  | Cheryl Crow  | 88905           | 1 Sunset Boulevard   | 788-52-1876            | 1000   |
| 15         | 66  | Marie Curie  | 78690           | 10 Rue Maurice Ravel | FR2939793              | 10000  |
```

You can run the following code:

```python
import pandas as pd
import numpy as np

# Load the dataset
df = pd.read_csv('data.csv')

# Remove personal information
df = df.drop(['Name', 'Address', 'Social Security Number'], axis=1)

# Pseudonymization
df['Patient ID'] = df['Patient ID'].apply(lambda x: 'Patient_' + str(x))

# Generalization
df['Age'] = df['Age'].apply(lambda x: str(x//10) + '0s')

# Noise addition
df['Income'] = df['Income'].apply(lambda x: x + np.random.normal(0, 500))
```

And yield the following output:

```plaintext
| Patient ID  | Age Group | ZIP/Postal Code |    Income |
| ----------- | --------- | --------------- | --------  |
| Patient\_12 | 40s       | BD74AD          |  1 545.91 |
| Patient\_13 | 30s       | 21209           |  1 410.58 |
| Patient\_14 | 60s       | 88905           |  1 487.86 |
| Patient\_15 | 60s       | 78690           | 10 521.22 |
```

In the example above, we load a dataset and remove personal information such as name, address, and social security number. We then use pseudonymization to replace the patient ID with a unique ID, generalize the age to age ranges, and add random noise to the income column. These techniques help to protect the privacy of individuals while still retaining the utility of the dataset.

---

## Apache Atlas

> A scalable and extensible set of core foundational governance services, enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop.

Source: https://dagster.io/glossary/apache-atlas
Further reading: https://atlas.apache.org/#/

---

## Append

> Adding or attaching new records or data items to the end of an existing dataset, database table, file, or list.

Source: https://dagster.io/glossary/data-append

### A definition of data appending

In the context of data engineering, "append" typically refers to the operation of **adding or attaching new records or data items to the end of an existing dataset, database table, file, or list**.

In distributed data processing systems like Apache Hadoop or Spark, appending data is a common operation when aggregating or processing large volumes of data. The append operation ensures that new information is added in sequence, maintaining the integrity and order of the dataset. For example, appending can be used to add new rows to a table in a database or new entries to a log file without modifying or deleting the existing content.

For data engineers working on complex data pipelines, data appending is a crucial and delicate operation, especially when dealing with streaming data or batch-processing workflows in both real-time and batch processing systems.

Data appending is not just about adding records to the end of a dataset. It involves considerations around consistency, efficiency, scalability, and reliability, especially when dealing with complex data pipelines in distributed and high-volume environments. Balancing these factors is critical for building robust and efficient data pipelines that can seamlessly handle the continuous influx of new data while maintaining the integrity and utility of the existing datasets.

### Appending vs. merging

Appending and [merging](https://dagster.io/glossary/data-merging) are related but separate operations.  In appending, we bring together two datasets with similar structures, whereas merging requires [aligning the data](https://dagster.io/glossary/data-alignment) and creating the rules for [deduplicating](https://dagster.io/glossary/data-deduplication) and [clensing data](https://dagster.io/glossary/data-cleaning) during the merge.

### Data appending use cases:

There are many use cases for data appending in data engineering including:

1. **Log Aggregation:**   Appending log entries from various services to a centralized log store for analysis, monitoring, and troubleshooting.
2. **Time-series Databases:**   Appending new time-stamped records in time-series databases for monitoring and analyzing trends over time.
3. **Stream Processing:**   Continuously appending data points from streaming sources to datasets or tables for real-time analysis and processing.
4. **Batch Processing:**   Accumulating and appending processed records in batch-oriented workflows to build up comprehensive datasets for analytics and reporting.
5. **ETL (Extract, Transform, Load)**:     Data appending is often used in the context of ETL processes where data from various sources is ingested, transformed, and loaded into a data warehouse, data lake, or other storage solutions.

### Key considerations in data appending:

When appending data, engineers often deal with structured or semi-structured data formats such as JSON, CSV, Avro, or Parquet. As you append new records to the end of an existing dataset or file, you must ensure that the schema is consistent and that any transformations or enrichments applied to the new data are in line with existing records.

1. **Data Integrity and Consistency:**   It is essential to maintain data integrity and schema consistency when appending new records, avoiding data corruption or discrepancies which can impact downstream analytics or applications.
2. **Concurrency and Locking:**   In environments where multiple processes or threads may be trying to append data simultaneously, managing concurrent writes and implementing appropriate locking mechanisms are crucial to prevent data loss or corruption.
3. **Indexing and Partitioning:**   Proper indexing and partitioning strategies must be considered to efficiently append and query data, especially in large-scale distributed storage systems.
4. **Error Handling:**   Robust error handling mechanisms need to be in place to deal with failures during the append operation, such as network issues, disk failures, or schema mismatches.
5. **Performance:**   The append operation should be optimized to ensure high throughput and low latency, particularly in real-time data processing pipelines where the timely addition of data is critical.
6. **Data Retention and Storage Management:**   As appending accumulates more data, managing storage resources, implementing data retention policies, and optimizing storage costs become essential.
7. **Idempotency and Deduplication:**   Ensuring that the append operation is idempotent and implementing deduplication strategies are crucial in situations where data might be replayed or where duplicate records need to be avoided.

### "Big Data" Technologies:

When dealing with big data, numerous technologies are available that enable efficient and scalable data appending. The diverse range of technologies for data appending in big data ecosystems facilitates the development of scalable, efficient, and robust data pipelines, capable of handling varying workloads and data types. Selecting the appropriate stack of technologies is paramount and should align with the specific requirements and constraints of the given big data project.

- Data engineers often use distributed computing frameworks like Apache Spark or Apache Flink for appending data in scalable and fault-tolerant manners.
- Distributed file systems like HDFS or cloud-based storage solutions like Amazon S3 are often used as the underlying storage where data is appended.
- Databases like Apache Cassandra and Apache HBase are used when a combination of write and read efficiency is needed for appending operations.

### An example of data appending in Python

To demonstrate data appending using Apache Spark in Python, let's consider a scenario where we have an existing Parquet file (existing\_data.parquet) in a distributed file system, and we have a new batch of data in a CSV file (new\_data.csv) that we need to append to the existing Parquet file.

For this illustration we will first run a short script that will generate both a local Parquet data and our new .csv file:

```python
from pyspark.sql import SparkSession
from pyspark.sql import Row
import csv

# Define the path for the new CSV file
file_path = 'radiohead_data.csv'

# Define the data to be written to the CSV file
data = [
    ['id', 'name', 'created_date', 'role'],  # Header row
    [1, 'Thom Yorke', '1968-10-07', 'vocals'],
    [2, 'Jonny Greenwood', '1971-10-05', 'guitar'],
    [3, 'Ed OBrien', '1968-04-15', 'guitar'],
]

# Write to the CSV file
with open(file_path, mode='w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)

print(f'{file_path} has been created successfully.')

# Initialize a Spark session
spark = SparkSession.builder.appName("CreateParquetExample").getOrCreate()

# Sample Data
data = [
    Row(id=4, name='Philip Selway', created_date='1967-05-23', role='drums'),
    Row(id=5, name='Colin Greenwood', created_date='1969-06-26', role='bass'),
    Row(id=2, name='Jonny Greenwood', created_date='1971-10-05', role='guitar'),
    Row(id=3, name='Ed OBrien', created_date='', role='vocals'),
]

# Creating a DataFrame from the sample data
df = spark.createDataFrame(data)

# Writing the DataFrame to a Parquet file
df.write.mode('overwrite').parquet('sample_data.parquet')

# Stop the Spark session
spark.stop()
```

Now that we have our two files we can append the data form the .csv file to the Parquet file.

```python
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DateType

# Initialize a Spark Session
spark = SparkSession.builder.appName("DataAppendingExample").getOrCreate()

# Define a schema for the CSV file
schema = StructType([
    StructField("id", IntegerType(), True),
    StructField("name", StringType(), True),
    StructField("created_date", DateType(), True),
    StructField("role", StringType(), True)
])

# Load the existing Parquet file
existing_data = spark.read.parquet("sample_data.parquet")
existing_data.createOrReplaceTempView("radioheadData")
print(spark.catalog.listTables())

# Load the new data from the CSV file
new_data = spark.read.csv("radiohead_data.csv", schema=schema, header=True)

# Append the new data to the existing data
appended_data = existing_data.union(new_data)

# Perform some transformations if needed, e.g., deduplication
final_data = appended_data.dropDuplicates()

# Write the appended data back to the Parquet file
final_data.write.mode('overwrite').parquet("sample_data.parquet")

# Because the data has changed, we must refresh the table before printing out the data
spark.sql('REFRESH TABLE radioheadData')

# Optionally, you can perform some validations or exploratory data analysis
final_data.show()

# Stop the Spark Session
spark.stop()
```

The output from this exercise should be as follows:

```plaintext
| ID | Name            | Created Date | Role   |
| -- | --------------- | ------------ | ------ |
| 5  | Colin Greenwood | 1969-06-26   | bass   |
| 4  | Philip Selway   | 1967-05-23   | drums  |
| 3  | Ed OBrien       | –            | vocals |
| 2  | Jonny Greenwood | 1971-10-05   | guitar |
| 3  | Ed OBrien       | 1968-04-15   | guitar |
| 1  | Thom Yorke      | 1968-10-07   | vocals |
```

Note that we are using `dropDuplicates()` in this example, so the process will not blindly append the new data to the old, but will also remove *exact* duplicate records from the data.  To illustrate this point you will notice that there are two duplicate rows in the datasets: one for "Jonny Greenwood" which is a perfect record match and one for "Ed OBrien" which is not a perfect match.  The end result is that the exact match is removed fome the final file, but the patial match results in two entries for "Ed OBrien".

---

## Archive

> Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. Store data for long-term retention and compliance.

Source: https://dagster.io/glossary/data-archiving

### Data archiving definition:

Data archiving, in the context of modern data pipelines, refers to the process of moving data that is no longer actively used to a separate storage system for long-term retention. Data archiving helps in reducing primary storage costs, enhancing system performance, and ensuring compliance with data retention policies and regulations. The archived data is indexed and has search capabilities, so it can still be easily accessed and retrieved if needed. It's a critical part of a data lifecycle management strategy and is usually one of the last stages in a data pipeline.

### Data archiving strategies in data engineering:

Archiving strategies are important to ensure that data is stored efficiently and can be retrieved when needed. Here are some main archiving strategies used in data engineering specific to data pipeline design:

- **Cold storage**: This strategy involves storing data that is infrequently accessed, and therefore can be stored on cheaper, slower storage devices. This is often used for archival purposes, where the data needs to be retained for a long period of time, but is not accessed frequently. Examples of cold storage solutions include Amazon Glacier and Google Cloud Storage Nearline.
- **Partitioning**: This strategy involves dividing data into smaller, more manageable chunks called partitions, which can be stored separately. This can help to improve query performance by allowing data to be queried in parallel. For example, data can be partitioned by date, region, or any other relevant attribute.
- **Compression**: This strategy involves reducing the size of data by compressing it, which can help to reduce storage costs and improve transfer times. There are many compression algorithms available, such as gzip and bzip2, which can be used depending on the type of data being compressed.
- **Incremental backups**: This strategy involves backing up only the changes made to data since the last backup, rather than backing up the entire dataset each time. This can help to reduce the amount of data that needs to be backed up, and also reduce backup times.
- **Versioning**: This strategy involves keeping multiple versions of data over time, which can be useful for auditing purposes or for keeping track of changes made to the data. Versioning can be achieved through techniques such as snapshotting or using version control systems.
- **Data archiving to the cloud**: Cloud providers like AWS, Azure and GCP offer data archival solutions where data can be transferred and stored securely in their cloud infrastructure. This can provide cost savings, flexibility and durability compared to on-premise solutions.

These are some of the main archiving strategies used in data engineering to ensure that data is stored efficiently and can be retrieved when needed. Choosing the appropriate archiving strategy for a particular data pipeline will depend on factors such as data access patterns, data size, cost, and retention requirements.

---

## Argo

> An open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes.

Source: https://dagster.io/glossary/argo
Further reading: https://argoproj.github.io/

---

## Association Rule Mining

> A machine learning method aimed at identifying interesting relations between variables (items or events) in large databases, frequently used for market basket analysis.

Source: https://dagster.io/glossary/association-rule-mining
Further reading: https://www.geeksforgeeks.org/association-rule/

---

## AsyncIO

> Speed up execution with asynchronous I/O.

Source: https://dagster.io/glossary/asyncio

### An introduction to asyncIO:

AsyncIO is a Python library for asynchronous I/O. It is built around the coroutines of Python and provides tools to manage them and handle the I/O in an efficient way.

AsyncIO allows tasks to be performed concurrently rather than sequentially. This can be useful in scenarios where a task has to wait for something - for example, a response from a server or a read from a file. By allowing other tasks to be executed during this waiting time, the overall execution time of the program can be significantly reduced.

AsyncIO operates on a system of events and callbacks that are common in modern data pipelines, where an event is something that happens (like receiving data from a network connection), and a callback is a function that is called when that event happens.

#### Key Components of asyncIO:

1. **Coroutines**: Coroutines are functions that can be paused and resumed. This makes them ideal for I/O operations because they can be paused while waiting for input or output, allowing other tasks to run in the meantime.
2. **Tasks**: Tasks are a type of coroutine that are managed by the AsyncIO event loop. When a task is created, it is scheduled to run on the event loop.
3. **Event Loop**: The event loop is the core of every AsyncIO application. It schedules and executes tasks and callbacks, handles I/O, and manages system events.

### Example of using AsyncIO in Python

Here is a basic example of how AsyncIO can be used.  Imagine we have a process that retrieves the contents of a web page and calculates the length of the returned file.  We might simply write this using Python `requests` as follows:

```python
import requests
import time

def fetch(url):
    return requests.get(url).text

def run_requests():
    urls = [
        'https://dagster.io',
        'http://python.org',
        'https://docs.python.org/3/library/asyncio.html',
        'https://github.com/python/asyncio',
    ]

    start_time = time.time()

    for url in urls:
        result = fetch(url)
        print(len(result))

    end_time = time.time()
    print(f'All requests completed in {(end_time - start_time):.2f} seconds')

if __name__ == "__main__":
    run_requests()
```

Which would take a few seconds, then return the results:

```plaintext
80938
50572
19715
189497
All requests completed in 3.27 seconds
```

Let's now run the same task with the implementation of AsyncIO and the `aiohttp` library (which uses asyncio internally) and measure how long it takes for them all to finish.

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

```python
import aiohttp
import asyncio
import time

async def fetch(session, url):
    async with session.get(url) as response:
        result = await response.text()
        print(f'Fetched {url}, Length: {len(result)} characters')
        return result

async def main():
    urls = [
        'https://dagster.io',
        'http://python.org',
        'https://docs.python.org/3/library/asyncio.html',
        'https://github.com/python/asyncio',
    ]

    start_time = time.time()

    async with aiohttp.ClientSession() as session:
        tasks = [fetch(session, url) for url in urls]
        await asyncio.gather(*tasks)

    end_time = time.time()
    print(f'All requests completed in {(end_time - start_time):.2f} seconds')

# Python 3.7+
asyncio.run(main())
```

By fetching the pages concurrently, we significantly improve the performance of our process.  Also note that the order in which the pages were retrieved is different than in the sequential approach.

```plaintext
Fetched https://dagster.io, Length: 80938 characters
Fetched https://docs.python.org/3/library/asyncio.html, Length: 19704 characters
Fetched http://python.org, Length: 50310 characters
Fetched https://github.com/python/asyncio, Length: 189502 characters
All requests completed in 1.03 seconds
```

---

## Auto-materialize

> The automatic execution of computations and the persistence of their results.

Source: https://dagster.io/glossary/auto-materialization

### A definition of auto-materialization

"auto materialization" refers to the automatic execution of computations and the persistence of their results into a physical storage medium, such as a disk or memory, without the need for manual intervention. This concept is particularly relevant in systems that manage data assets and pipelines, where the goal is to ensure that data is up-to-date and available for downstream consumption.

Dagster, for instance, has the concept of [auto materialization policies](https://docs.dagster.io/guides/automate/declarative-automation) that can be applied to data assets. These policies determine when and how assets should be automatically materialized based on certain conditions, such as changes in upstream data, freshness requirements, or specific schedules. Auto materialization helps in maintaining the data's relevance and freshness by automating the process of updating data assets when their dependencies have changed, without requiring users to manually trigger these updates.

### Auto-materialization policies

"auto-materialization policies" refer to the rules or strategies that determine when and how data assets should be automatically materialized. These policies can be configured to automatically materialize assets based on certain conditions or triggers, such as changes in upstream data, code updates, or according to a schedule.

For example, Dagster supports different types of auto-materialization policies, such as "eager" and "lazy" policies. An eager policy might materialize an asset as soon as any of its dependencies are updated, while a lazy policy might delay materialization until the asset is explicitly requested or needed.

These policies help manage the materialization of assets in a way that balances performance, resource utilization, and the freshness of data. They can be customized to fit the specific needs of a data pipeline, ensuring that data is materialized in the most efficient and effective manner for a given use case.

You can read more about how Dagster handles [auto materialization in the Dagster docs](https://docs.dagster.io/guides/automate/declarative-automation).

---

## Backend-as-a-Service (BaaS)

> A cloud computing service model that serves as the middleware that provides developers with ways to connect their web and mobile applications to cloud services via application programming interfaces (APIs) and software developers' kits (SDKs).

Source: https://dagster.io/glossary/backend-as-a-service-baas
Further reading: https://en.wikipedia.org/wiki/Backend_as_a_service

---

## Backpressure

> A mechanism to handle situations where data is produced faster than it can be consumed.

Source: https://dagster.io/glossary/data-backpressure

### Definition of backpressure in data

In the context of data engineering and particularly in stream processing and message passing systems, the term "backpressure" refers to a mechanism to handle situations where data is produced faster than it can be consumed.

Some areas and systems where you will see backpressure mechanisms deployed include:

- **Reactive Streams**: This is a standard for asynchronous stream processing with non-blocking backpressure. Popular libraries/frameworks like [Akka Streams](https://doc.akka.io/docs/akka/current/stream/index.html), [Project Reactor](https://projectreactor.io/docs), and [RxJava](https://github.com/ReactiveX/RxJava) have incorporated this.
- [**Apache Kafka**](https://kafka.apache.org/) provides a form of backpressure by allowing consumers to pull data at their own pace, rather than having data pushed to them.
- [**Apache Flink**](https://flink.apache.org/) & [**Apache Spark Streaming**](https://spark.apache.org/streaming/) both have mechanisms to handle backpressure to ensure that the streaming jobs can cope with large amounts of data.

### Why is backpressure needed?

Imagine a scenario where you have a producer (or multiple producers) sending data to a consumer for processing. If the producer sends data faster than the consumer can handle, without a controlling mechanism in place, the consumer might be overwhelmed. This could lead to data loss, system crashes, or other undesirable effects.

Backpressure is essentially a feedback mechanism where the consumer can signal the producer about its current capacity. If the consumer is overwhelmed, it can slow down the producer or even stop it temporarily to prevent overloading.

### The benefites and challenges of building backpressure into your pipelines:

Building a backpressure mechanism into your pipeline provides several key benefits:- **Avoids Overloading**: Backpressure prevents downstream systems (consumers) from being overloaded by upstream systems (producers).- **Resource Efficiency**: Instead of wasting resources (like memory) on data that can't be processed immediately, systems can utilize resources more efficiently.- **Enhanced Stability**: It makes the data processing pipeline more resilient and stable, reducing the chances of failures.

This said, there are also challenges associated with this approach:- **Balancing**: Striking the right balance is key. Too much backpressure can make the system slow and underutilized, while too little can lead to overloading and potential data loss.- **Propagation**: In complex systems with multiple stages, backpressure needs to be propagated correctly through all components to be effective.- **Latency**: Introducing backpressure can add latency, especially if data has to wait before being processed.

### Approaches to implementing ("applying") backpressure:

There are a number of techniques for implementing a backpressure mechanism in data flows:- **Buffering**: Introduce buffers (or queues) between the producer and consumer. When the buffer reaches a threshold, it can signal the producer to slow down.- **Rate Limiting**: Limit the rate at which a producer can send data to the consumer.- **Dynamic Adjustment**: Some systems can dynamically adjust the rate of data flow based on the current state of the consumer.- **Acknowledgements**: The consumer sends an acknowledgment after processing a piece of data. The producer waits for this acknowledgment before sending more data.

In conclusion, backpressure is a fundamental concept in data engineering, especially in real-time processing systems. It helps in ensuring that systems are resilient, efficient, and that they can handle variable data loads effectively.

### An example of backpressure in data pipelines using Python

Here's a simple example using Python to demonstrate a backpressure mechanism. In this example, we'll use Python's `queue` for buffering and `threading` to simulate data producers and data consumers.  In this example:

- We have a shared `buffer` (of size 5) between the producer and consumer.
- The producer generates a random number and tries to put it into the buffer.
- If the buffer is full, the producer waits.
- The consumer tries to consume data from the buffer.
- If the buffer is empty, the consumer waits.
- The consumer sleeps for a slightly longer random time to simulate backpressure, i.e., the consumer processing items slower than the producer produces them.

Note: This example will keep on running until you manually interrupt it.

```python
import queue
import threading
import time
import random

class Producer(threading.Thread):
    def __init__(self, buffer):
        super().__init__()
        self.buffer = buffer

    def run(self):
        while True:
            item = random.randint(1, 100)
            print(f"Producing {item}")
            
            # If the buffer is full, wait before producing more
            while self.buffer.full():
                print("Buffer full, waiting...")
                time.sleep(1)
            
            self.buffer.put(item)
            time.sleep(random.random())  # Sleep for a random time

class Consumer(threading.Thread):
    def __init__(self, buffer):
        super().__init__()
        self.buffer = buffer

    def run(self):
        while True:
            # If the buffer is empty, wait
            while self.buffer.empty():
                print("Buffer empty, waiting for items...")
                time.sleep(1)
            
            item = self.buffer.get()
            print(f"Consuming {item}")
            time.sleep(1 + random.random())  # Sleep for a slightly longer random time to simulate backpressure

if __name__ == "__main__":
    buffer = queue.Queue(maxsize=5)  # Limited size buffer to store data items

    producer = Producer(buffer)
    consumer = Consumer(buffer)

    producer.start()
    consumer.start()

    producer.join()
    consumer.join()
```

When you run the program, you'll notice periods where the producer has to wait because the buffer is full, illustrating the backpressure mechanism in action.The output will look something like this:

```plaintext
Producing 82
Buffer full, waiting...
Consuming 24
Producing 97
Buffer full, waiting...
Consuming 99
Producing 100
```

---

## Backup

> Create a copy of data to protect against loss or corruption.

Source: https://dagster.io/glossary/data-backup

### Definition of 'Backup'

In data engineering, both backing up and archiving are processes that involve storing data for long-term retention or backup purposes. However, there are some differences between the two concepts.

*Backing up* involves creating a copy of data to protect against data loss due to accidental deletion, hardware failure, or other types of disasters. The primary goal of backing up data is to ensure that it can be recovered quickly and easily in the event of a problem. Backups are typically performed on a regular basis (e.g. daily, weekly, or monthly) and are often stored in a different location than the primary data to protect against site-level disasters.

[*Archiving*](https://dagster.io/glossary/data-archiving), on the other hand, involves moving data that is no longer actively used from its original location to a secondary storage location for long-term retention. Archiving is often used for compliance, legal, or historical reasons, or to free up primary storage space for more frequently accessed data. Archived data is typically accessed less frequently than primary data and is often stored on cheaper, slower storage devices.

#### Backing up vs. archiving: a question of frequency of access

While backing up and archiving can serve similar purposes, the main difference is the frequency of access to the data. Backups are designed for quick and easy recovery of data in the event of a problem, while archives are intended for long-term retention of data that is no longer actively used.

Backing up and archiving data in data engineering typically involves different techniques and processes.

*Backing up data* usually involves making a copy of the data at a specific point in time and storing it in a separate location, such as a backup server or cloud storage. Backup techniques can include full backups, differential backups, incremental backups, and continuous backups. Backup frequency, retention periods, and restoration processes are also important considerations.

*Archiving data*, on the other hand, involves moving data that is no longer actively used from its original location to a secondary storage location for long-term retention. Archiving techniques can include compressing and encrypting the data, creating metadata for search and retrieval, and setting retention policies. Archiving may involve different storage options, such as tape or disk storage, and may use different tools or technologies for data movement and management.

While both backing up and archiving data involve storing data for long-term retention, the goals, methods, and techniques for each process are different, and typically require different tools and technologies.

---

## Batch Processing

> Process large volumes of data all at once in a single operation or batch.

Source: https://dagster.io/glossary/batch-processing

### Definition of batch processing:

Batch processing in data engineering refers to the practice of processing large volumes of data all at once in a single operation or 'batch'. This method is often utilized when the data doesn't need to be processed in real-time and can be processed without the need for user interaction. Batch processing is most effective when dealing with large amounts of data where it's more efficient to load and process the data in chunks, rather than one record at a time.

### Batch processing steps

Batch processing usually involves four main steps:

1. Gathering data: Data is accumulated over a period of time.
2. Input: The collected data is loaded into the system.
3. Processing: The system executes the pre-defined process.
4. Output: The final result is delivered, which could be a report, updates to a database, or other types of output.

### Example of batch processing in Python

For example, consider a bank that needs to update its customers' balances overnight. Rather than updating each balance as a separate task, the bank could gather all transactions made during the day and process them together in a single batch during off-peak hours.

Here's a simple Python script that illustrates the concept of batch processing by reading a large CSV file in chunks, performing a calculation, and then saving the results to a new CSV file.

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Say we have a file `large_input.csv` as follows:

```plaintext
column1,column2
10,5
20,4
30,3
40,2
50,1
15,6
25,7
35,8
45,9
55,10
...
```

Our batch processing script might look like this:

```python
import pandas as pd

chunksize = 10 ** 6  # Number of lines to process at each iteration
i = 0

for chunk in pd.read_csv('large_input.csv', chunksize=chunksize):
    # Perform some kind of processing, for example, adding a new column which is a calculation of two existing columns
    chunk['new_column'] = chunk['column1'] * chunk['column2']

    if i == 0:
        # Save the first chunk to the output file, creating it
        chunk.to_csv('output.csv', index=False)
    else:
        # Append subsequent chunks to the output file
        chunk.to_csv('output.csv', mode='a', header=False, index=False)

    i += 1
```

This script reads the `large_input.csv` file in chunks, processes each chunk by performing a calculation and adding a new column, then writes the processed chunk to `output.csv`. This is a form of batch processing as it allows the script to process a large file that might not fit in memory by breaking it into manageable chunks.

### Best practices in implementing batch processing:

Batch processing can be a highly efficient way to handle large data sets, but like any method, it can be further optimized to improve performance and properly manage costs. Here are some general guidelines to consider when optimizing batch processing in data engineering:

1. **Parallel Processing**: If the batch processing tasks are independent and can be performed in parallel, distributing the work across multiple machines can significantly speed up processing time.
2. **Optimize Chunk Size**: If your system allows you to process data in chunks, finding the right chunk size can improve efficiency. Too small, and the overhead of initiating the process can slow things down. Too large, and you may run into memory issues. The optimal chunk size depends on the specific computations you're performing and your chosen infrastructure.
3. **Schedule During Off-Peak Times**: If possible, schedule batch processing tasks to run during off-peak hours when there's less competition for resources.  This is where an orchestrator like Dagster can greatly improve your efficiency.
4. **Preprocessing**: Reduce the volume of data processed in the batch by performing [filtering](https://dagster.io/glossary/data-filtering), [aggregation](https://dagster.io/glossary/data-aggregation), or downsampling during a [preprocessing step](https://dagster.io/glossary/data-preprocessing).
5. **Optimize Code**: As with any programming task, ensure your code is as efficient as possible. This might include using built-in functions, optimizing your use of data structures, or minimizing expensive operations like disk I/O.
6. **Monitor and Adjust**: Continually monitor the performance of your batch processing tasks. Identify any bottlenecks or areas of inefficiency and adjust as necessary. This might involve upgrading hardware, modifying your algorithms, or changing your data structures.
7. **Error Handling**: Make sure your batch processes can handle errors gracefully. If a single operation fails, it shouldn't cause the entire batch to fail. A well-designed error handling system can also make it easier to identify and fix problems.
8. **Data Partitioning**: [Partitioning](https://dagster.io/glossary/data-partitioning) the data can make the data processing and querying more efficient. This can be based on certain criteria like date, region, etc., which largely depends on how the data is accessed.
9. **Resource Provisioning**: Provision resources according to the batch job needs. Too much or too less can affect the efficiency of the job. Utilizing cloud resources can be advantageous here, as you can scale up resources when required and scale them down when not in use.
10. **Data Caching**: In case the batch job needs to reprocess some data, caching that data can save significant time and resources.

Remember that these are general guidelines, and their effectiveness can vary depending on the specific use case, available resources, and other factors. Always measure and monitor the impact of any changes you make to ensure they're having the desired effect.

### Pros and Cons of batch processing in data engineering

Batch processing, as with all data processing methods, has its own set of pros and cons.  These are equally dependent on the specific use case, available resources, and operational requirements. Here are some rule-of-thumb main advantages and disadvantages:

**Pros of Batch Processing:**

1. **Efficiency**: Batch processing can be more efficient for large volumes of data because you can often perform the same operation once for many pieces of data, rather than repeating the operation for each individual piece of data.
2. **Resource Management**: It allows for better resource utilization as heavy operations can be scheduled for off-peak times.  By [partitioning](https://dagster.io/glossary/data-partitioning) your data and using a data-aware orchestrator like Dagster, you can achieve highly granular control over the execution of your batch processes.
3. **Simplicity**: Batches make error handling and recovery easier because you can often simply rerun the entire batch instead of trying to find and fix individual failed operations.
4. **Cost-effective**: It can be less expensive than real-time processing because it doesn't require the system to be constantly running and waiting for new data to arrive.

**Cons of Batch Processing:**

1. **Latency**: The most significant drawback is the delay in data processing and availability of results, as batch processing is not real-time. This can make it unsuitable for use cases where real-time data analysis is needed.
2. **Error Propagation**: If there's a critical error in the process, the whole batch can fail, and identifying and fixing the problem might require rerunning the entire batch.
3. **Resource Peaks**: Batch processing can cause peak loads on the system when the batches are processed, especially for larger datasets or more complex operations, which could impact other processes.
4. **Data Duplication**: If your data is continually updated and the source data changes before the batch is processed, this can lead to duplicated data or the processing of outdated information.

In modern data pipelines, a combination of batch and real-time (stream) processing is often used to balance the benefits and drawbacks of each. This approach, called Lambda Architecture, provides the latency of real-time processing and the simplicity and robustness of batch processing.

---

## Categorize

> Organizing and classifying data into different categories, groups, or segments.

Source: https://dagster.io/glossary/data-categorization

#### Data categorization definition:

Data categorization involves organizing and classifying data into different categories, groups, or segments.

By grouping related data together, data engineers can more easily analyze, access, and utilize data.

[      Jump to the code example ↓    ](#)

#### Why Categorize Data?

- **Efficiency**: Sorting data into categories makes it easier to process and manage. Instead of dealing with a single massive pool of data, you can work with smaller, related chunks.
- **Security**: Different data categories may have different access requirements. Sensitive data might require stricter access controls compared to public data.
- **Regulation Compliance**: Some regulations might require certain categories of data to be stored or treated differently.
- **Improved Analytics**: Categorizing data can also lead to improved analytics. For example, sales data can be categorized by region, allowing for regional sales analytics.

#### Examples of Data Categorization:

Based on your use case, you might want to organize data into different "buckets" or categories, such as:\* **Private vs. Public**: Private data might be internal company financials, while public data might be publicly available stock prices.\* **Sensitive vs. Non-sensitive**: Data categorized as "sensitive" might include personal information, while non-sensitive data might be aggregated statistics that don't identify individuals.\* **Temporal Data**: Data can be categorized based on time periods, such as real-time, historical, or forecasted data.

#### Approaches to the categorization process:

As data engineers, we aspire to automate the process of data categorization,  yet we find many manual classification steps are still in use, often involving human experts—or even relatively unskilled contractors—sorting and classifying data by hand

This said, intelligent process automation continues to replace manay of the manual approaches. Automated classification uses algorithms or tools to automatically categorize data. This might involve machine learning or rule-based systems.

For more complex tasks, a hybrid approach is sometimes adopted in which a combination of manual and automated methods is used.

Poor data quality or changes in the data formats are the main challenges in fully automating a categorization process.

#### Categorization strategies

One key factor in a successful categorization process is deciding which categories to create in the first place. It's essential to strike a balance between granularity and simplicity. If categories are too broad, they may not provide meaningful differentiation. On the other hand, if they are too specific, the categorization system might become overly complex and hard to manage.

Here are some best strategies to consider:

1. **Understand the Use Case**:

   - **Purpose**: Determine the primary purpose of categorizing the data.
   - **Stakeholder Needs**: Engage with stakeholders, such as analysts, data scientists, and business users, to understand their data needs and how they plan to use the data.
2. **Start with a High-Level Categorization**:

   - Begin with broad categories and then refine into sub-categories if necessary.
3. **Use Existing Standards and Frameworks**:

   - There might be industry standards or frameworks available that provide guidelines on data categorization. For example, in healthcare, there's the International Classification of Diseases (ICD) for categorizing diseases.
4. **Consistency is Key**:

   - Ensure that whatever categorization scheme you adopt is applied consistently across the board. This aids in easy understanding and minimizes confusion.
5. **Adopt a Hierarchical Structure**:

   - A hierarchical categorization allows for more refined data querying and can be especially useful for drill-down analyses. For instance, data could be categorized by region > country > state > city. It also makes your data structure more adaptable (see below).
6. **Future proof your categorization**:

   - The categorization system should be designed to adapt to changing business needs or data types. It should be scalable to accommodate growing data volumes without requiring a complete overhaul. See the section "[making your categorization structure adaptable](#)" below.
7. **Automate Where Possible**:

   - Using machine learning or rule-based algorithms can aid in automatically categorizing data, especially if you're dealing with vast amounts of it.
8. **Review and Iterate**:

   - Periodically review the categories to ensure they remain relevant. Remove obsolete categories and add new ones that emerge with changing business scenarios or data sources.
9. **Avoid Over-Categorization**:

   - While granularity can be helpful, overly specific categories can be counterproductive. It can make the system complex and harder to manage.
10. **Include Metadata and Documentation**:

    - For each category, it's beneficial to have metadata and documentation that describes its purpose, the kind of data it holds, any associated rules, and other relevant details. This aids in clarity and helps users understand the categorization system better.
11. **Engage and Train the Team**:

    - It's essential to engage with the data teams and provide them with training on the categorization system. Their feedback can be invaluable in refining categories.  This is especially true in a hybrid approach where you have manula categorization steps.

Remember, the primary goal of data categorization is to make data more manageable, accessible, and valuable to users. The categorization strategy should align with this goal.  This might sound obvious but many teams become myopic over the steps of classifying data and lose sight of what the end goal was originally.

#### Making your categorization structure adaptable

Adaptability in data categorization is crucial given the ever-evolving nature of data sources, business needs, and analytics requirements. Ensuring that your data categorization system can adapt over time is essential for long-term data management success. Here are some strategies to make data categorization more adaptable:

1. **Modular Design**:

   - Build your categorization system in a modular manner, so adding or removing categories doesn't disrupt the entire system.
2. **Hierarchical Structure**:

   - As recommended above, hierarchical categorization allows for introducing sub-categories or new levels without necessarily overhauling the entire system.
3. **Use of Tags and Labels**:

   - Instead of (or in addition to) rigid categories, use tags or labels. They can be easily added, removed, or changed. This system can adapt to evolving needs, especially for unstructured data.
4. **Flexible Data Structures**:

   - Consider using databases or data platforms that support flexible schema or schema-less data structures, like NoSQL databases. These can easily adapt to changes in categorization.
5. **Automate with Machine Learning**:

   - As mentioned above, Machine learning algorithms can be trained to categorize data the first time around but can also help adjust to changes over time, especially when new data patterns emerge.
6. **Versioning**:

   - Implement a versioning system for your categorization schema. This way, you can introduce changes without disrupting existing categorizations and roll back if necessary.
7. **Documentation and Metadata**:

   - Maintain thorough documentation for the categorization schema and any changes made. Metadata management tools can be used to provide context and history.
8. **Periodic Reviews**:

   - Schedule regular reviews of the categorization system to identify necessary updates or improvements.
9. **Centralized Data Asset Catalog**:

   - Use a centralized data asset catalog that provides a unified view of all data assets and their categorizations. This central view makes it easier to implement and track changes, especially across several teams.
10. **Standardize Processes for Change**:

    - Have a standardized process in place for introducing changes to the categorization system. This could include procedures for proposing, reviewing, approving, and implementing changes.
11. **Backward Compatibility**:

    - Whenever possible, ensure that new categorizations are backward compatible with old ones. This ensures that historical data analyses and systems don't break.
12. **Prototype and Test**:

    - Before rolling out major changes, prototype the new categorization system and test its impact. This can provide insights into potential pitfalls and areas of improvement.
13. **Decouple Systems**:

    - Ensure that your data categorization is not tightly coupled with critical systems. Loose coupling ensures that changes in categorization don't have cascading effects on other parts of the system.  Again, category ids can help.

By integrating adaptability into the very design and maintenance processes of data categorization, you can ensure that your system remains relevant and effective over time, even as business needs and data sources evolve.

#### An example of data categorization in Python

Let's consider an example of using the `pandas` library, along with a basic usage of `scikit-learn` for automated data categorization.

Say you have a dataset of customer transaction records. These records include details like `transaction_amount`, `transaction_date`, and `product_id`. We want to categorize customers into segments based on their transaction behavior: `High Value`, `Medium Value`, and `Low Value`.

Here's a hypothetical approach:

1. Compute the total transaction value for each customer over the past year.
2. Use KMeans clustering (a machine learning algorithm) to segment customers based on their transaction value.
3. Label the clusters as `High Value`, `Medium Value`, and `Low Value` based on cluster centroids.

```python
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

## Sample dataset
data = {
    'customer_id': [1, 2, 3, 4, 5],
    'transaction_amount': [100, 1500, 250, 1200, 650],
    'transaction_date': ['2023-01-10', '2023-01-15', '2023-01-20', '2023-01-25', '2023-01-30'],
    'product_id': [101, 102, 103, 104, 105]
}

df = pd.DataFrame(data)

## Aggregate transaction values by customer
customer_value = df.groupby('customer_id')['transaction_amount'].sum().reset_index()

## Use KMeans clustering to segment customers (assuming 3 segments for simplicity)
scaler = StandardScaler()
scaled_values = scaler.fit_transform(customer_value[['transaction_amount']])

## Explicitly setting n_init to its current default value of 10
kmeans = KMeans(n_clusters=3, n_init=10, random_state=0).fit(scaled_values)
customer_value['segment'] = kmeans.labels_

## Sort segments by their centroids and label
sorted_labels = customer_value.groupby('segment')['transaction_amount'].mean().sort_values(ascending=False).index

label_mapping = {
    sorted_labels[0]: 'High Value',
    sorted_labels[1]: 'Medium Value',
    sorted_labels[2]: 'Low Value'
}

customer_value['segment_label'] = customer_value['segment'].map(label_mapping)

print(customer_value)
```

In this example, the `customer_id` is grouped by its transaction amounts, and then those aggregate amounts are scaled and clustered into segments using KMeans clustering. The centroids of these clusters are used to define which cluster corresponds to "High", "Medium", or "Low" value customers.

The output from our exercise above is as follows:

```plaintext
| Customer ID | Transaction Amount | Segment | Segment Label |
| ----------- | ------------------ | ------- | ------------- |
| 1           | 100                | 2       | Low Value     |
| 2           | 1,500              | 1       | High Value    |
| 3           | 250                | 2       | Low Value     |
| 4           | 1,200              | 1       | High Value    |
| 5           | 650                | 0       | Medium Value  |
```

An experienced data engineer can further refine and expand on this example by incorporating more features, fine-tuning the clustering algorithm, or using other advanced techniques for segmentation.

### Conclusion

In summary, data categorization in data engineering is about bringing order to data, making it more manageable, secure, and useful. Proper categorization is foundational for many data-related tasks and is especially crucial for organizations dealing with large or diverse datasets.

---

## CBOR (Concise Binary Object Representation)

> A binary format encoding data in a more efficient and compact manner than JSON. It is designed to efficiently serialize and deserialize complex data structures without losing schema-free property of JSON.

Source: https://dagster.io/glossary/cbor-concise-binary-object-representation

---

## Checkpointing

> Saving the state of a process at certain points so that it can be restarted from that point in case of failure.

Source: https://dagster.io/glossary/checkpointing

### Definition of checkpointing:

Checkpointing, in the context of data processing and pipeline execution, refers to the practice of saving the state of a process at certain points so that it can be restarted from that point in case of failure, rather than from the beginning. This can be crucial for long-running processes and pipelines, ensuring that progress is not lost and that computation resources are not wasted.

#### Key Aspects of Checkpointing

**Fault Tolerance**: In distributed systems, failures are inevitable. Checkpointing allows a system to recover from failures by restarting the processing from the last saved state rather than reprocessing all the data from scratch. This minimizes data loss and processing time in case of hardware failures, software bugs, or network issues.

**State Management**: The "state" in data processing refers to the intermediate data and computations that a task depends on. Checkpointing involves saving this state periodically so that it can be reloaded and used to resume processing.

**Performance Optimization**: By reducing the amount of reprocessing needed after a failure, checkpointing helps optimize resource usage and overall performance of data pipelines. This is particularly **Consistency**: Checkpointing ensures data consistency by maintaining a coherent state of data processing. If a failure occurs, the system can roll back to the last consistent checkpoint and continue processing, avoiding partial and inconsistent states.

#### Checkpointing Mechanisms

Checkpointing can be implemented in various ways, depending on the system and the specific requirements:

**Event-Based Checkpointing**: Checkpoints are taken based on certain events, such as the completion of a specific data batch or the processing of a certain number of records. This is typically more efficient than time-based checkpointing and more reliable than manual approaches.

**Time-Based or Scheduled Checkpointing**: The system takes checkpoints at regular time intervals. This is simple to implement but might not always be optimal in terms of resource usage.  This is typically an older approach used in slow-moving legacy systems.

**Manual Checkpointing**: Here, developers have control over when checkpoints are created, allowing for more fine-grained control over the process.   This is most suited for one-off system changes and upgrades.

#### Implementation Considerations

When implementing checkpointing, several factors need to be considered:

**Storage**: The choice of storage for checkpoints is crucial. It needs to be reliable and durable, often involving distributed storage systems like AWS S3, or similar.**Frequency**: Deciding the frequency of checkpoints involves a trade-off between fault tolerance and performance. Frequent checkpoints provide better fault tolerance but may incur higher overhead.**State Size**: The size of the state being saved can impact the efficiency of checkpointing. Efficient serialization and compression techniques may be needed to manage large state sizes.

### Checkpointing in Dagster

Dagster implicitly supports a form of checkpointing through asset materializations. This allows for more efficient pipeline execution, as computations can be resumed based on the latest successful asset materializations rather than starting from scratch.

Dagster assets represent units of data that are the outputs of computations. When you define assets in Dagster, you create a graph of these assets, specifying how they depend on one another. Each time an asset is computed or updated ("materialized"), Dagster records this event, effectively creating a checkpoint in the asset's lifecycle. This metadata can serve a similar purpose to checkpoints, as it allows users to understand the state of their data assets at any given time and to restart computations from the last known good state if necessary.

Checkpointing is a technique used in data engineering to save the state of a process at specific intervals. This allows for recovery from failures without having to restart the entire process. Here's an example of checkpointing in a data processing pipeline using Python. We'll simulate a data processing task that reads data in chunks, processes it, and uses checkpointing to save the progress.

### A Python example of Checkpointing

Let's demonstrate checkpointing in Python using a CSV file to simulate data chunks and a checkpoint file to store the progress. First, let's create a sample CSV file with dummy data:

```python
import csv

# Sample data
data = [
    ['id', 'name', 'value'],
    [1, 'Alice', 10],
    [2, 'Bob', 20],
    [3, 'Charlie', 30],
    [4, 'David', 40],
    [5, 'Eve', 50],
    [6, 'Jacob', 60],
    [7, 'Porshe', 70],
    [8, 'Le', 80],
    [9, 'Antoine', 90],
    [10, 'Katarina', 100]
]

# Write sample data to CSV
with open('data.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)
```

#### Checkpointing Example

Now, let's write a Python script to process this data in chunks and use checkpointing to save the progress.  We will build in a delay to give us the chance to interact with the program.

```python
import csv
import os
import time

# Function to read the last checkpoint
def read_checkpoint():
    if os.path.exists('checkpoint.txt'):
        with open('checkpoint.txt', 'r') as file:
            return int(file.read().strip())
    return 0

# Function to write the current checkpoint
def write_checkpoint(checkpoint):
    with open('checkpoint.txt', 'w') as file:
        file.write(str(checkpoint))

# Function to process data
def process_data(row):
    # Simulate data processing
    print(f"Processing row: {row}")

# Main function to read and process data with checkpointing
def main():
    checkpoint = read_checkpoint()
    print(f"Starting from checkpoint: {checkpoint}")

    with open('data.csv', 'r') as file:
        reader = csv.reader(file)
        header = next(reader)  # Skip header

        current_position = 0
        for row in reader:
            if current_position >= checkpoint:
                process_data(row)
                checkpoint = current_position + 1
                write_checkpoint(checkpoint)

                # Delay for 5 seconds to allow interruption
                time.sleep(5)
            current_position += 1

if __name__ == "__main__":
    main()
```

To break down the steps:

**Checkpoint Read/Write Functions**: `read_checkpoint()`: Reads the last checkpoint from the file, and then `write_checkpoint(checkpoint)` writes the current checkpoint to the file.

**Data Processing**: `process_data(row)`: Simulates processing a data row.

**Main Function**: Here we read the last checkpoint, opens the CSV file and skips the header, process data rows starting from the last checkpoint, then update the checkpoint after processing each row.

#### Let's run the Script

If you now execute the two scripts above in sequence, the second one will start from the beginning and process all rows form the test file.You can stop the script mid-execution (e.g., using a keyboard interrupt).When you run the script again, it will resume processing from the last saved checkpoint.

This example demonstrates a simple yet effective way to implement checkpointing in a data processing pipeline, allowing for resumption of work after interruptions without reprocessing already completed data.

Your output may look something like this:

```plaintext
% python checkpoint.py
Starting from checkpoint: 0
Processing row: ['1', 'Alice', '10']
Processing row: ['2', 'Bob', '20']
^C KeyboardInterrupt

% python checkpoint.py
Starting from checkpoint: 2
Processing row: ['3', 'Charlie', '30']
Processing row: ['4', 'David', '40']
Processing row: ['5', 'Eve', '50']
^C KeyboardInterrupt

% python checkpoint.py
Starting from checkpoint: 5
Processing row: ['6', 'Jacob', '60']
^C KeyboardInterrupt

% python checkpoint.py
Starting from checkpoint: 6
Processing row: ['7', 'Porshe', '70']
Processing row: ['8', 'Le', '80']
Processing row: ['9', 'Antoine', '90']
Processing row: ['10', 'Katarina', '100']
```

---

## Clean or Cleanse

> Remove invalid or inconsistent data values, such as empty fields or outliers.

Source: https://dagster.io/glossary/data-cleaning

### Data cleaning definition:

Data cleansing, also known as data cleaning, is the process of identifying and correcting or removing inaccuracies, inconsistencies, and errors in data to improve its quality and reliability. In data engineering, data cleansing is an essential step in the data pipeline to ensure that downstream processes are not negatively affected by bad data.  But poor data cleansing practices can actually negatively impact your data.

### Data cleaning example using Python:

Here are some best practices and practical examples in Python for data cleansing in data engineering:

**Identify and handle missing data**: Use Python libraries such as Pandas and NumPy to detect and handle missing data. The following code snippet shows how to detect missing data using Pandas:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import pandas as pd
import numpy as np

# Create a sample dataset with missing data
df = pd.DataFrame({
   'A': [1, 2, np.nan, 4],
   'B': [5, np.nan, 7, np.nan],
   'C': [8, 9, 10, 11]
})

# Print the original dataset
print("Original dataset:\n", df)

# Detect missing values
print("\nMissing values:\n", df.isnull())

# Remove rows with missing data
df.dropna(inplace=True)

# Print the modified dataset
print("\nModified dataset:\n", df)

# Fill missing values with a specific value
df.fillna(value=0, inplace=True)

# Print the modified dataset
print("\nModified dataset after filling missing values:\n", df)

# Interpolate missing values
df = pd.DataFrame({
   'A': [1, 2, np.nan, 4],
   'B': [5, np.nan, 7, np.nan],
   'C': [8, 9, 10, 11]
})

df.interpolate(method='linear', inplace=True)

# Print the modified dataset
print("\nModified dataset after interpolating missing values:\n", df)
```

In the above example, we create a sample dataset with missing data and use Pandas and NumPy to detect and handle missing data. We first print the original dataset and then detect missing values using the `isnull()` method. Next, we remove rows with missing data using the `dropna()` method and print the modified dataset. We then fill missing values with a specific value using the `fillna()` method and print the modified dataset again. Finally, we interpolate missing values using the `interpolate()` method and print the modified dataset.

It will output the following:

```plaintext
Original dataset:
      A    B   C
0  1.0  5.0   8
1  2.0  NaN   9
2  NaN  7.0  10
3  4.0  NaN  11

Missing values:
        A      B      C
0  False  False  False
1  False   True  False
2   True  False  False
3  False   True  False

Modified dataset:
      A    B  C
0  1.0  5.0  8

Modified dataset after filling missing values:
      A    B  C
0  1.0  5.0  8

Modified dataset after interpolating missing values:
      A    B   C
0  1.0  5.0   8
1  2.0  6.0   9
2  3.0  7.0  10
3  4.0  7.0  11
```

**Remove duplicates**: Use Python libraries such as Pandas to remove duplicate records from the data. The following code snippet shows how to remove duplicates using Pandas:

Assuming your starting file `data.csv` is as follows (note the duplicate records):

```plaintext trailing-newline
| Patient ID | Age | Name         | Zip/Postal Code | Address              | Social Security Number | Income |
| ---------- | --- | ------------ | --------------- | -------------------- | ---------------------- | ------ |
| 12         | 46  | James Bond   | BD74AD          | 27 Ardmore Terrace   | CN142371               | 1 000  |
| 13         | 32  | William Tell | 21209           | 23 Archery Road      | 321-65-6278            | 1 000  |
| 14         | 61  | Cheryl Crow  | 88905           | 1 Sunset Boulevard   | 788-52-1876            | 1 000  |
| 13         | 32  | William Tell | 21209           | 23 Archery Road      | 321-65-6278            | 1 000  |
| 15         | 66  | Marie Curie  | 78690           | 10 Rue Maurice Ravel | FR2939793              | 10 000 |
| 15         | 66  | Marie Curie  | 78690           | 10 Rue Maurice Ravel | FR2939793              | 10 000 |
| 12         | 46  | James Bond   | BD74AD          | 27 Ardmore Terrace   | CN142371               | 1 000  |
```

You can remove the duplicates as follows:

```python
import pandas as pd

df = pd.read_csv('data.csv')
# remove duplicates
df.drop_duplicates(inplace=True)
print(df)
```

You output will be:

```plaintext
| Patient ID | Age | Name         | ZIP/Postal Code | Address              | Social Security Number | Income |
| ---------- | --- | ------------ | --------------- | -------------------- | ---------------------- | -----: |
| 12         | 46  | James Bond   | BD74AD          | 27 Ardmore Terrace   | CN142371               |  1 000 |
| 13         | 32  | William Tell | 21209           | 23 Archery Road      | 321-65-6278            |  1 000 |
| 14         | 61  | Cheryl Crow  | 88905           | 1 Sunset Boulevard   | 788-52-1876            |  1 000 |
| 15         | 66  | Marie Curie  | 78690           | 10 Rue Maurice Ravel | FR2939793              | 10 000 |
```

PyJanitor is another library that provides a set of functions for cleaning and tidying data. It includes functions for identifying and removing duplicates, converting data types, and handling missing values.

```python
import pandas as pd
import janitor

df = pd.read_csv('data.csv')
df = df.clean_names() # cleans column names
df = df.drop_duplicates() # removes duplicate rows
df = df.fillna(0) # fills missing values with 0
print(df)
```

Which will yield the following output:

```plaintext
   feature_1  feature_2
0          1          2
1          2          4
2          3          6
3          4          8
4          5         10
```

**Standardize data**: Use Python libraries such as Pandas and scikit-learn to standardize data. The following code snippet shows how to standardize data using scikit-learn:

Given the input file:

```plaintext
A,B,C
101,211,0.98
312,456,0.97
105,776,0.89
0.07,809,0.98
9,1034,0.99
```

Running the following:        

```python
import pandas as pd
from sklearn.preprocessing import StandardScaler

data = pd.read_csv('data.csv')
scaler = StandardScaler()

# standardize the data
standardized_data = scaler.fit_transform(data)
print(standardized_data)
```

Will yield the following output:

```plaintext
[[-0.03929484 -1.54190454  0.49245809]
 [ 1.83909466 -0.69527385  0.21887026]
 [-0.00368556  0.41052949 -1.96983236]
 [-0.93780599  0.52456546  0.49245809]
 [-0.85830827  1.30208344  0.76604592]]
```

**Remove outliers:** Use Python libraries such as Pandas and NumPy to identify and remove outliers from the data. The following code snippet shows how to remove outliers using Pandas:

Given the input file:

```plaintext
column_name
0.123
0.129
0.110
0.19
0.134
0.09
```

The following operation…

```python
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')

# remove outliers
df = df[(np.abs(df['column_name'] - df['column_name'].mean()) / df['column_name'].std()) < 3]
```

… will remove the two datapoints deemed outliers, and will yield the following output:

```plaintext
   column_name
0        0.123
1        0.129
2        0.110
4        0.134
```

**Handle inconsistent data**: Use Python libraries such as Pandas to handle inconsistent data, such as different data types or formats. The following code snippet shows how to handle inconsistent data using Pandas:

```python
import pandas as pd

df = pd.read_csv('data.csv')

# convert data type of column to float
df['column_name'] = df['column_name'].astype(float)
```

---

## Cluster

> Group data points based on similarities or patterns to facilitate analysis and modeling.

Source: https://dagster.io/glossary/data-clustering

### Data clustering definition:

Data clustering is a technique that groups similar data points into clusters based on their similarities. This can be useful in various data engineering applications such as customer segmentation, anomaly detection, and image segmentation.

### data clustering example in Python:

In Python, there are several libraries that can be used for data clustering, including scikit-learn, KMeans, and DBSCAN.

Here are some practical examples of using these libraries for data clustering:

#### **K-Means clustering:**

K-Means is a popular algorithm used for clustering data. We can apply K-Means clustering against a synthetic dataset with 300 samples, 4 clusters, and a specified random state for reproducibility as follows:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# Create synthetic dataset
n_samples = 300
random_state = 42
X, y = make_blobs(n_samples=n_samples, centers=4, random_state=random_state, cluster_std=1.2)

# Perform K-Means clustering
k = 4  # Number of clusters
kmeans = KMeans(n_clusters=k, random_state=random_state, n_init=1)
kmeans.fit(X)

# Visualize the results
plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=200, c='red', marker='x', label='Cluster Centers')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('K-Means Clustering with k = {}'.format(k))
plt.legend()
plt.show()
```

which will vizualize the clustering as follows:

![](https://dagster.io/images/glossary/681399f654933b29e12fba3d_glossary-8.png)

This is a fairly simple example of K-Means clustering.  [Scroll down](#) for a more sophisticated example.

#### **Hierarchical clustering:**

Hierarchical clustering is a technique that creates a tree-like structure of clusters.

Here is one example of Hierarchical clustering using a synthetic dataset. You can modify the dataset, linkage method, or clustering parameters to experiment with different datasets and see how Hierarchical clustering works on various data.

```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.cluster import AgglomerativeClustering

# Create synthetic dataset
n_samples = 300
random_state = 42
X, y = make_blobs(n_samples=n_samples, centers=4, random_state=random_state, cluster_std=1.2)

# Standardize the dataset
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Perform hierarchical clustering using complete linkage
linkage_matrix = linkage(X_scaled, method='complete')

# Plot dendrogram
plt.figure(figsize=(10, 5))
dendrogram(linkage_matrix, truncate_mode='level', p=3)
plt.title('Dendrogram')
plt.xlabel('Sample Index')
plt.ylabel('Distance')
plt.show()

# Choose the number of clusters based on the dendrogram
n_clusters = 4

# Perform Agglomerative Clustering with the chosen number of clusters
agg_clustering = AgglomerativeClustering(n_clusters=n_clusters)
agg_clustering.fit(X_scaled)

# Visualize the results
plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=agg_clustering.labels_, cmap='viridis', alpha=0.7)
plt.xlabel('Feature 1 (Standardized)')
plt.ylabel('Feature 2 (Standardized)')
plt.title('Hierarchical Clustering with {} Clusters'.format(n_clusters))
plt.show()
```

This script performs the following steps:

1. Import necessary libraries: NumPy, Matplotlib, scikit-learn, and SciPy.
2. Create a synthetic dataset with 300 samples, 4 clusters, and a specified random state for reproducibility.
3. Standardize the dataset using StandardScaler.
4. Perform hierarchical clustering using complete linkage method.
5. Plot the dendrogram to visualize the hierarchical clustering structure.
6. Choose the number of clusters based on the dendrogram.
7. Perform Agglomerative Clustering with the chosen number of clusters.
8. Visualize the results, including the cluster assignments:

![](https://dagster.io/images/glossary/681399f654933b29e12fba1e_glossary-11.png)

Both k-means and hierarchical clustering are good techniques to explore if you are new to data clustering.

#### **DBSCAN clustering**

DBSCAN is another popular clustering algorithm that can be useful for detecting outliers in data. Here's an example of using DBSCAN to cluster customer data based on their spending habits:

```python
from sklearn.cluster import DBSCAN
import pandas as pd

# load customer spending data
data = pd.read_csv('input.csv')

# extract relevant features
features = ['col_A', 'col_B', 'col_C', 'col_D']
X = data[features]

# perform DBSCAN clustering with eps=3 and min_samples=2
dbscan = DBSCAN(eps=3, min_samples=2).fit(X)

# add cluster labels to the original data
data['cluster'] = dbscan.labels_

# save results to a new CSV file
data.to_csv('output.csv', index=False)
```

These are just a few examples of how data clustering can be used in data pipelines using Python. Other techniques include

1. [Hierarchical Clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering)
2. [Density-Based Spatial Clustering of Applications with Noise](https://en.wikipedia.org/wiki/DBSCAN) (DBSCAN)
3. [Expectation-Maximization (EM) Clustering](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
4. [Fuzzy C-Means Clustering](https://en.wikipedia.org/wiki/Fuzzy_clustering)
5. [Affinity Propagation Clustering](https://en.wikipedia.org/wiki/Affinity_propagation)
6. [Spectral Clustering](https://en.wikipedia.org/wiki/Spectral_clustering)
7. [Subspace Clustering](https://en.wikipedia.org/wiki/Clustering_high-dimensional_data#Subspace_clustering)
8. [Grid-Based Clustering](https://en.wikipedia.org/wiki/Cluster_analysis#Grid-based_clustering)

… and many more [Model-Based Clustering techniques](https://en.m.wikipedia.org/wiki/Cluster_analysis#Definition)

The choice of algorithm and parameters will depend on the specific problem being solved and the characteristics of the data.

#### A more sophisticated example of K-Means clustering in Python:

Here's a more elaborate example of K-Means clustering in Python using the popular machine learning library, scikit-learn. In this example, we'll apply K-Means clustering to the Iris dataset and evaluate the quality of the clustering using various metrics.

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris
from sklearn.metrics import silhouette_score, adjusted_rand_score
from sklearn.preprocessing import StandardScaler

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Standardize the dataset
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Determine the optimal number of clusters using the elbow method and silhouette scores
inertia = []
silhouette_scores = []
k_values = list(range(2, 11))

for k in k_values:
    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
    kmeans.fit(X_scaled)
    inertia.append(kmeans.inertia_)
    silhouette_scores.append(silhouette_score(X_scaled, kmeans.labels_))

# Plot the elbow method and silhouette scores
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))

ax1.plot(k_values, inertia, 'o-')
ax1.set_title('Elbow Method')
ax1.set_xlabel('Number of Clusters (k)')
ax1.set_ylabel('Inertia')

ax2.plot(k_values, silhouette_scores, 'o-')
ax2.set_title('Silhouette Scores')
ax2.set_xlabel('Number of Clusters (k)')
ax2.set_ylabel('Silhouette Score')

plt.show()

# Choose the optimal number of clusters based on the elbow method and silhouette scores
optimal_k = 3

# Perform K-Means clustering with the optimal number of clusters
kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)
kmeans.fit(X_scaled)

# Visualize the results
plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=kmeans.labels_, cmap='viridis', alpha=0.7)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=200, c='red', marker='x', label='Cluster Centers')
plt.xlabel('Sepal Length (Standardized)')
plt.ylabel('Sepal Width (Standardized)')
plt.title('K-Means Clustering with k = {}'.format(optimal_k))
plt.legend()
plt.show()

# Evaluate the clustering
silhouette = silhouette_score(X_scaled, kmeans.labels_)
ari = adjusted_rand_score(y, kmeans.labels_)
print("Silhouette Score: {:.3f}".format(silhouette))
print("Adjusted Rand Index: {:.3f}".format(ari))
```

This script performs the following steps:

1. Import necessary libraries: NumPy, pandas, Matplotlib, and scikit-learn.
2. Load the Iris dataset and extract features and target labels.
3. Standardize the dataset using `StandardScaler`.
4. Determine the optimal number of clusters (k) using the elbow method and silhouette scores.
5. Plot the elbow method and silhouette scores to visualize the optimal number of clusters.
6. Perform K-Means clustering with the optimal number of clusters.
7. Visualize the results, including the cluster assignments and cluster centers.
8. Evaluate the quality of the clustering using silhouette scores and adjusted Rand index.

In this example, we use the elbow method and silhouette scores to determine the optimal number of clusters for K-Means clustering. We then perform the clustering with the optimal number of clusters and visualize the results. Finally, we evaluate the quality of the clustering using silhouette scores and adjusted Rand index.

In this example, we use the elbow method and silhouette scores to determine the optimal number of clusters for K-Means clustering. We then perform the clustering with the optimal number of clusters and visualize the results. Finally, we evaluate the quality of the clustering using silhouette scores and adjusted Rand index.

By running the script, you can observe the elbow method and silhouette scores plots that help determine the optimal number of clusters.

![](https://dagster.io/images/glossary/681399f654933b29e12fb9fd_glossary-9.png)

![](https://dagster.io/images/glossary/681399f654933b29e12fbaef_glossary-10.png)

In this case, `k=3` seems to be a good choice. The visualization of the clustering result shows the different clusters and their centers.

The Silhouette Score and Adjusted Rand Index are used to measure the quality of the clustering.

```plaintext
Silhouette Score: 0.460
Adjusted Rand Index: 0.620
```

A higher Silhouette Score indicates better separation between clusters, while the Adjusted Rand Index measures the similarity between the true labels and the predicted cluster assignments. In this case, the Silhouette Score and Adjusted Rand Index provide an indication of the quality of the clustering result.

You can modify the dataset or clustering parameters to experiment with different datasets and see how K-Means clustering works on various data.

---

## Combinatorial Explosion

> A phenomenon in computer science where the number of possible solutions or combinations in a problem grows exponentially with the size of the problem.

Source: https://dagster.io/glossary/combinatorial-explosion

---

## Compact

> Reducing the size of data while preserving its essential information.

Source: https://dagster.io/glossary/data-compaction

#### Data compaction definition:

In data engineering, **data compaction is the process of reducing the size of data while preserving its essential information**. It is used in data management and storage optimization, especially when dealing with large volumes of data. Compaction is commonly used in databases, file systems, and data storage systems to reduce storage costs, and improve data access speeds.

Compaction enables efficient use of resources, enhances system performance, and ensures that data remains accessible and valuable over time.

#### Compaction in data engineering:

Compaction may involve one of several approaches:

**Data Storage Reduction**: Compaction aims to reduce the amount of storage space required to store data. This is achieved by identifying and eliminating redundant or unnecessary information within the data while retaining the valuable content.

**Compression**: One of the common techniques used in compaction is data compression. Data compression algorithms like gzip, LZ4, or Snappy are applied to data to shrink its size by encoding it in a more space-efficient format. Compressed data can be decompressed on-the-fly when needed for analysis or retrieval, although this extra step has its own performance trade offs.  See [Data Compression](https://data-compression) entry. We provide an example of [Data Compression using Python below](#).

**Data Merging**: In some data storage systems, especially those based on log-structured or columnar storage, compaction involves merging smaller data segments or files into larger ones. This consolidation reduces the overhead of managing numerous smaller files and improves read and write performance. See the entry on [Data Merging](https://data-merging).

**Aggregation**: Compaction may involve aggregating data at different levels of granularity. For example, daily data may be aggregated into weekly or monthly summaries, reducing the overall data volume. Aggregating data can also improve query performance for analytical workloads. See [Aggregation](https://data-aggregation) entry.

**Data Cleanup**: Another aspect of compaction involves data cleanup and removal of obsolete or duplicate records. This helps maintain data quality and ensures that only relevant information is retained. See [Cleanse](https://data-cleaning) and [Deduplication](https://data-deduplication) entries.

**Index Optimization**: In databases, compaction may include optimizing data structures like indexes. Indexes can be rebuilt or reorganized to reduce fragmentation and improve query performance.  See [Data Indexing](https://data-indexing) entry.

**Time-Series Compaction**: In time-series databases, compaction techniques are often applied to efficiently store historical data. Older data points may be downsampled or aggregated to reduce storage requirements while still preserving important trends.  We provide an example of [time-series downsampling below](#).

**Data Lifecycle Management**: Compaction is often part of data lifecycle management, where data is archived, moved to lower-cost storage tiers, or deleted when it is no longer actively needed. This helps in cost optimization and compliance with data retention policies.

**Consistency**: In distributed data systems, compaction ensures data consistency by removing outdated or conflicting data versions, helping maintain the integrity of distributed databases.

### Data compaction vs. data compression vs. data reduction

While related, compaction, compression and reduction are slightly different techniques:

```javascript trailing-newline
| **Category**     | **Compaction**                                                                                                                            | **Compression**                                                                                   | **Reduction**                                                                                                                 |
| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| **Aim**          | Minimize storage space while preserving data contents and structure.                                                                      | Reduce data size by encoding it in a more space-efficient format, minimizing transfer or storage. | Reduce the overall amount of data while retaining its critical information.                                                   |
| **Techniques**   | Data compression, merging smaller data segments into larger ones, and optimizing data structures (e.g., indexes) to reduce fragmentation. | Encoding data in a compressed form to represent it in fewer bits.                                 | Aggregation, downsampling, encoding, filtering out less relevant data points, dimensionality reduction for feature selection. |
| **Key Benefits** | Improve storage efficiency and reduce costs.                                                                                              | Reduce data transfer times, save storage space, and improve data transmission efficiency.         | Improve data manageability, simplify analytics, and speed up query performance.                                               |
```

In summary, data compaction is a broader concept that encompasses various techniques aimed at minimizing storage space while preserving data integrity and structure. It often involves consolidation, aggregation, and optimization of data. All three concepts are essential in modern data engineering, and they may be used together to achieve efficient data storage, transmission, and processing.

### Two code examples of Data Compaction in Python

As mentioned above, there are lots of techniques we can employ in data compaction. Let's look at two: compression and time-series downsampling. Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

#### Compaction via compression

Let's create a self-contained Python example that demonstrates data compaction using a common compression library called `gzip`. In this example, we will compress and decompress data to illustrate how data compaction reduces the storage space required for the same dataset.

```python
import gzip
import io

## Sample data to compact
data_to_compact = """
Dagster is different from other data orchestrators.
It’s the first orchestrator built to be used at every stage of the data development lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.
And it’s the first orchestrator that includes software-defined assets - it frees up teams to think about critical data assets they’re trying to build and let the orchestrator manage the tasks.
"""

## Step 1: Compress the data
compressed_data = io.BytesIO()
with gzip.GzipFile(fileobj=compressed_data, mode='wb') as f:
    f.write(data_to_compact.encode('utf-8'))

compressed_data.seek(0)

## Step 2: Decompress the data
decompressed_data = io.BytesIO()
with gzip.GzipFile(fileobj=compressed_data, mode='rb') as f:
    decompressed_data.write(f.read())

## Step 3: Print the results
original_data_size = len(data_to_compact)
compressed_data_size = compressed_data.tell()
decompressed_data.seek(0)
decompressed_data_text = decompressed_data.read().decode('utf-8')

print("Original Data Size:", original_data_size, "bytes")
print("Compressed Data Size:", compressed_data_size, "bytes")
print("Decompressed Data Size:", len(decompressed_data_text), "bytes")
print("\nOriginal Data:")
print(data_to_compact)
print("\nDecompressed Data:")
print(decompressed_data_text)
```

In this example:

1. We first create some sample text data (`data_to_compact`).
2. We use the `gzip` library to compress the data and save it to `compressed_data`.
3. Then, we decompress the data from `compressed_data` and store it in `decompressed_data`.
4. Finally, we compare the original data size, compressed data size, and decompressed data size, and we print both the original and decompressed data to verify that the essential information is preserved.

You can run this code to see how data compaction through compression reduces the storage space required for the same dataset while preserving its content.

#### Compaction via time-series downsampling

Time-series compaction, often referred to as downsampling or aggregation, is a common task for data engineers working with large volumes of time-series data. In this example, we'll use Python and the Pandas library to demonstrate how to downsample time-series data to reduce its granularity while preserving essential information.

For this example, we'll work with simulated temperature data measured every minute and downsample it to hourly averages.

```python
import pandas as pd
import numpy as np

def mem_usage(dataframe):
    if isinstance(dataframe, pd.DataFrame):
        # Get memory usage of each column
        memory_usage = dataframe.memory_usage(deep=True)
        # Calculate the total memory usage of the DataFrame in bytes
        total_memory_usage = memory_usage.sum()
        # Convert bytes to megabytes (MB) for a more readable output
        total_memory_usage_mb = total_memory_usage / (1024 * 1024)  # 1 MB = 1024 * 1024 bytes
        return (f"Total memory usage of the DataFrame: {total_memory_usage} bytes ({total_memory_usage_mb:.2f} MB)")
    else:
        return False

## Generate sample time-series data (simulated temperature readings)
np.random.seed(42)
date_range = pd.date_range(start='2024-01-01', end='2024-01-30', freq='T')
temperature_data = np.random.uniform(0, 100, size=(len(date_range),))
temperature_df = pd.DataFrame({'datetime': date_range, 'temperature': temperature_data})

## Display the first few rows of the original data
print("Original Data:")
print(temperature_df)
bytes_used = mem_usage(temperature_df)
if bytes_used is not False:
    print(bytes_used)

## Downsample the data to hourly averages
hourly_avg_temperature_df = temperature_df.resample('H', on='datetime').mean()

## Display the first few rows of the downsampled data
print("\nDownsampled Data (Hourly Averages):")
print(hourly_avg_temperature_df)
bytes_used = mem_usage(hourly_avg_temperature_df)
if bytes_used is not False:
    print(bytes_used)
```

In this example:

1. We generate simulated time-series data for temperature readings over a period of ten days, with measurements taken every minute.
2. We create a Pandas DataFrame to store this data, with columns 'datetime' for timestamps and 'temperature' for temperature values.
3. We then downsample the data to hourly averages using the `resample` method with a frequency of '`H`' (hourly) and applying the `mean` function for aggregation. You can use other aggregation functions like `sum`, `min`, or `max` depending on your specific requirements.  You can try changing this to '`D`' for daily aggregations.
4. Finally, we print the first and last few rows of both the original and downsampled data to see the difference in granularity.

Here is the output our example will generate (although the temperature values will be randomized):

```plaintext
Original Data:
                 datetime  temperature
0     2024-01-01 00:00:00    37.454012
1     2024-01-01 00:01:00    95.071431
2     2024-01-01 00:02:00    73.199394
3     2024-01-01 00:03:00    59.865848
4     2024-01-01 00:04:00    15.601864
...                   ...          ...
41756 2024-01-29 23:56:00    55.088656
41757 2024-01-29 23:57:00    69.560490
41758 2024-01-29 23:58:00    40.454901
41759 2024-01-29 23:59:00    73.148175
41760 2024-01-30 00:00:00    16.333289

[41761 rows x 2 columns]
Total memory usage of the DataFrame: 668304 bytes (0.64 MB)

Downsampled Data (Hourly Averages):
                     temperature
datetime
2024-01-01 00:00:00    46.750077
2024-01-01 01:00:00    48.671940
2024-01-01 02:00:00    46.212512
2024-01-01 03:00:00    50.480704
2024-01-01 04:00:00    55.487067
...                          ...
2024-01-29 20:00:00    51.402151
2024-01-29 21:00:00    48.945141
2024-01-29 22:00:00    49.935838
2024-01-29 23:00:00    44.468143
2024-01-30 00:00:00    16.333289

[697 rows x 1 columns]
Total memory usage of the DataFrame: 11152 bytes (0.01 MB)
```

Note the major gain in terms of memory usage (0.01MB vs. 0.64MB), but downsampling does imply that we have also lost the granularity of the original dataset. It's crucial to choose the right aggregation method and frequency depending on the nature of the data and your analysis goals.

---

## Compress

> Reduce the size of data to save storage space and improve processing performance.

Source: https://dagster.io/glossary/data-compression

#### Data compression definition:

Data compression is important in the context of data pipelines because it can help reduce the amount of storage space required, which can result in faster processing times and lower costs.

#### Data compression examples in Python:

In Python, there are several built-in modules and libraries that can be used for data compression.  The two main ones are gzip and zipfile.  Both modules are included in the standard library of Python, which means they are built into Python by default and can be used without the need to install any external packages or modules.

The[ gzip module](https://docs.python.org/3/library/gzip.html) provides a simple way to compress and decompress files using the gzip format, which is a common file format used for compressing files on Unix-based systems. The module provides functions to read and write gzip files, as well as utilities to compress and decompress data in memory.

The [ZipFile module](https://docs.python.org/3/library/zipfile.html)  provides a way to create, read, and extract files from ZIP archives. ZIP files are a popular way to package multiple files into a single file, and the zipfile module makes it easy to work with these files in Python. The module provides functions to create new ZIP files, add files to existing ZIP files, and extract files from ZIP files.

##### gzip

Here is an example of gzip as used in code. Please note that you need to [have gzip installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import gzip

## compress a file
with open('data.txt', 'rb') as f_in, gzip.open('data.txt.gz', 'wb') as f_out:
    f_out.writelines(f_in)

## decompress a file
with gzip.open('data.txt.gz', 'rb') as f_in, open('data2.txt', 'wb') as f_out:
    f_out.writelines(f_in)
```

Given an input file `data.txt` , this will generate a compressed version of the file.  It will then decompress the same file back into a new `data2.txt` file.

If you open the compressed file in a text editor you will notice it is hard to read and looks something like this:

```plaintext
��@d�data.txtE�a�$�
D{�(((���{Jz�96bg��
��J�������8s>�?���������ۿٞ1��9�xϷ�?��<��oO~���kc����zy���{��y������X�<��o�;���~���c�^�����6��,_��i�{����u�V?�w���������Go�~���;���6yK�����,����?�~{���y�sxt-y�w��5�����g��}�d+�}����i븀�'࣋e�o�5��k���s+�
������y���w�c��k�Ƴ���n�1���S_�,�������kO��pJ�֡�=O=����3Z=�=��i�e���w�ٲ�f+s�O/��޲��gs�ݕΣ&�iX����÷9����V=���'-۫|�{�����|�F<��j���^/]Zꩅ��1�sV�㝝ӫ�n,���Z��z6xW-�u����=���}g�z���
��w��;W��X��Y��7��/�:~�@��xm6�G���;�>?��'�1W�_�
```

You will also notice that data.txt and data2.txt are the exact same size, while data.txt.gz is a smaller size:

```plaintext
$ ls -la
-rw-r--r--   1 16893 Apr 19 11:00 data.txt
-rw-r--r--   1   7952 Apr 19 11:00 data.txt.gz
-rw-r--r--   1 16893 Apr 19 11:00 data2.txt
```

##### ZipFile

Next, here is an example of ZipFile as used in code.  Assuming we have two arbitrary files (`data1.txt` and `data2.txt`), we can add these to a zipfile compressed file.  Then we will extract them from the file into a new folder:

```python
import zipfile

## create a zip file
with zipfile.ZipFile('data.zip', 'w', compression=zipfile.ZIP_DEFLATED) as f:
    f.write('data1.txt')
    f.write('data2.txt')

## extract files from a zip file
with zipfile.ZipFile('data.zip', 'r') as f:
    f.extractall('newfolder')
```

While gzip and ZipFile are the most frequently used, they are not the only options out there.  For example, the **bz2** module provides a way to compress and decompress files using the [bzip2 format](https://en.wikipedia.org/wiki/Bzip2), which can achieve higher compression ratios than gzip. It is commonly used for compressing large text files.  Bzip2 is also built into current Python distributions.

Let’s look at an example:

```python
import bz2

## compress a file
with open('data.txt', 'rb') as f_in, bz2.open('data.txt.bz2', 'wb') as f_out:
    f_out.writelines(f_in)

## decompress a file
with bz2.open('data.txt.bz2', 'rb') as f_in, open('data.txt', 'wb') as f_out:
    f_out.writelines(f_in)
```

As with all compressed files, this one is also unreadable in a simple text editor, and will look like this:

```plaintext
BZh91AY&SY�q��S�`0�`$���ץ��e�`vsV�nNn���N��U�f�,����Vgu[�];j��f����Ѿ���M�l��u�٢�wwF;i��ܶ�S�53*�S���S�LM*������POz��$� $�U
�;���n�����~
```

### Data compression vs. data compaction vs. data reduction

While related, compaction, compression and reduction are slightly different techniques:

```javascript trailing-newline
| **Category**     | **Compression**                                                                                   | **Compaction**                                                                                                    | **Reduction**                                                                                                                 |
| ---------------- | ------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| **Aim**          | Reduce data size by encoding it in a more space-efficient format, minimizing transfer or storage. | Minimize storage space while preserving data contents and structure.                                              | Reduce the overall amount of data while retaining its critical information.                                                   |
| **Techniques**   | Encoding data in a compressed form to represent it in fewer bits.                                 | Data compression, merging smaller data segments into larger ones, and optimizing data structures (e.g., indexes). | Aggregation, downsampling, encoding, filtering out less relevant data points, dimensionality reduction for feature selection. |
| **Key benefits** | Reduce data transfer times, save storage space, and improve transmission efficiency.              | Improve storage efficiency and reduce costs.                                                                      | Improve data manageability, simplify analytics, and speed up query performance.                                               |
```

Compression, compaction, and reduction are complimentary and often used together to achieve our data engineering objectives.

---

## Consolidate

> Combine multiple datasets into one to create a more comprehensive view of the data.

Source: https://dagster.io/glossary/data-consolidation

### Data consolidation definition:

Data consolidation is a process of combining multiple datasets into a single dataset to create a more comprehensive view of the data. When designing data pipelines, it enables us to work with large volumes of data from different sources—which may have different structures, formats, or quality—in a more efficient and effective manner.

In the context of modern data engineering, data consolidation typically involves the following steps:

1. Identify data sources: Data engineers need to identify the sources of data they want to consolidate, which can include internal and external data sources, such as databases, files, APIs, and streaming data.
2. Extract data: The data from the identified sources is extracted, either in batches or real-time, and stored in a data lake or staging area.
3. Transform data: Data from different sources may have different formats, structures, or quality. Data engineers need to transform the data to ensure consistency, quality, and compatibility. This step may involve data cleaning, normalization, deduplication, and enrichment.
4. Integrate data: Data from different sources is integrated into a single dataset by mapping data attributes, resolving data conflicts, and creating a unified schema.
5. Load data: The consolidated data is loaded into a data warehouse or data mart, where it can be queried, analyzed, and visualized.

Data consolidation is a key component of modern data engineering because it enables organizations to leverage the power of big data and analytics to gain insights, improve decision-making, and drive innovation. By consolidating data from multiple sources into a single dataset, data engineers can create a more comprehensive and accurate view of their business operations, customers, and markets, which can help them identify patterns, trends, and opportunities for growth.

### Example of Data Consolidation using Python

Let's create two sample datasets, `sales_data` and `marketing_data`, each with a common column, `Date` and `Region`, and different metrics, `Sales` and `Impressions`, respectively.

Next, we create two Pandas DataFrames from the sample data using the `pd.DataFrame()` method.

Finally, we consolidate the data from both dataframes into a single dataset by merging them on the common columns, `Date` and `Region`, using the `pd.merge()` method with an outer join. This creates a new dataframe `consolidated_df` that contains all the data from the `sales_df` and `marketing_df` dataframes.

Please note that you need to [have Pandas installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import pandas as pd

# create sample data
sales_data = {'Date': ['2022-01-01', '2022-01-02', '2022-01-03'],
              'Region': ['North', 'South', 'East'],
              'Sales': [1000, 2000, 1500]}

marketing_data = {'Date': ['2022-01-02', '2022-01-03', '2022-01-04'],
                  'Region': ['South', 'East', 'West'],
                  'Impressions': [500, 1000, 800]}

# create pandas dataframes from sample data
sales_df = pd.DataFrame(sales_data)
marketing_df = pd.DataFrame(marketing_data)

# consolidate data by merging dataframes
consolidated_df = pd.merge(sales_df, marketing_df, on=['Date', 'Region'], how='outer')

print(consolidated_df)
```

This data consolidation script will print out:

```plaintext
| Date       | Region |  Sales | Impressions |
| ---------- | ------ | -----  | ----------  |
| 2022-01-01 | North  | 1000.0 |           – |
| 2022-01-02 | South  | 2000.0 |       500.0 |
| 2022-01-03 | East   | 1500.0 |      1000.0 |
| 2022-01-04 | West   |      – |       800.0 |
```

In the context of the output from the example I provided, `NaN` stands for "Not a Number."

NaN is a special floating-point value that represents undefined or unrepresentable values, such as the result of an invalid mathematical operation or missing data.

In this specific case, the `NaN` values in the `Impressions` column of the consolidated dataframe represent missing data, which means that there were no marketing impressions data available for the North and West regions on January 1 and January 4, respectively.

---

## Cosine Similarity

> A measure of similarity between two entities used in text analysis, natural language processing, etc.

Source: https://dagster.io/glossary/cosine-similarity

### Cosine Similarity definition:

Cosine similarity is a metric used to measure how similar two entities (e.g., documents, vectors, data points) are irrespective of their size.

In the context of data engineering, it's primarily used in [text analysis](https://unstructured-data-analysis) and natural language processing, but also in other areas like recommendation systems. Cosine similarity is particularly useful because it is *independent of the magnitude of the vectors*. This makes it a good choice for cases where the magnitudes of the vectors are not relevant or are misleading. For example, in text analysis, a longer document might have higher term frequencies, but that doesn't necessarily mean it's more "similar" to another document compared to a shorter one.

### How cosine similarity works:

Here's a breakdown of how cosine similarity works:

**Vector Representation of Data**: The first step in applying cosine similarity is to represent the data as vectors. In text analysis, this could mean representing documents or sentences as vectors of term frequencies (TF) or TF-IDF (Term Frequency-Inverse Document Frequency) scores. Each dimension of the vector represents a unique term (word) from the corpus (collection of texts). In other contexts, the data points might be represented in different ways, but the key is that they need to be converted into vectors.

**Calculating the Cosine of the Angle**: Once you have two vectors, the cosine similarity is calculated by taking the dot product of these vectors and dividing by the product of their magnitudes (or lengths). This is equivalent to finding the cosine of the angle between the two vectors in a multi-dimensional space. The formula is as follows:

![](https://dagster.io/images/glossary/681399f654933b29e12fba80_cosine-similarity-min.png)

where `A` and `B` are vectors, `A.B` is the dot product, and `||A||` and `||B||` are the magnitudes (lengths) of the vectors `A` and `B`.

**Interpreting the Results**: The cosine similarity will always give a value between -1 and 1, where:   - 1 indicates that the two vectors are identical (they point in the same direction).   - 0 indicates that the two vectors are orthogonal (unrelated).   - -1 indicates that the two vectors are diametrically opposed.

Let's note that in the context of text represented as TF-IDF vectors, cosine similarity ranges from 0 to 1 and not -1 to 1, because TF-IDF vectors are non-negative.

### An example of cosine similarity in Python:

As an example of cosine similarity in Python, let's consider a scenario where we have multiple documents and we want to find how similar they are to a query. This kind of analysis is typical in information retrieval systems or recommendation engines.

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code, namely `sklearn`, `numpy`, and `nltk`.

Also, the code uses NLTK's *stopwords* and *tokenizer*, so you'll need to download the necessary NLTK data using `nltk.download()` as we do in the code below.

In this example, I'll demonstrate:

1. **Preprocessing of Text**: Including tokenization, removal of stopwords, and stemming/lemmatization.
2. **Vectorization of Text**: Using TF-IDF vectors.
3. **Cosine Similarity Calculation**: Between a query document and a set of other documents.
4. **Ranking Documents**: Based on their similarity to the query.

Here's the Python example:

```python
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize
import nltk

# Download required NLTK data
nltk.download('punkt')
nltk.download('stopwords')

# Define a basic preprocessor
def preprocess(document):
    # Tokenize
    tokens = word_tokenize(document.lower())
    # Remove stopwords and stem
    stemmer = PorterStemmer()
    return ' '.join([stemmer.stem(word) for word in tokens if word not in stopwords.words('english')])

# Sample "documents" and a query document
documents = [
    "Dagster helps data engineers tame complexity. Elevate your data pipelines with software-defined assets, first-class testing, and deep integration with the modern data stack.",
    "Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.",
    "Declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date.",
    "Dagster is an open-source, cloud-native data orchestration engine to build, deploy, and manage data pipelines. It is inspired heavily by Airflow and started as an open-source library for building ETL/ELT processes and ML pipelines."
]
query = ["Dagster is a cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability."]

# Preprocess the documents and the query
processed_docs = [preprocess(doc) for doc in documents]
processed_query = preprocess(query[0])

# Vectorize the text
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(processed_docs + [processed_query])

# Calculate Cosine Similarity
cosine_similarities = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1])

# Display the similarity scores
print("Cosine Similarity Scores:", cosine_similarities)

# Optionally, rank the documents based on similarity
ranking = np.argsort(cosine_similarities[0])[::-1]
print("Ranking of documents based on similarity to query:", ranking)
```

In this code:

- The `preprocess` function tokenizes the documents, removes stopwords, and applies stemming. This is a common preprocessing step in text analysis.
- `TfidfVectorizer` is used to convert the preprocessed text into TF-IDF vectors.
- Cosine similarity is calculated between the query and each of the documents.
- The documents are then ranked based on their similarity to the query.

Here is the output of the code above, indicating the second document in our array is the closest to the reference 'query' document:

```javascript
Cosine Similarity Scores: [[0.09165726 0.96072959 0.01847582 0.07214393]]
Ranking of documents based on similarity to query: [1 0 3 2]
```

---

## Crash Recovery

> The process by which an operating system or application restarts operation after a crash, possibly recovering lost data.

Source: https://dagster.io/glossary/crash-recovery

---

## CRON

> A time-based job scheduler in Unix-like computer operating systems for scheduling periodic jobs at fixed times, dates, or intervals.

Source: https://dagster.io/glossary/cron

---

## Curate

> Select, organize, and annotate data to make it more useful for analysis and modeling.

Source: https://dagster.io/glossary/data-curation

### Data curation definition:

Data curation refers to the process of organizing, maintaining, and preserving data to ensure its accuracy, consistency, and reliability. The goal of data curation is to maintain high-quality, trusted data throughout its lifecycle, from initial collection to long-term storage and retrieval. Many data curation practices are manual and require an understanding of the context in which the data was collected, or is being used.

### Data curation best practices:

By implementing best practices and using the right tools, you can ensure that your organization’s data is well-maintained and usable throughout your data processes. Some best practices for data curation in data pipelines include:

- **Establishing clear data governance policies**: This involves defining ownership, access rights, and security protocols for data. It helps to ensure that data is well-protected and only accessed by authorized personnel.
- **Documenting data lineage**: Documenting the origin and processing history of data helps to ensure its traceability and reproducibility. It makes it easier to understand the context and meaning of data and helps to avoid errors and inconsistencies.
- **Implementing version control**: Version control allows data engineers to keep track of changes made to data and helps to avoid accidental data loss or corruption. It ensures a reliable history of changes made to data over time.
- **Automating data validation**: Data validation involves checking data for accuracy, completeness, and consistency. Automating this process helps to ensure that data is constantly checked and validated before being used in downstream processes.
- **Using data profiling tools**: Data profiling tools help to identify patterns, anomalies, and errors in data. They make it easier to identify and correct data quality issues before they cause downstream problems.

### Python libraries for data curation

In Python, there are several libraries and tools that can be used for data curation, such as:

- [Great Expectations](https://greatexpectations.io/): A library for automated data validation. It allows data engineers to define expectations about the structure and content of data and validate them automatically.
- [DVC](https://dvc.org/): A version control system for data science and machine learning projects. It allows data scientists to track changes to data files and collaborate with team members.
- DataProfiler: A library for data profiling and analysis. It allows data engineers to understand the structure and quality of data and identify potential issues.

### An example of data curation in Python

Let's look at a basic example script that does the following:

1. Downloads a public dataset (We will use the well-known Iris dataset)
2. Loads the dataset into a DataFrame
3. Removes any duplicates or rows with missing data
4. Normalizes numeric data
5. Splits the data into a training set and a test set
6. Saves the cleaned, curated data to file

Here's the script.  Please note that you must [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run the code examples below.

```python
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

# Step 1: Download the dataset
iris = datasets.load_iris()
iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)

# Step 2: Load the dataset into a DataFrame
iris_df['target'] = iris.target

# Step 3: Remove any duplicates or rows with missing data
iris_df.drop_duplicates(inplace=True)
iris_df.dropna(inplace=True)

# Step 4: Normalize numeric data
scaler = MinMaxScaler()
iris_df[iris.feature_names] = scaler.fit_transform(iris_df[iris.feature_names])

# Step 5: Split the data into a training set and a test set
train_df, test_df = train_test_split(iris_df, test_size=0.2, random_state=42)

# Step 6: Save the cleaned, curated data to disk
train_df.to_csv('train.csv', index=False)
test_df.to_csv('test.csv', index=False)
```

The output to `train.csv` and `test.csv` will look like this:

```plaintext trailing-newline
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | target |
| ----------------- | ---------------- | ----------------- | ---------------- | ------ |
| 0.0833            | 0.6667           | 0.0000            | 0.0417           | 0      |
| 0.3889            | 1.0000           | 0.0847            | 0.1250           | 0      |
| 0.6667            | 0.4583           | 0.5763            | 0.5417           | 1      |
| 0.1389            | 0.5833           | 0.1017            | 0.0417           | 0      |
```

This script does a very basic job of data curation. A typical curation step would involve more sophisticated cleaning (like handling outliers, correcting mislabeled data, or dealing with unbalanced datasets) or data transformation (like feature engineering or encoding categorical variables). But the basic process will look similar: load the data, clean it, transform it, and save the curated data for later use.

---

## Cursor

> A database object used to traverse the results of a SQL query, allowing individual rows to be accessed.

Source: https://dagster.io/glossary/cursor

---

## DAG (Directed Acyclic Graph)

> A finite directed graph with no directed cycles, used extensively in representing data flow in data processing systems like Apache Airflow.

Source: https://dagster.io/glossary/dag-directed-acyclic-graph

---

## Data Degradation

> The gradual loss or deterioration of data quality over time.

Source: https://dagster.io/glossary/data-degradation

---

## Data Dictionary

> A collection of descriptions of the data objects or items in a data model for the benefit of programmers and others who need to refer to them.

Source: https://dagster.io/glossary/data-dictionary

---

## Data Provenance

> Information that helps to trace the origins, processing, and use of data, helping to determine the quality and reliability of the dataset.

Source: https://dagster.io/glossary/data-provenance

---

## De-identify

> Remove personally identifiable information (PII) from data to protect privacy and comply with regulations.

Source: https://dagster.io/glossary/data-deidentification

### De-identify data - a definition:

De-identifying data is the process of removing or obfuscating personally identifiable information from datasets, while still retaining their utility for analysis. One common method is to replace sensitive data with pseudonyms or anonymized values.

In Python, one way to de-identify data is to use regular expressions (“regex”) to find and replace sensitive information. For example, the `re` library can be used to search for patterns such as email addresses, phone numbers, or social security numbers, and replace them with generic terms like "EMAIL", "PHONE", or "SSN".

You can install this library with `pip install regex`.

### Example of de-identifying data in Python:

```python
import re

email_pattern = re.compile(r'\b[\w.-]+?@\w+?\.\w+?\b')
phone_pattern = re.compile(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b')

text = "John Smith's email is john.smith@example.com and his phone number is 555-123-4567"
print(text)

text = email_pattern.sub('EMAIL', text)
text = phone_pattern.sub('PHONE', text)

print(text)
```

This would output: "John Smith's email is EMAIL and his phone number is PHONE"

Another method to de-identify data is to use a hashing function to replace sensitive values with a unique hash value, which can be used to link records across datasets without revealing sensitive information. For example, the `hashlib` library which comes pre-built in Python can be used to generate a SHA-256 hash of a sensitive value:

```python
import hashlib

email = 'john.smith@dagster.com'
hashed_email = hashlib.sha256(email.encode()).hexdigest()

print(hashed_email)
```

This would output a unique hash value that can be used to link records with the same email address, without revealing the actual email: "ce909f6d89a6f29d7fc442d6fcacc166b89f87c1baeac9c24b14f986ae2c75ca".

#### De-identifying data in Pandas:

Let’s look at the de-identification process using the popular Pandas library:

1. **Identify sensitive data**: Before de-identification, it's important to identify what data needs to be protected. This can include personal identifiers such as names, addresses, and social security numbers.
2. **Anonymize the data**: The next step is to anonymize the data by removing or altering identifying information. Here are some Python functions that can be used for this:
3. `pandas.DataFrame.drop()` : This function can be used to drop columns that contain sensitive information.
4. `pandas.DataFrame.replace()` : This function can be used to replace sensitive information with non-identifying information.
5. `hashlib`: This module can be used to hash sensitive information such as email addresses or phone numbers.
6. **Validate the data**: After de-identification, it's important to validate the data to ensure that no identifying information remains. Here are some Python functions that can be used for this:
7. `pandas.DataFrame.duplicated()`: This function can be used to check for duplicate rows in the dataset.
8. `pandas.DataFrame.isnull()`: This function can be used to check for missing values in the dataset.
9. `pandas.DataFrame.any()`: This function can be used to check if any identifying information remains in the dataset.

Using Pandas functions such as the ones listed above can help ensure that sensitive information is properly removed or altered in datasets.

---

## Deduplicate

> Identify and remove duplicate records or entries to improve data quality.

Source: https://dagster.io/glossary/data-deduplication

### Data deduplication definition:

The presence of duplicates in your data can lead to incorrect analysis results and cause storage and processing inefficiencies, leading to increased costs.

There are several Python functions and techniques for de-duplicating data in regular programming, such as using hash functions, using set data structure, and comparing columns to identify duplicates. While traditional Python object lists and sets can be used for deduplicating data, they may not be the most efficient choice for large-scale data processing. This is because lists and sets require storing all data in memory, which can quickly become a bottleneck and slow down the processing.

For deduplicating large datasets, it's recommended to use specialized libraries and data structures designed for efficient processing, such as the pandas library in Python. Pandas has built-in functions like `drop_duplicates()` that can easily handle large datasets. Additionally, distributed processing frameworks like Apache Spark and Dask can be used to process large datasets in a distributed and parallelized manner, which can further improve the performance of data deduplication.

### Deduplicating data in Python

Please note that you must [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run the code examples below.

Python provides several libraries and functions for de-duplicating data, such as:

**Pandas:** Pandas provides a `drop_duplicates()` function that can be used to remove duplicate rows from a Pandas DataFrame.  Given an input file `data.csv` with 56 rows of which 10 are duplicates, the following code…

```python
import pandas as pd

df = pd.read_csv('data.csv')
print(f"This dataframe has {len(df)} rows.")
df = df.drop_duplicates()
print(f"This dataframe now has {len(df)} rows.")
```

… might yield this output:

```javascript
This dataframe has 56 rows.
This dataframe now has 44 rows.
```

**Dask:** Dask is a parallel computing library in Python that can be used for big data processing. Dask provides a `drop_duplicates()` function that can be used to remove duplicates from a Dask DataFrame.

In the following example, the first line imports the dask.dataframe module as dd.

The second line reads in CSV files using the dd.read\_csv() function, which returns a Dask DataFrame. The \* in the filename parameter data\*.csv is a wildcard character that matches any file with a name starting with "data" and ending with ".csv". If there are multiple files that match this pattern, Dask will concatenate them into a single dataframe.

```python
import dask.dataframe as dd

df = dd.read_csv('data*.csv')
df = df.drop_duplicates()
```

The resulting dataframe will be distributed across multiple workers, which can perform computations in parallel.

**PySpark:** PySpark is the Python API for Apache Spark, a big data processing framework. PySpark provides a `dropDuplicates()` function that can be used to remove duplicates from a PySpark DataFrame.

Given the input `data.csv` file:

```plaintext
| Name   | ZIP    | Amount |
| ------ | ------ | -----  |
| James  | 12345  |     99 |
| Bob    | 19876  |     23 |
| Claire | 212565 |    124 |
| Bob    | 19876  |     23 |
| Claire | 212565 |    123 |
| James  | 12345  |     99 |
```

We can use PySpark to deduplicate the data:

```python
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("De-duplication").getOrCreate()
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df = df.dropDuplicates()

df.show()
```

The `df.show()` command will provide us with the output:

```plaintext
+------+------+------+
|  name|   zip|amount|
+------+------+------+
| James| 12345|    99|
|   Bob| 19876|    23|
|Claire|212565|   124|
|Claire|212565|   123|
+------+------+------+
```

Note that the lines have to be identical across all columns to be considered a duplicate, which is why the last two lines remain in the dataframe.

---

## Denoise

> Remove noise or artifacts from data to improve its accuracy and quality.

Source: https://dagster.io/glossary/data-denoising

### Data denoising - a definition:

In the context of data pipelines, denoising refers to the process of removing noise from the data, which could be caused by various factors such as measurement errors, faulty sensors, or even human error during data entry. The goal is to extract useful information from the data and remove any unwanted signals that could interfere with the analysis.

### Data denoising in Python:

Here are some practical examples of denoising techniques in Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

**Moving Average Filter**: This is a simple denoising technique that involves taking the average of a sliding window of data points to smooth out the noise. It can be implemented using the rolling function in pandas or convolve function in numpy.

```python
import numpy as np

def moving_average_filter(data, window_size):
    window = np.ones(window_size)/float(window_size)
    convolved_data = np.convolve(data, window, 'same')
    print("Original data: {}".format(data))
    print("Moving average filtered data with window size {}: {}".format(window_size, convolved_data))
    return convolved_data

# example usage
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
window_size = 4
filtered_data = moving_average_filter(data, window_size)
```

This example will put yield the following output:

```plaintext
Original data: [ 1  2  3  4  5  6  7  8  9 10]
Moving average filtered data with window size 4: [0.75 1.5  2.5  3.5  4.5  5.5  6.5  7.5  8.5  6.75]
```

**Median Filter**: This technique involves replacing each data point with the median of its neighboring points. It can be implemented using the signal.medfilt function in scipy.

```python
from scipy import signal

def median_filter(data, window_size):
    filtered_data = signal.medfilt(data, kernel_size=window_size)
    print("Original data: {}".format(data))
    print("Median filtered data with window size {}: {}".format(window_size, filtered_data))
    return filtered_data

# example usage
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
window_size = 5
filtered_data = median_filter(data, window_size)
```

Which will output:

```plaintext
Original data: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Median filtered data with window size 5: [1 2 3 4 5 6 7 8 8 8]
```

Try changing the `window_size` value to 4 for an interesting result.

Since we are applying a median filter to an input signal using the `medfilt()` function from the `scipy.signal` module. The `medfilt()` function applies a median filter to the input signal using a sliding window with a specified size, and returns the filtered signal.

When the window size is even, such as when `window_size` = 4, the function raises an error because the `medfilt()` function requires an odd number for the window size. This is because the median filter operates by taking the median value of the data points within the window, and when the window size is even, the median value is not well-defined.

**Wavelet Transform**: This is a more advanced denoising technique that involves decomposing the signal into different frequency bands using wavelets and then thresholding the coefficients to remove the noise. It can be implemented using the pywt library (which may require installation using `pip install PyWavelets`.

```python
import numpy as np
import pywt

def wavelet_denoising(data, wavelet='db4', level=1):
    coeffs = pywt.wavedec(data, wavelet, level=level)
    threshold = np.std(coeffs[-level])
    coeffs = [pywt.threshold(c, threshold) for c in coeffs]
    denoised_data = pywt.waverec(coeffs, wavelet)
    print("Original data: {}".format(data))
    print("Denoised data using wavelet {}: {}".format(wavelet, denoised_data))
    return denoised_data

# example usage
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
wavelet = 'db4'
level = 1
denoised_data = wavelet_denoising(data, wavelet, level)
```

Which will yield the following output:

```plaintext
Original data: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Denoised data using wavelet db4: [0.95707075 2.00990332 2.94189799 3.95561577 5.00005885 5.96165328
 6.97197144 7.97478225 8.97266171 9.96551716]
```

These are just a few examples of denoising techniques that can be used in data pipelines. The choice of technique depends on the nature of the noise and the requirements of the analysis.

---

## Denormalize

> Optimize data for faster read access by reducing the number of joins needed to retrieve related data.

Source: https://dagster.io/glossary/data-denormalization

### Data denormalization definition:

Denormalization is the process of adding redundant data to a database to improve [read performance](https://www.johnnunemaker.com/database-performance-simplified/). It involves breaking with normalization rules in order to reduce the number of joins needed to retrieve data. This can be important for improving query performance in data pipelines that have to handle large amounts of data.

Denormalization can be a useful technique for improving query performance in your data pipelines, but it should be used judiciously and with an understanding of the trade-offs involved. Denormalizing data can lead to increased data redundancy and the risk of data inconsistencies.

### Data denormalizing in Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run the code examples below.

In Python, one way to denormalize data is to use the `pandas.merge()` function to combine data from multiple tables into a single DataFrame. For example, if you have a table of customers and a table of orders, you could denormalize the data by merging the two tables based on the customer ID column:

Given input files of customer.csv as:

```plaintext
customer_id,name,total_orders
1,Mary,42
2,Brian,28
3,Mercedes,7
4,Rose, 20
```

And an file of:

You can merge this data as follows:

```python
import pandas as pd

# Load the customer and order tables into DataFrames
customers = pd.read_csv('customers.csv')
orders = pd.read_csv('orders.csv')

# Merge the two tables based on the customer ID column
merged_data = pd.merge(customers, orders, on='customer_id')

print(merged_data)
```

This would create a new DataFrame that includes all of the columns from both the customers and orders tables, with the data merged together based on the `customer_id` column and would look like this:

```plaintext
| Customer ID | Name     | Total Orders | Item     | Value |
| ----------- | -------- | -----------  | -------- | ----  |
| 1           | Mary     |           42 | cat food |    12 |
| 1           | Mary     |           42 | apples   |     6 |
| 1           | Mary     |           42 | books    |    24 |
| 2           | Brian    |           28 | bananas  |    12 |
| 2           | Brian    |           28 | candles  |    12 |
| 2           | Brian    |           28 | coffee   |     4 |
| 3           | Mercedes |            7 | nails    |     7 |
| 4           | Rose     |           20 | paper    |    18 |
| 4           | Rose     |           20 | pencils  |     2 |
```

Another way to denormalize data in Python is to use a document-oriented database like [MongoDB](https://dev.to/damcosset/mongodb-normalization-vs-denormalization), which allows you to store data in a denormalized format as documents. This can be useful for handling complex, nested data structures that don't fit well into traditional relational database tables. With MongoDB, you can store related data together in a single document, rather than splitting it up into multiple tables with foreign key relationships.

Suppose we have two collections in our MongoDB database: "users" and "orders". The "users" collection contains documents with information about each user, such as their name and email address. The "orders" collection contains documents with information about each order, including the user ID of the user who placed the order.

You can install the free MongoDB community edition locally on \[Mac]\(<https://www.mongodb.com/docs/manual/tutorial/install-mongodb-on-os-x/>) | \[Linux]\(<https://www.mongodb.com/docs/manual/administration/install-on-linux/>) | \[Windows]\(<https://www.mongodb.com/docs/manual/tutorial/install-mongodb-on-windows/>)

Here's how we can denormalize this data by embedding user information within each order document:

```python
import pymongo

# Connect to MongoDB
client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client["mydatabase"]

# Get collections
users_col = db["users"]
orders_col = db["orders"]

# Find all orders
orders = orders_col.find()

# Denormalize data
for order in orders:
    user = users_col.find_one({"_id": order["user_id"]})
    order["user_name"] = user["name"]
    order["user_email"] = user["email"]

    # Update order document with denormalized user data
    orders_col.update_one(
        {"_id": order["_id"]},
        {"$set": {
            "user_name": order["user_name"],
            "user_email": order["user_email"]
        }}
    )
```

In this example, we first connect to our MongoDB database and get references to the "users" and "orders" collections. We then find all orders in the `orders` collection and denormalize each order document by embedding the user's name and email within the order document. Finally, we update the `orders` collection with the denormalized data.

Denormalizing data in this way can be useful for optimizing read performance, as it reduces the need to perform multiple queries to retrieve related data. However, it can also increase write complexity and potentially lead to data inconsistency if the denormalized data is not kept up-to-date with changes to the original data. Therefore, it's important to carefully consider the trade-offs before denormalizing data.

---

## Derive

> Extracting, transforming, and generating new data from existing datasets.

Source: https://dagster.io/glossary/data-derivation

### Deriving data definition:

In data engineering, "deriving data" refers to the process of extracting, transforming, and generating new data from existing datasets.

This derived data is often used to enhance understanding, improve data quality, support decision-making, or meet specific business needs. The derived data is usually a result of applying some transformation, computation, or aggregation on the source data.

Overall, deriving data is a fundamental aspect of data engineering, enabling organizations to extract greater value from their data assets.

### Key elements of data derivation:

1. **Source Data**: This is the original dataset you start with. It could be [ingested](https://dagster.io/glossary/data-ingestion) from databases, data lakes, external APIs, or other sources.
2. **Transformation**: This involves applying operations to the source data to generate new information. Transformations can be simple arithmetic operations, aggregations, filtering, joining multiple datasets, or even applying complex algorithms.
3. **Derived Data**: The [materialized](https://dagster.io/glossary/data-materialization) result of the transformation. This is the new data that was not present in its original form in the source data but has been extracted or computed based on it.

### Example use cases of deriving data:

- [**Aggregations**](https://dagster.io/glossary/data-aggregation): Summing up sales figures by month to generate a monthly sales report.
- [**Feature Engineering in Machine Learning**](https://dagster.io/glossary/feature-extraction): Creating new predictive features from existing dataset columns. For example, from a dataset containing "date of birth", you can derive an "age" column.
- [**Data Enrichment**](https://dagster.io/glossary/data-enrichment): Combining data from different sources. If you have a list of users with their email addresses and another list with email addresses and purchase history, you could derive a combined dataset showing user activity.
- [**Data Normalization**](https://dagster.io/glossary/data-normalization): Transforming data to fit a standard scale or format. For instance, converting temperature readings from Fahrenheit to Celsius.
- [**Data Imputation**](https://dagster.io/glossary/data-imputation): Filling missing values based on certain rules or algorithms derived from the available data.
- **Temporal Derivations**: Generating data like "Month to Date" or "Year to Date" metrics.

It's important to note the following when working with derived data:

- [**Data Lineage**](https://dagster.io/glossary/data-lineage): It's essential to track where the derived data comes from and understand the transformations applied. This helps in ensuring transparency, reproducibility, and trust in the derived data.
- **Quality Checks**: Derived data should undergo quality checks to ensure its accuracy and relevance. Incorrect transformations can lead to misleading conclusions.
- **Storage and Management**: Depending on the size and usage, derived data might be stored differently from the source data. Considerations about storage, retrieval efficiency, and cost are important.

### An example scenario of data derivation in data engineering:

To illustrate the concept of data derivation, let's consider a scenario: Real-time Stream Processing for Anomaly Detection.

Say you work for a fintech company that processes millions of transactional events every minute through your payment gateway. You aim to derive new critical data assets in real-time to identify anomalous transaction patterns, which might indicate fraudulent activities.  These data assets will be used by downstream teams (data consumers) to run anti-fraoud processes.

Going back to our three elements of data derivation:

**1. Source Data**: Real-time streaming data of transactions, ingested through tools like Apache Kafka. Each transaction record consists of attributes like `user_id`, `transaction_id`, `transaction_amount`, `location`, `merchant_id`, and `timestamp`.

**2. Transformation & Derivation**:

You would use several techniques for transofrming the data and deriving your new key data asset:

- **Windowing**: Using Apache Flink or Apache Spark Streaming, the data stream is divided into time-based windows, e.g., tumbling windows of 5 minutes.
- **Feature Engineering**: For each window, derive new features such as:
- Average transaction amount for the user in the last 24 hours.
- Number of distinct locations from which transactions originated in the last 30 minutes.
- Count of transactions for the user in the last 5 minutes.
- **Joining with Reference Data**: Join the real-time data stream with static datasets (residing in a distributed file system like HDFS or a database) that might include:
- User's historical transaction behavior.
- Merchant risk scores.
- **Anomaly Scoring**: Use a pre-trained machine learning model (like an Isolation Forest or One-Class SVM) to score each transaction for its likelihood of being anomalous. This model would have been previously trained on historical data and continuously retrained at regular intervals.

**3. Derived Data**:

- For each transaction in the current time window, a derived record is produced that includes the original attributes, the newly derived features, and an anomaly score.
- Aggregated metrics like the total number of high-risk scores in the current window, average risk score per merchant, etc. are created.

**4. Sink & Action**:

- You store derived insights in a real-time analytics database like [Druid](https://druid.apache.org/) or [ClickHouse](https://clickhouse.com/) for dashboarding and alerting.
- You use the anomaly score to trigger alerts or even block transactions in real-time, using thresholds or business rules.

**Considerations**:

For this scenario to work, you would want to invest time in several aspects of the project:

- **State Management**: You need to maintain the state across windows for continuity, especially for continuous queries that rely on historical data.
- **Scalability**: As data rates fluctuate, the processing system needs to scale. Running Dagster on Kubernetes can be used for orchestrating the scalability of such streaming applications.
- **Model Management**: Machine Learning models need frequent re-training. Ensuring the ML model used for anomaly scoring remains up-to-date and relevant is crucial. This involves model versioning, continuous training, and strategies for model deployment without interrupting the streaming process, all of which can be orchestrated via Dagster data pipelines.

This example embodies advanced data engineering concepts, blending real-time processing, machine learning, and scalable architectures to derive valuable insights from raw transactional data.

### An example of deriving data using Python and Spark

Let's consider a scenario involving Apache Spark, one of the most widely-used distributed data processing frameworks. In this example, we'll derive features from user transaction data for further analytics or machine learning.

Again, let's look at processing a large dataset of user transactions to derive user behavior metrics over a time window.  We will not replicate the entire scenario listed above, but just demonstrate the derivation concept.

First, let's create two dummy source data files.  Naturally, in a real-world scenario, data sources would likely be more diverse (e.g., databases, data lakes, streaming sources), and configurations would be more intricate.

Create the following two files locally (in the same folder as your demo code):

- `transactions.csv` contains columns: user\_id, transaction\_id, transaction\_amount, timestamp

```plaintext
| User ID | Transaction ID | Transaction Amount | Timestamp           |
| ------- | -------------- | -----------------  | ------------------- |
| 1       | 1001           |              50.25 | 2023-09-13 10:00:00 |
| 1       | 1002           |              75.00 | 2023-09-12 14:15:10 |
| 2       | 1003           |             100.00 | 2023-09-15 12:30:45 |
| 2       | 1004           |              25.50 | 2023-09-14 09:05:00 |
| 3       | 1005           |             110.75 | 2023-09-11 16:20:20 |
| 1       | 1006           |              45.00 | 2023-09-17 15:30:00 |
| 3       | 1007           |              95.25 | 2023-09-18 11:00:00 |
```

- `user_profiles.csv` contains columns: user\_id, signup\_date, user\_type

```plaintext
| User ID | Signup Date | User Type          |
| ------- | ----------- | ------------------ |
| 1       | 2023-01-01  | Dagster Team       |
| 2       | 2023-06-15  | Dagster Enterprise |
| 3       | 2022-11-20  | Dagster Team       |
```

Using these as our source, we will derive the following data

- Total transaction amounts for each user in the last 7 days.
- Total transactions count for each user in the last 7 days.
- Days since user signup till today.

Note: You need to [have the necessary Python libraries installed (a Spark environment, and PySpark) in your Python environment](https://dagster.io/glossary) to run this code.

```python
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from datetime import datetime, timedelta

# Configure Spark application
conf = SparkConf().setAppName("MySparkApp").setMaster("local[*]")  # Setting master to "local[*]" for local execution

# Initialize SparkContext with the configuration and set the log level
sc = SparkContext(conf=conf)
sc.setLogLevel("WARN")

# Initialize SparkSession
spark = SparkSession.builder \
    .appName("DeriveDataExample") \
    .getOrCreate()

# Load data
transactions = spark.read.csv('transactions.csv', header=True, inferSchema=True)
user_profiles = spark.read.csv('user_profiles.csv', header=True, inferSchema=True)

# Calculate the timestamp for 7 days ago
seven_days_ago = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d %H:%M:%S')

# Filter transactions in the last 7 days
recent_transactions = transactions.filter(transactions.timestamp > seven_days_ago)

# Derive total transaction amounts and counts for each user in the last 7 days
derived_metrics = recent_transactions.groupBy('user_id').agg(
    F.sum('transaction_amount').alias('total_amount_7d'),
    F.count('transaction_id').alias('transaction_count_7d')
)

# Derive days since user signup
user_derived = user_profiles.withColumn('days_since_signup',
                                        F.datediff(F.current_date(), F.col('signup_date')))

# Join the derived metrics with user profiles
final_derived_data = user_derived.join(derived_metrics, on='user_id', how='left').fillna(0)

# Display or save the derived data
final_derived_data.show()

# Optionally, save to a destination
# final_derived_data.write.csv('output_derived_data.csv', header=True)

spark.stop()
# Stop the SparkContext when done
sc.stop()
```

The output of the example will be the derived data as follows:

```plaintext
| User ID | Signup Date | User Type          | Days Since Signup | Total Amount (7d) | Transaction Count (7d) |
| ------- | ----------- | ------------------ | ----------------- | ----------------- | ---------------------- |
| 1       | 2023-01-01  | Dagster Team       | 262               | 45.00             | 1                      |
| 2       | 2023-06-15  | Dagster Enterprise | 97                | 125.50            | 2                      |
| 3       | 2022-11-20  | Dagster Team       | 304               | 95.25             | 1                      |
```

Using Apache Spark and Python (PySpark), this example of data derivation demonstrates:

- Distributed data processing with Apache Spark.
- Aggregation methods on large datasets.
- Data transformations and enrichments (joining, date calculations).
- Scalability: This code can handle very large datasets across clusters.

---

## Deserialize

> Deserialization is essentially the reverse process of serialization. See: 'Serialize'.

Source: https://dagster.io/glossary/data-deserialization

### Definition of Deserialization:

In the context of data management, serialization and deserialization are key to storing data persistently (like writing data to disk) and communicating data between different systems (for example, through APIs). It allows the structured, complex data of one system to be understood by another, irrespective of the language or architecture they're built with.

Deserialization is essentially the reverse process of serialization. It is the process of converting the serialized format back into a usable object in the program. This process is used to extract the data or the state of the object from the stored or received serialized format.

[      Jump to the entry for 'Serialize' for more details and examples.    ](https://dagster.io/glossary/data-serialization)

**Note: Deserialization can present potential security risks**, especially when dealing with unknown sources. Deserializing data from an untrusted source can lead to what's known as **deserialization attacks**, where malicious data is loaded into an object, potentially leading to code execution or privilege escalation. Therefore, it's important to ensure that any serialized data is appropriately secured and validated.

---

## Dimensionality

> Analyzing the number of features or attributes in the data to improve performance.

Source: https://dagster.io/glossary/data-dimensionality

### Data dimensionality definition:

Dimensionality in data engineering pipelines refers to the number of features or attributes that are present in the data as it moves through the pipeline. In data engineering, it is important to consider dimensionality as a key factor in designing and building data pipelines because high-dimensional data can have significant impacts on pipeline performance, storage requirements, and processing times.

For example, if you are working with a large dataset that contains thousands or millions of rows and hundreds or thousands of columns, it can be challenging to move this data efficiently through a pipeline. Large datasets with high dimensionality can also require more storage space and processing power, which can increase the cost and time required to manage the data.

To address the challenge of high-dimensional data in data engineering pipelines, it is common to use techniques such as feature selection, feature extraction, and dimensionality reduction. These techniques can reduce the number of features in the data, making it more manageable and easier to process.

### Data dimensionality in Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run the code examples below.

Here is a very basic example Python code for evaluating dimensionality (in this case just number of rows and columns) in a dataset using the pandas library, assuming an input file `dataset.csv`:

```python
import pandas as pd

# Load dataset into a pandas DataFrame
df = pd.read_csv('dataset.csv')

# Get the number of rows and columns in the dataset
num_rows = df.shape[0]
num_cols = df.shape[1]

# Print the dimensionality of the dataset
print('Number of rows:', num_rows)
print('Number of columns:', num_cols)
```

Depending on your input file, this would output something like:

```plaintext
Number of rows: 16
Number of columns: 3
```

In this very basic example, we first load the dataset into a pandas DataFrame using the `read_csv()` function. Then, we use the shape attribute of the DataFrame to get the number of rows and columns in the dataset. Finally, we print the dimensionality of the dataset by outputting the number of rows and columns using the `print()` function.

This code can be used to quickly evaluate the dimensionality of a dataset and gain insight into its structure. If you need to perform further analysis on the data, you may want to use additional techniques such as visualization, statistical analysis, or machine learning algorithms to better understand the relationships between the features in the dataset.

#### A more advanced example of analyzing dimensionality of data using Python and sklearn:

Let’s look at a more advanced example using `sklearn`.

By exploring multiple dimensions of the data using techniques like PCA and data visualization, we can gain a more complete understanding of the data and make better decisions about how to preprocess and analyze it.

```python
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA

# Load the iris dataset
iris = load_iris()
X = iris.data

# Perform Principal Component Analysis (PCA) to reduce the dimensionality of the dataset
pca = PCA(n_components=4)
X_pca = pca.fit_transform(X)

# Print the variance explained by each principal component
print('Explained variance:', pca.explained_variance_ratio_)

# Print the reduced dimensionality of the dataset
print('Number of rows:', X_pca.shape[0])
print('Number of columns:', X_pca.shape[1])

# Plot the first two principal components against each other
import matplotlib.pyplot as plt
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=iris.target)
plt.title("My Dimensionality Example")
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.show()
```

In this example, we still use the samel  “iris dataset” from scikit-learn (literally a dataset of the characteristics of Iris flowers) and perform PCA to reduce the dimensionality of the dataset. We set `n_components` to 4, which means we're keeping all four dimensions of the original data.

After computing the principal components, we print the explained variance ratio of each principal component to see how much of the variance in the original dataset is captured by each component. This gives us an idea of which dimensions are most important for describing the data.

Next, we plot the first two principal components against each other using matplotlib. This allows us to visualize the data in a two-dimensional space and see how well the different classes of flowers are separated from each other. This can help us gain a better understanding of the underlying structure of the data and identify any patterns or relationships that exist.

You will see the following output in the terminal:

```plaintext
Explained variance: [0.92461872 0.05306648 0.01710261 0.00521218]
Number of rows: 150
Number of columns: 4
```

![](https://dagster.io/images/glossary/681399f654933b29e12fbcc5_glossary-5.png)

---

## Discretize

> Transform continuous data into discrete categories or bins to simplify analysis.

Source: https://dagster.io/glossary/data-discretization

### Data discretization definition:

Discretization is the process of converting continuous data into a set of discrete intervals or categories. This technique can be used for data reduction, simplification, or to make the data more suitable for analysis and it typically applied to very large datasets.

### Data discretization examples using Python

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run the code samples below.

#### using pandas `cut()`

One practical example of discretization in Python is using the pandas `cut()` function. The `cut()` function allows you to specify the number of bins you want to use and the range of the data, and it returns a new column with the values binned into those intervals. For example:

```python
import pandas as pd

# create a sample dataset
data = pd.DataFrame({'values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})

# bin the values into three intervals
data['bins'] = pd.cut(data['values'], bins=3, labels=['Low', 'Medium', 'High'])

# view the binned data
print(data)
```

This will create a new column called 'bins' with the values binned into three intervals, 'Low', 'Medium', and 'High'. The output will be:

```plaintext
| Values | Bins   |
| ------ | ------ |
|      1 | Low    |
|      2 | Low    |
|      3 | Medium |
|      4 | Medium |
|      5 | Medium |
|      6 | High   |
|      7 | High   |
|      8 | High   |
|      9 | High   |
|     10 | High   |
```

Another example of using using the pandas `cut()` function, if we have a DataFrame `df` containing a column `ages` , we can create bins and labels as follows:

```python
import pandas as pd

# create a sample dataset
data = {'ages': [21, 32, 45, 18, 52, 28, 38, 50]}
df = pd.DataFrame(data)

bins = [0, 18, 35, 50, float('inf')]
labels = ['child', 'young adult', 'middle-aged', 'senior']

df['age_group'] = pd.cut(df['ages'], bins=bins, labels=labels)

print(df)
```

This will create a new column `age_group` in `df` containing the categorical labels based on the binning and print out the result:

```plaintext
| Age | Age Group   |
| --- | ----------- |
|  21 | young adult |
|  32 | young adult |
|  45 | middle-aged |
|  18 | child       |
|  52 | senior      |
|  28 | young adult |
|  38 | middle-aged |
|  50 | middle-aged |
```

#### Using scikit-learn

Another example of discretization in Python is using the KBinsDiscretizer class from the scikit-learn library. This class allows you to specify the number of bins, the strategy for dividing the data, and whether to encode the intervals as integers or one-hot vectors. For example:

```python
from sklearn.preprocessing import KBinsDiscretizer

# create a sample dataset
data = [[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]]

# bin the values into three intervals
discretizer = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
data_bins = discretizer.fit_transform(data)

# view the binned data
print(data_bins)
```

This will create a new array with the values binned into three intervals, and will yield the following:

```plaintext
[[0.]
 [0.]
 [1.]
 [1.]
 [1.]
 [2.]
 [2.]
 [2.]
 [2.]
 [2.]]
```

In this case, the `encode` parameter is set to `ordinal`, which means that the intervals are represented as integers. If it were set to `onehot`, the intervals would be represented as one-hot vectors.

---

## Downsample

> Reduce the amount of data for analysis, storage, or processing.

Source: https://dagster.io/glossary/data-downsampling

### Definition of downsampling:

In data engineering, "downsampling" refers to the process of reducing the amount of data for analysis, storage, or processing. This is done by systematically selecting a subset of the data at a lower rate than the original.

The main goal of downsampling is to reduce the computational requirements, storage needs, and potentially noise in your data. For instance, in time series data, it is common to downsample to different granularities (daily, hourly, etc.) to make the data easier to work with and to align it more closely with the problem you're trying to solve.

In image or audio file processing, downsampling can be used to reduce the resolution of an image or sound file.

Downsampling can be performed in various ways, such as:

1. **Average Pooling**: This method averages a group of data points to create a single representative data point. For example, you might reduce the granularity of time-series data from seconds to minutes by averaging all the data points within each minute.
2. **Decimation**: This method discards some data points without any replacement. For instance, you might keep every nth data point and discard the rest.
3. **Reservoir Sampling**: This is a randomized algorithm that allows you to select a sample of n items from a dataset of an unknown size, m, where m > n. The algorithm guarantees that every possible subset of m items has an equal chance of being the sampled subset.

Downsampling has to be done carefully. If not handled properly,

### Example of downsampling in Python

Here's an example of downsampling a time series data set using the Pandas library in Python.

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import pandas as pd
import numpy as np

# Create a date range
date_rng = pd.date_range(start='1/1/2023', end='12/31/2023', freq='H')

# Create a DataFrame with the date_rng as the index and random data
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = np.random.randint(0,500,size=(len(date_rng)))
df.set_index('date', inplace=True)

# Print original DataFrame
print("Original DataFrame:")
print(df)

# Downsampling: reduce datetime rows to daily
df_daily = df.resample('D').mean()

# Print downsampled DataFrame
print("\nDownsampled DataFrame:")
print(df_daily)
```

In this script, we first generate a date range from January 1, 2020 to January 10, 2020 with an hourly frequency. This serves as the index of our DataFrame. The data column of the DataFrame is populated with random integers between 0 and 100. The DataFrame is then resampled (downsampled) to a daily frequency using the resample method, and the mean of the hourly data for each day is computed. Both the original and downsampled DataFrames are then printed.

The `resample` function is a flexible and high-performance method in pandas for frequency conversion and resampling of time-series data. It allows you to convert the data into different frequencies. The 'D' argument makes it daily, 'H' makes it hourly, 'M' is for monthly, and so on.

Your output will look something like this:

```plaintext
Original DataFrame:
                     data
date
2023-01-01 00:00:00   402
2023-01-01 01:00:00   191
2023-01-01 02:00:00   432
2023-01-01 03:00:00   288
2023-01-01 04:00:00   274
...                   ...
2023-12-30 20:00:00   329
2023-12-30 21:00:00   277
2023-12-30 22:00:00   469
2023-12-30 23:00:00   289
2023-12-31 00:00:00   193

[8737 rows x 1 columns]

Downsampled DataFrame:
                  data
date
2023-01-01  253.083333
2023-01-02  226.041667
2023-01-03  259.208333
2023-01-04  225.208333
2023-01-05  296.166667
...                ...
2023-12-27  244.666667
2023-12-28  261.625000
2023-12-29  248.708333
2023-12-30  250.375000
2023-12-31  193.000000

[365 rows x 1 columns]
```

So we downsampled a dataset of 8,737 rows to a smaller set of just 365 rows.

You can replace `.mean()` with any other function (`sum()`, `min()`, `max()`, `median()`, etc.) depending on what kind of downsampling you need.

---

## Drift Detection

> Identifying when the statistical properties of the target variable, which the model is trying to predict, change.

Source: https://dagster.io/glossary/drift-detection

---

## Elasticsearch

> A search engine based on the Lucene library, providing a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

Source: https://dagster.io/glossary/elasticsearch

---

## Encapsulate

> The bundling of data with the methods that operate on that data.

Source: https://dagster.io/glossary/data-encapsulation

### Definition of Data Encapsulation:

**Data encapsulation** is the practice of bundling data with the methods that operate on that data, while restricting direct access to some of an object's components.

### Why Data Encapsulation is a fundamental concept in data engineering:

This fundamental concept is crucial in data engineering for several reasons:

**Modularity**: Encapsulation promotes the design of modular systems. By keeping data and its associated operations together, systems become more organized, making them easier to understand, develop, and maintain.

**Information Hiding**: One of the key aspects of encapsulation is information hiding. This means that the internal state of an object is hidden from the outside. Only the object's own methods can access and modify this state, which protects the integrity of the data and prevents external entities from causing inconsistencies or errors.

**Abstraction**: Encapsulation allows for a high level of abstraction. Users or other parts of the system interact with an object through a well-defined interface (methods or functions), without needing to understand the complexities of its internal workings.

**Reusability and Maintenance**: Encapsulated code is often more reusable and easier to maintain. Since changes to the internal workings of an object do not affect other parts of the system, updates and bug fixes are more straightforward.

**Security**: In data engineering, security is paramount, and encapsulation can play a role in securing data. By controlling how data is accessed and modified, and who has the authority to do so, encapsulation helps in maintaining data integrity and security.

### Uses of data encapsulation:

In modern data engineering, encapsulation is used in various forms, such as:

- **Object-Oriented Programming (OOP)**: This is the most direct implementation of encapsulation. Classes in OOP languages like Python, Java, or C++ encapsulate data and methods.
- **Data APIs**: Encapsulation is also seen in how data is accessed and manipulated through APIs. APIs provide a controlled interface to data sources, ensuring that data is accessed in a structured and secure manner.
- **Data Storage and Management**: Encapsulation is crucial in data management systems, like databases, where the internal structure of the database is hidden. Users interact with the data through a set of predefined queries and operations, without needing to know how the data is stored or maintained internally.
- **Microservices Architecture**: In a microservices architecture, each microservice encapsulates a specific functionality or data set. This encapsulation ensures that services are loosely coupled and can be developed and scaled independently.

### Example of data encapsulation in Python

Let's design a Python class that simulates a more complex data processing pipeline. This pipeline will include data ingestion, cleaning, transformation, and storage. The example will showcase *encapsulation*, *abstraction*, and *modular design*.

We will create a `DataPipeline` class that encapsulates all the steps in a data processing workflow:

1. **Data Ingestion**: Load data from a source (in this case a .csv file).
2. **Data Cleaning**: Clean the data to ensure quality.
3. **Data Transformation**: Transform the data into a format suitable for analysis, including some values derived from the source data.
4. **Data Storage**: Store the processed data in a .csv file system.

Here's an implementation:

```python
import pandas as pd

class DataPipeline:
    def __init__(self, source_config):
        self.__source_config = source_config
        self.__data = None

    def __load_data(self):
        # Private method to load data based on the source configuration
        if self.__source_config['type'] == 'csv':
            self.__data = pd.read_csv(self.__source_config['path'])

    def __clean_data(self):
        # Convert 'Date' from string to datetime
        self.__data['Date'] = pd.to_datetime(self.__data['Date'])

        # Handling missing values - Example: fill with mean or drop
        self.__data.fillna(self.__data.mean(numeric_only=True), inplace=True)

        # Handling outliers - Example: using a simple Z-score method for demonstration
        # This is a basic approach and can be replaced with more sophisticated methods as needed.
        for col in ['Temperature', 'Humidity', 'WindSpeed']:
            z_scores = (self.__data[col] - self.__data[col].mean()) / self.__data[col].std()
            self.__data = self.__data[abs(z_scores) < 3]

        # Ensuring correct data types and number of decimals
        self.__data['Temperature'] = self.__data['Temperature'].astype(float).round(4)
        self.__data['Humidity'] = self.__data['Humidity'].astype(float).round(2)
        self.__data['WindSpeed'] = self.__data['WindSpeed'].astype(int)

    def __transform_data(self):
        # Example: Aggregate data by 'Date' if it's a time series with multiple entries per date.
        # This example assumes daily aggregation, taking the mean of the values.
        self.__data = self.__data.groupby('Date').agg({
            'Temperature': 'mean',
            'Humidity': 'mean',
            'WindSpeed': 'mean'
        }).reset_index()

        # Creating new features - Example: 'FeelsLike' temperature using a simple formula
        # This is a hypothetical formula just for demonstration.
        self.__data['FeelsLike'] = self.__data['Temperature'] - 0.1 * self.__data['WindSpeed']

        # Time Series Features - Adding day of the week, month as new columns
        self.__data['DayOfWeek'] = self.__data['Date'].dt.day_name()
        self.__data['Month'] = self.__data['Date'].dt.month

    def __store_data(self, target_config):
        # Private method to store data
        if target_config['type'] == 'csv':
            self.__data.to_csv(target_config['path'])

    def execute_pipeline(self, target_config):
        # Public method to execute the entire pipeline
        self.__load_data()                  # Step 1: Load the data
        self.__clean_data()                 # Step 2: Clean the data
        self.__transform_data()             # Step 3: Transform the data
        self.__store_data(target_config)    # Step 4: Store the data

# Example usage
source_config = {'type': 'csv', 'path': 'https://dagster.io/glossary/data-encapsulation.csv'}
target_config = {'type': 'csv', 'path': 'data-encapsulation-out.csv'}

pipeline = DataPipeline(source_config)
pipeline.execute_pipeline(target_config)
```

In this example:

- **Modularity**: Each step of the data processing workflow is encapsulated into a private method. This modular approach makes the code more maintainable and scalable.
- **Information Hiding and Abstraction**: The internal implementation of data loading, cleaning, transforming, and storing is hidden from the user. The user interacts with the `DataPipeline` class through the `execute_pipeline` method.
- **Flexibility and Scalability**: The design allows for easy extension to support additional data sources and storage options. You can add more complex logic to each method to handle a wide range of data processing tasks.

This example provides a foundational structure that can be expanded and customized for specific needs in large-scale data engineering projects. The encapsulation of data and methods within the `DataPipeline` class facilitates easy maintenance and scalability, key considerations in complex data engineering environments.

---

## Encode

> Convert categorical variables into numerical representations for ML algorithms.

Source: https://dagster.io/glossary/data-encoding

### Data encoding definition:

**Data Encoding** refers to the converting of categorical variables into numerical representations that can be understood by machine learning algorithms. Common techniques include one-hot encoding, label encoding, or ordinal encoding.

Machine learning algorithms, especially traditional ones, are mathematical models that work on the basis of numeric computations and statistical operations. They require data to be **represented in a numerical format** to be able to establish any meaningful patterns or relationships within the data.

Categorical data, which might include labels like 'red', 'green', 'blue', can't be directly processed by these algorithms. To overcome this, categorical data is often encoded into a numerical form.

There are different encoding methods like one-hot encoding, label encoding, ordinal encoding, etc., each with its own pros and cons. The encoding technique to use depends on the categorical variable properties (e.g., nominal or ordinal) and the specific machine learning algorithm being used.

Furthermore, some machine learning models can handle categorical variables implicitly, such as decision trees and random forests. However, even in those cases, depending on the situation and the specific data set, it might be more efficient or yield better performance to manually encode categorical variables.

Lastly, categorical data often leads to high-dimensionality if not appropriately encoded. This is because a categorical variable with 'n' unique values might need to be transformed into 'n' binary variables, leading to an increase in the dimensionality of the data, a phenomenon known as "the curse of dimensionality", which can make learning more difficult for many algorithms.

### Example of data encoding using Python

Let's look at the process of data encoding using one-hot encoding and label encoding, which are two commonly used encoding techniques in machine learning.

We'll be using the Python library `pandas` for data manipulation and `sklearn` for the encoding techniques. Please note that you may need to [add the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder

# let's say we have some pipeline data like the following
data = {
    'Region': ['North', 'West', 'East', 'South', 'North', 'West', 'West', 'East'],
    'Product': ['Apples', 'Oranges', 'Bananas', 'Apples', 'Bananas', 'Bananas', 'Oranges', 'Apples'],
    'Quantity': [100, 150, 200, 100, 300, 200, 150, 100]
}

df = pd.DataFrame(data)

# let's print the original dataframe
print("Original DataFrame:")
print(df)

# data encoding
# for simplicity, we'll use label encoding for the 'Region' column and one-hot encoding for the 'Product' column

# label encoding
le = LabelEncoder()
df['Region_encoded'] = le.fit_transform(df['Region'])

# one-hot encoding
ohe = OneHotEncoder(sparse_output=False)
ohe_results = ohe.fit_transform(df[['Product']])
ohe_df = pd.DataFrame(ohe_results, columns=ohe.get_feature_names_out(['Product']))

# concatenate original dataframe with the one-hot encoding dataframe
df = pd.concat([df, ohe_df], axis=1)

print("DataFrame after Encoding:")
print(df)
```

This Python script creates a simple dataframe from the given data. It then applies label encoding to the 'Region' column, where each unique region name is assigned an integer value. It also applies one-hot encoding to the 'Product' column, creating a new binary column for each unique product.

The resulting dataframe contains the original data, as well as the encoded 'Region' and 'Product' data.

Here is our output:

```plaintext
Original DataFrame:
  Region  Product  Quantity
0  North   Apples       100
1   West  Oranges       150
2   East  Bananas       200
3  South   Apples       100
4  North  Bananas       300
5   West  Bananas       200
6   West  Oranges       150
7   East   Apples       100
DataFrame after Encoding:
  Region  Product  Quantity  Region_encoded  Product_Apples  Product_Bananas  Product_Oranges
0  North   Apples       100               1             1.0              0.0              0.0
1   West  Oranges       150               3             0.0              0.0              1.0
2   East  Bananas       200               0             0.0              1.0              0.0
3  South   Apples       100               2             1.0              0.0              0.0
4  North  Bananas       300               1             0.0              1.0              0.0
5   West  Bananas       200               3             0.0              1.0              0.0
6   West  Oranges       150               3             0.0              0.0              1.0
7   East   Apples       100               0             1.0              0.0              0.0
```

Encoding data is a crucial step in preparing categorical data for use in machine learning algorithms, as these algorithms typically require numerical input.

---

## Enrich

> Enhance data with additional information from external sources.

Source: https://dagster.io/glossary/data-enrichment

### Data enrichment definition:

Enriching data in the context of modern data pipelines refers to the process of adding additional information or context to existing data, which can help improve its value and usefulness. This can involve integrating data from different sources, such as APIs or external databases, or performing data transformations to derive new insights.

One practical example of enriching data in Python is using an API to retrieve additional information about customers in a sales dataset. For instance, you could use the Twitter API to retrieve users' social media handles and add that information to a customer profile dataset.

Another example of data enrichment is performing sentiment analysis on customer feedback data, which involves using natural language processing (NLP) techniques to analyze the tone and emotion expressed in written feedback. This can help identify areas for improvement in a product or service, and improve customer satisfaction.

### Data enrichment example in Python:

In Python, there are several libraries and tools available for enriching data, including Pandas, NumPy, and NLTK (Natural Language Toolkit). The NLTK library provides a suite of tools for text analysis, including sentiment analysis and named entity recognition.  Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run the code samples below.

Here's an example of how to enrich data using the NLTK library in Python:

```python
import nltk
from nltk.corpus import wordnet

# Sample data
text = "Regarding data orchestration, Dagster is clearly the superior solution."

# Tokenize the text
tokens = nltk.word_tokenize(text)

# Define a function to find synonyms for a given word
def get_synonyms(word):
    synonyms = []
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
            synonyms.append(lemma.name())
    return set(synonyms)

# Enrich the data by adding synonyms for each word
enriched_data = []
for token in tokens:
    synonyms = get_synonyms(token)
    enriched_data.append((token, synonyms))

# Print the enriched data
print(enriched_data)
```

Here, we start by importing the necessary NLTK library and wordnet corpus. We then define a sample text and tokenize it into individual words.  Next, we define a function `get_synonyms()` that takes a word as input and returns a set of synonyms using the NLTK wordnet corpus. We use this function to find synonyms for each word in the text, and store the results in a list of tuples `enriched_data` , where each tuple contains the original word and its synonyms.

Here is a sample output:

```plaintext
[('Regarding', {'affect', 'see', 'regard', 'reckon', 'view', 'involve', 'consider'}), ('data', {'datum', 'data_point', 'data', 'information'}), ('orchestration', {'instrumentation', 'orchestration'}), (',', set()), ('Dagster', set()), ('is', {'comprise', 'represent', 'exist', 'follow', 'make_up', 'personify', 'be', 'embody', 'live', 'cost', 'equal', 'constitute'}), ('clearly', {'understandably', 'distinctly', 'intelligibly', 'clear', 'clearly'}), ('the', set()), ('superior', {'Superior', 'higher-up', 'ranking', 'master', 'higher-ranking', 'superordinate', 'superior', 'Lake_Superior', 'victor', 'superscript'}), ('solution', {'answer', 'root', 'resolution', 'solvent', 'solution', 'result'}), ('.', set())]
```

---

## ETL

> Extract, transform, and load data between different systems.

Source: https://dagster.io/glossary/etl-extract-transform-load

### ETL definition:

ELT stands for Extract, Load, Transform, and is a process used in modern data pipelines for integrating and transforming data from various sources into a centralized data store. The ELT process is similar to the more traditional ETL (Extract, Transform, Load) process, but with a key difference: data is extracted from source systems and loaded directly into a data store, where it can then be transformed.

[Dagster](https://dagster.io/) provides [many integrations with common ETL/ELT tools](https://dagster.io/integrations).

### Data ETL example in Python:

Here's an example of an ELT process in Python using the Pandas library and SQLite3. Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run the code:

Given two input files of customers.csv and orders.csv as follows:

```plaintext
| Customer ID | Name     | Total Orders |
| ----------- | -------- | ------------ |
| 1           | Mary     |           42 |
| 2           | Brian    |           28 |
| 3           | Mercedes |            7 |
| 4           | Rose     |           20 |
```

and

```plaintext
| Customer ID | Item     | Value |
| ----------- | -------- | ----- |
| 1           | cat food |    12 |
| 1           | apples   |     6 |
| 1           | books    |    24 |
| 2           | bananas  |    12 |
| 2           | candles  |    12 |
| 2           | coffee   |     4 |
| 3           | nails    |     7 |
| 4           | paper    |    18 |
| 4           | pencils  |     2 |
```

**Extract**: We start by extracting data from our source systems. For this example, let's assume we have a CSV file containing customer orders.

```python
import pandas as pd

# Extract data from CSV file
orders_df = pd.read_csv('orders.csv')
customers_df = pd.read_csv('customers.csv')
```

**Load**: Next, we load the data into our data store. In this case, we'll use a SQLite database.

```python
import sqlite3

# Connect to SQLite database
conn = sqlite3.connect('my_database.db')

# Load data into database
orders_df.to_sql('orders', conn, if_exists='replace', index=False)
customers_df.to_sql('customers', conn, if_exists='replace', index=False)
```

**Transform:** Finally, we transform the data as needed. For example, we might want to join the orders data with customer data to get more insights.

```python
# Extract customer data from database
customers_df = pd.read_sql_query('SELECT * FROM customers', conn)

# Join orders and customers data
enriched_df = pd.merge(orders_df, customers_df, on='customer_id')

print(enriched_df)
```

In this example, we used Pandas to extract data from a CSV file, load it into a SQLite database, and then join it with customer data to enrich the dataset. This ELT process can be repeated for other sources of data, allowing us to integrate and transform multiple datasets into a centralized data store.

Our example would yield the following output:

```plaintext
| Customer ID | Item     | Value | Name     | Total Orders |
| ----------- | -------- | ----- | -------- | ------------ |
| 1           | cat food |    12 | Mary     |           42 |
| 1           | apples   |     6 | Mary     |           42 |
| 1           | books    |    24 | Mary     |           42 |
| 2           | bananas  |    12 | Brian    |           28 |
| 2           | candles  |    12 | Brian    |           28 |
| 2           | coffee   |     4 | Brian    |           28 |
| 3           | nails    |     7 | Mercedes |            7 |
| 4           | paper    |    18 | Rose     |           20 |
| 4           | pencils  |     2 | Rose     |           20 |
```

---

## Export

> Extract data from a system for use in another system or application.

Source: https://dagster.io/glossary/data-export

### Data export definition:

Data export involves moving data from one system to another, often for sharing or analysis purposes. Exporting data involves converting the data into a specific format that can be easily imported by the target system. This can involve various steps such as selecting the relevant data, cleaning and transforming it, and formatting it for export.

There are several techniques for data export when designing your pipelines, depending on the requirements and constraints of the system. Some common techniques include:

- **Writing data to a file**: This is a simple and widely used technique where data is written to a file in a specific format (e.g., CSV, JSON, Parquet). The file can then be used by downstream systems or loaded into a database.
- **Exporting data to a database**: This involves exporting data directly to a database, where it can be used by downstream systems. The database can be a traditional relational database or a NoSQL database, depending on the data and use case.
- **Publishing data to a messaging system**: In this technique, data is published to a messaging system like Apache Kafka or RabbitMQ. This allows other systems to consume the data in real-time.
- **Streaming data to a cloud-based data warehouse**: This technique involves streaming data directly to a cloud-based data warehouse like Amazon Redshift or Google BigQuery. This allows for real-time analysis and reporting.

In each of these techniques, the data is typically transformed and processed before being exported to ensure that it is in the correct format and contains the required information. Python libraries like pandas, pyodbc, and sqlalchemy can be used to facilitate data export to various sources.

### Data exports in Python

Python provides a variety of libraries and tools to export data in various formats, including CSV, Excel, JSON, and more.  Other libraries, such as openpyxl or xlsxwriter, can be used to export data to Excel files, and json library can be used to export data in JSON format.

When managing multiple environments like production, staging, and development in a data pipeline, it's important to have a system in place for exporting data that is appropriate for each environment. Here are some techniques for data export in the context of managing multiple environments:

**Parameterization**: One approach is to parameterize the data export process so that it can be configured differently for each environment. For example, you might define a set of environment-specific configuration files that specify the output format, file location, and other details for each environment.

**Automated deployment**: Another technique is to use an automated deployment system that can handle the process of exporting data as part of the overall deployment process. This approach can help ensure that the correct data is exported for each environment and that the process is repeatable and consistent across all environments.

**Data archiving**: In some cases, it may be necessary to archive data from one environment and Export logging: It's You can write scripts that automate the data export process, making it easy to manage and export data across multiple environments. Dagster provides functionality for managing data pipelines and automating the export process across different environments.

Dagster provides a main abstraction called [iO Managers](https://docs.dagster.io/guides/build/io-managers) for separating storage logic for exporting data from your main business logic.  Such abstractions can be used to dramatically accelerate your development lifecycle by helping you work efficiently across environments.

---

## Extrapolate

> Predict values outside a known range, based on the trends or patterns identified within the available data.

Source: https://dagster.io/glossary/data-extrapolation

### Extrapolation definition

**Data Extrapolation** is a statistical method used to estimate or predict values outside a known range, based on the trends or patterns identified within the available data.

### An example of extrapolation in Python

Extrapolation is an important method used in machine learning, statistics, and data engineering to predict or infer values that go beyond the original observation range.But let's be aware that while extrapolation can provide insights into potential trends, it may lead to less accurate predictions due to inherent uncertainties or changes in patterns beyond the observed data range.Let's consider a simple linear regression example with extrapolation. Here's a Python script which performs extrapolation using Scikit-Learn's linear regression. Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Set a random seed for reproducibility
np.random.seed(42)

# Generate some synthetic data
X = np.array(sorted(list(range(5))*20)) + np.random.normal(size=100, scale=0.5)
y = X + np.random.normal(size=100, scale=1)

# Reshape X to a 2D array
X = X.reshape(-1,1)

# Create and train the model
model = LinearRegression()
model.fit(X, y)

# Generate some future points for extrapolation
X_future = np.array(range(-2, 10)).reshape(-1, 1)

# Predict their corresponding y values
y_future = model.predict(X_future)

# Plot the original data
plt.scatter(X, y, color = "blue")

# Plot the line of best fit
plt.plot(X_future, y_future, color = "red")

# Plot the extrapolated points
plt.scatter(X_future, y_future, color = "green")

# Set title
plt.title('Linear Regression Extrapolation')

# Show the plot
plt.show()
```

In this script, we first generate some synthetic data, which we reshape to 2D so it can be used in the LinearRegression model. We then train the model on this data.

We generate some future X values (from -2 to 9, which goes beyond our original X values from roughly 0 to 5). We then use the model to predict what the y values for these X values will be, which is the extrapolation.

The plot shows the original data (blue dots), the line of best fit (red line), and the extrapolated points (green dots).

![](https://dagster.io/images/glossary/681399f654933b29e12fbb0b_extrapolate-1.png)

As you can see, the model has extrapolated the trend from the original data to the future data.

Remember, extrapolation can lead to less accurate predictions, because it's assuming that the current trend (which is derived from existing data) will continue in the future. This may not always be the case in real-world situations.

### ML Extrapolation

The example above is quite basic and shows a linear model. [Phillip Isola](https://twitter.com/phillip_isola) shared [this interesting non-linear example](https://twitter.com/phillip_isola/status/1680344648105984003) of extrapolation in ML.

Phillip says:

A simple, fun example to refute the common story that ML can interpolate but not extrapolate: Black dots are training points. Green curve is true data generating function. Blue curve is best fit. Notice how it correctly predicts far outside the training distribution!

![](https://dagster.io/images/glossary/681399f654933b29e12fbabd_extrapolate-2.png)

You will find the code below and Phillip shared the [repo here](https://gist.github.com/phillipi/038cd0322520f30ff155954a2c4f6971).

```python
import numpy as np
import matplotlib.pyplot as plt

def mk_data(true_theta, noise_sd, N_data_points, data_mean, data_sd):
  data_x = np.expand_dims(np.random.randn(N_data_points)*data_sd + data_mean,axis=1)
  data_y = np.zeros(data_x.shape)
  for k in range(data_y.shape[0]):
    data_y[k,0] = np.expand_dims(np.array([f(data_x[k,0],true_theta)]),axis=1) + np.random.randn(1)*noise_sd
  data = np.concatenate([data_x,data_y],axis=1)
  return data

def mk_loss_map(data,f):
  theta_min = -2
  theta_max = 2
  [xx,yy] = np.meshgrid(np.linspace(theta_min,theta_max,101), np.linspace(theta_min,theta_max,101))

  data_fit_loss = np.zeros(xx.shape)

  for i in range(xx.shape[0]):
    for j in range(yy.shape[1]):
      theta = np.concatenate([xx[[i],j],yy[[i],j]],axis=0)
      for k in range(data.shape[0]):
        data_fit_loss[i,j] += loss(f(data[k,0], theta),data[k,1])
  data_fit_loss /= data.shape[0]

  return data_fit_loss, xx, yy

def get_best_theta(J,xx,yy):
  best_theta_idx = np.unravel_index(J.argmin(), J.shape)
  best_theta = np.concatenate([xx[[best_theta_idx[0]],best_theta_idx[1]], yy[[best_theta_idx[0]],best_theta_idx[1]]], axis=0)
  return best_theta

def mk_plot(J,data,true_theta,best_theta):

  u = 4
  xx = np.linspace(-u,u,101)
  plt.plot(xx,f(xx,best_theta),c='b',linewidth=2,zorder=1)
  plt.plot(xx,f(xx,true_theta),'--',c='g',linewidth=3,zorder=2)
  plt.scatter(data[:,0],data[:,1],c='k',zorder=3)
  plt.gca().set_xlim(-u,u)
  plt.gca().set_ylim(-u,u)
  plt.gca().set_aspect('equal')
  plt.gca().set_xlabel('$x$')
  plt.gca().set_ylabel('$y$',rotation=0)

def mk_fit(true_theta, noise_sd, N_data_points, data_mean, data_sd):
  data = mk_data(true_theta, noise_sd, N_data_points, data_mean, data_sd)
  J, xx, yy = mk_loss_map(data,f)
  best_theta = get_best_theta(J, xx, yy)
  return J, best_theta, data

def f(x,theta):
  y = np.sin(theta[1]*x**2) + theta[0]*x
  return y

def loss(x,y):
  return np.abs(x-y)**0.25


seed = 4
np.random.seed(seed)

true_theta = np.array([0.2,-0.5])
noise_sd = 0.2

data_mean = -3
data_sd = 0.5
N_data_points = 25
J, best_theta, data = mk_fit(true_theta, noise_sd, N_data_points, data_mean, data_sd)
mk_plot(J, data, true_theta, best_theta)

# Show the plot
plt.show()
```

---

## Fan-Out

> A pipeline design in which one operation is broken into - or results in - many parallel downstream tasks.

Source: https://dagster.io/glossary/fan-out

### Fan-out definition:

"Fan-out" is a tricky term in data engineering as it can be applied to many different scenarios.  It originated in the design of electronic circuits in which a "fan-out" design involves a signal being split or merged from one 'upstream' component to many parallel sub-components for effective processing (or in the case of 'fan-in', it's the other way around.)

In data engineering and the design of data pipelines, "Fan-out" generically refers to a scenario where one upstream operation triggers many similar downstream processes. One example could be an overnight data update that triggers dozens of downstream reports.

![](https://dagster.io/images/glossary/681399f654933b29e12fb9cc_fan-out-1.png)

From here, it's easy to picture how a second fan-out step in a pipeline can logarithmically scale the number of tasks required:

![](https://dagster.io/images/glossary/681399f654933b29e12fbaa0_fan-out-2.png)

In a 'fan-out' scenario, the individual 'branches' of the fan-out process can be referred to as "tasks", "jobs", or "workers", depending on the specific context or the tool being used.

- In data pipelines or data orchestration, they might be referred to as "tasks" or "jobs".
- In the context of parallel computing or distributed systems, these branches might be referred to as "workers" or "processes". A "worker" typically refers to a single entity (like a CPU, thread, or machine in a cluster) that is responsible for executing a piece of work.

### Use cases

In data engineering, there are various processes and scenarios where a fan-out approach is often applied to improve efficiency and performance. Here are a few examples:

1. **Data Ingestion**: In high-velocity data ingestion scenarios, data needs to be captured from various sources and processed rapidly. Fan-out can be employed to distribute the incoming data to multiple processing nodes. This is common in real-time analytics or streaming applications where data is captured and processed concurrently.
2. **Distributed Computing**: With big data, it's common to use distributed systems such as Dask or Spark. These systems use a fan-out approach to distribute data across multiple nodes in a cluster for parallel processing. For instance, in the [MapReduce programming model](https://en.wikipedia.org/wiki/MapReduce), the Map phase is a fan-out operation where input data is divided into chunks and mapped onto various nodes.
3. **Data Transformation**: During ETL (Extract, Transform, Load) operations, transformations may need to be performed on large datasets. Fan-out can be used to distribute the transformations across multiple workers to speed up the process.
4. **Distributed Databases**: In distributed databases, a fan-out approach can be used to distribute queries across multiple nodes. Each node searches a portion of the data, improving the query's speed.
5. **Message Brokers**: In message-oriented middleware or event-driven architectures, a single message published to a topic can be delivered to multiple subscribers, effectively implementing a fan-out pattern. Apache Kafka is an example of such a system.
6. **Data Replication**: Fan-out is often used in data replication strategies, where data from one database is copied and distributed to multiple replica databases for load balancing or redundancy purposes.

### Scaling up execution in a fan-out design

While "fan-out" does not *explicitly* mean scaling up data processing through parallelization or multi-threading, data engineers often imply such a process when using the term.  It is easy to see how a pipeline with a 'fan-out' requirement would rapidly require a scale up of computing power, with each sub-task ran in isolation from the others.

With this in mind, we will see definitions of "fan-out" that explicitly refer to concurrent isolated execution:

The term "Fan-out" refers to the design of systems or algorithms to distribute tasks, data, or computations across multiple nodes or processes. This term is often used in reference to the structure of networks, database systems, or distributed computing environments.

In Python, there are four main techniques to managing processing in a fan-out design:  multiprocessing, threading, parallelizing, and AsynchIO. These are both common techniques to achieve concurrent execution and improve performance. However, they differ in how they handle parallelism and utilize system resources.

- **Multiprocessing** is typically used for tasks that involve heavy data processing, such as large-scale data transformations, computations, or machine learning training on multiple cores.
- **Threading** is more commonly used for tasks that involve I/O operations, such as reading and writing files, network communication, or interacting with databases.
- **Parallelizing** refers to the breaking down of data processing tasks into smaller units of work that can be executed concurrently, either on a single machine with multiple processing units or across distributed computing resources.
- **AsynchIO** is a specific library in Python for asynchronous I/O, built around the coroutines of Python and provides tools to manage them and handle the I/O in an efficient way.

You can find more details on each technique in their respective glossary entries:

Jump to the entry for 'Multiprocess.'

Jump to the entry for 'Threading.'

Jump to the entry for 'Parallelize.'

Jump to the entry for 'AsychIO'.

Note that while these techniques can enhance performance, each may also introduce complexities related to shared data access, synchronization, and potential race conditions. Therefore, proper care should be taken when implementing concurrent solutions using either approach.

### Concurrency vs. parallelism

![](https://dagster.io/images/glossary/681399f654933b29e12fba54_fan-out-3.png)

There is a rich conversation in the world of Python programming around concurrency vs. parallelism. Here's a brief explanation of each:

1. **Concurrency** is when multiple tasks are able to start, run, and complete in overlapping time periods. It doesn't necessarily mean that they'll be all executing at the same instant. For example, if you are running a multi-threaded program on a single-core machine, you might have multiple threads of execution, with the processor switching between threads. This is a form of *concurrency*.
2. **Parallelism**, on the other hand, is where multiple tasks are executing *at the same time*. It implies a system with multiple processing units (i.e., multicore or multiprocessor). In a parallel system, multiple tasks are being executed simultaneously on different cores or processors.

**Concurrency is about dealing with lots of things at once.** It's a concept used to structure a solution to solve complex problems that may involve multiple asynchronous tasks. Concurrent programming can make a significant impact on the responsiveness and performance of software where managing multiple simultaneous connections, users, or inputs is essential. It's more about the design of your system, how it's organized.

**Parallelism, on the other hand, is about doing lots of things at once to maximize computational speed.** It's often used in contexts where a problem can be divided into discrete, independent tasks that can be executed simultaneously (like matrix operations in scientific computing or rendering graphics).

The key is choosing the right approach for the right problem in your pipeline. If you have a lot of IO-bound tasks (like reading and writing to disk or network), then concurrency can be a good way to keep your program responsive and efficient. If you have a lot of CPU-bound tasks (like computations), then parallelism can help make your program faster by utilizing multiple cores or processors.

Note that concurrency and parallelism are *not* mutually exclusive. Concurrency is about the structure of the software, parallelism is about its execution. Concurrent software may or may not execute in parallel, depending on the runtime conditions and hardware.

#### Python's Global Interpreter Lock (GIL)

When it comes to Python programming, understanding these two concepts is crucial due to Python's Global Interpreter Lock (GIL), a mechanism used in the CPython interpreter to synchronize access to Python objects, preventing multiple native threads from executing Python bytecodes at once. The GIL is a barrier to multithreaded Python programs achieving true parallelism on a multicore processor.

The consequence of the GIL is that in Python, multithreaded programs don't execute threads truly concurrently on multiple cores; they merely interleave the execution of threads, which is beneficial for I/O-bound programs (waiting for I/O such as disk or network operations) but not for CPU-bound programs (intense computation tasks). This is where multiprocessing comes in, which involves using multiple separate Python interpreter processes, not threads. Because these processes are separate, they can execute on different cores and achieve true parallelism (with the right kind of task).

In Python:- You use threads or AsyncIO for I/O-bound tasks, achieving concurrency.- You use processes for CPU-bound tasks, achieving parallelism.

However, other Python interpreters such as [Jython](https://www.jython.org/) and [IronPython](https://ironpython.net/) don't have a GIL and can achieve multithreading parallelism, but they are less commonly used compared to CPython. Also, using Python with external libraries like Numpy, which release the GIL when they do heavy computation, can achieve parallelism.

For a more complete exploration of concurrency vs. parallelism, we recommend [Parallelism, Concurrency, and AsyncIO in Python - by example](https://testdriven.io/blog/python-concurrency-parallelism/) by [Amal Shaji](https://testdriven.io/authors/shaji/)

---

## Feature Extraction

> Identify and extract relevant features from raw data for use in analysis or modeling.

Source: https://dagster.io/glossary/feature-extraction

### Feature extraction definition:

Feature extraction is the process of identifying and extracting relevant features from raw data to create a set of informative features that can be used for machine learning or other data analysis tasks. The aim of feature extraction is to reduce the dimensionality of the data while retaining as much relevant information as possible.

In the context of modern data pipelines, feature extraction is an important step in preparing data for machine learning models. It can significantly improve the accuracy of predictive models, reduce model training time, and reduce the risk of overfitting.

### Feature extraction example using Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

The following are some techniques for feature extraction in Python:

**Bag of Words (BoW):** BoW is a simple but effective technique for feature extraction in natural language processing (NLP) tasks. It involves counting the frequency of each word in a corpus of text and representing each document as a vector of word counts that can be used as input for machine learning algorithms. This can be implemented using libraries like scikit-learn:

```python
from sklearn.feature_extraction.text import CountVectorizer

corpus = ["This is the first document.", "This is the second document.", "And this is the third one.", "Is this the first document?"]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(X.toarray())
```

This code would yield the following output:

```plaintext
[[0 1 1 1 0 0 1 0 1]
 [0 1 1 0 0 1 1 0 1]
 [1 0 0 1 1 0 1 1 0]
 [0 1 1 1 0 0 1 0 1]]
```

**TF-IDF:** Term Frequency-Inverse Document Frequency (TF-IDF) is another technique for feature extraction in NLP tasks. It assigns weights to each word based on its frequency in the document and its rarity in the corpus.

```python
from sklearn.feature_extraction.text import TfidfVectorizer

corpus = ["This is the first document.", "This is the second document.", "And this is the third one.", "Is this the first document?"]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
print(X.toarray())
```

This code would yield the following output:

```plaintext
[[0.         0.43877674 0.54197657 0.43877674 0.         0.         0.35872874 0.         0.43877674]
 [0.         0.43877674 0.54197657 0.         0.         0.65465367 0.35872874 0.         0.43877674]
 [0.57735027 0.         0.         0.57735027 0.57735027 0.         0.29247968 0.57735027 0.        ]
 [0.         0.43877674 0.54197657 0.43877674 0.         0.         0.35872874 0.         0.43877674]]
```

**Principal Component Analysis (PCA):** PCA is a technique for feature extraction that reduces the dimensionality of the data while retaining as much information as possible. It involves transforming the data into a new coordinate system that maximizes the variance of the data.

```python
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA

iris = load_iris()
X = iris.data
y = iris.target

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

print(X_pca)
```

This code would yield the following output:        

```plaintext
[[-2.68412563  0.31939725]
 [-2.71414169 -0.17700123]
 [-2.88899057 -0.14494943]
 [-2.74534286 -0.31829898]
 [-2.72871654  0.32675451]
 ...
 [ 1.90094161  0.11662796]
 [ 1.39018886 -0.28266094]]
```

**Scale-Invariant Feature Transform (SIFT) for Image data:** In computer vision, feature extraction involves transforming raw image data into numerical representations that capture important characteristics of the image, such as edges, corners, and texture. One popular technique for image feature extraction is the Scale-Invariant Feature Transform (SIFT), which detects and describes local features in an image. This can be implemented using the OpenCV library:

```python
import cv2

# Load an image
img = cv2.imread('image.jpg')
image8bit = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX).astype('uint8')

# Create a SIFT detector
sift = cv2.SIFT_create()

# Detect and compute keypoints and descriptors
kp, des = sift.detectAndCompute(image8bit, None)

# Print the number of keypoints and the shape of the descriptor matrix
print('Number of keypoints:', len(kp))
print('Descriptor shape:', des.shape)
```

Based on the input image you provide, your output will look something like this:

```plaintext
Number of keypoints: 1448
Descriptor shape: (1448, 128)
```

**Autoregressive Integrated Moving Average (ARIMA) model for time series data:** In time series analysis, feature extraction involves transforming raw time series data into numerical representations that capture important patterns and trends in the data, such as seasonality, trend, and volatility. One popular technique for time series feature extraction is the Autoregressive Integrated Moving Average (ARIMA) model, which models the data as a combination of autoregressive, differencing, and moving average terms. This can be implemented using the `statsmodels` library:

Given the simple `timeseries.csv` input file:

```plaintext
Date,Value
2023-05-01,10
2023-05-02,15
2023-05-03,20
2023-05-04,18
2023-05-05,22
2023-05-06,25
2023-05-07,30
2023-05-08,35
2023-05-09,40
2023-05-10,45
2023-05-11,50
2023-05-12,55
2023-05-13,60
2023-05-14,65
2023-05-15,70
```

Will return the following output:

```python
import pandas as pd
import numpy as np
import statsmodels.api as sm

# Load a time series dataset
data = pd.read_csv('timeseries.csv', parse_dates=['date'], index_col='date')

# Create an ARIMA model
model = sm.tsa.ARIMA(data, order=(1, 1, 1))

# Fit the model to the data
results = model.fit()

# Print the model summary
print(results.summary())
```

```plaintext
                               SARIMAX Results
==============================================================================
Dep. Variable:                  Value   No. Observations:                   15
Model:                 ARIMA(1, 1, 1)   Log Likelihood                 -30.790
Date:                Sat, 22 Apr 2023   AIC                             67.580
Time:                        21:57:35   BIC                             69.497
Sample:                    05-01-2023   HQIC                            67.403
                         - 05-15-2023
Covariance Type:                  opg
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.9956      0.047     20.976      0.000       0.903       1.089
ma.L1         -0.7962      0.914     -0.871      0.384      -2.588       0.995
sigma2         3.9599      4.594      0.862      0.389      -5.045      12.965
===================================================================================
Ljung-Box (L1) (Q):      0.14   Jarque-Bera (JB): 45.05
Prob(Q):                 0.70   Prob(JB):         0.00
Heteroskedasticity (H):  0.05   Skew:            -2.75
Prob(H) (two-sided):     0.00   Kurtosis:         9.86
=================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
```

---

## Feature Scaling

> A method used to normalize the range of independent variables or features of data.

Source: https://dagster.io/glossary/feature-scaling

---

## Feature Selection

> Identify and select the most relevant and informative features for analysis or modeling.

Source: https://dagster.io/glossary/feature-selection

### Feature selection definition:

Feature selection is the process of selecting a subset of relevant features (variables or columns) from a larger set of features to use in a model or analysis. This process can help reduce the dimensionality of the data and improve the accuracy and efficiency of the model. In the context of modern data pipelines, feature selection is an important step in preparing data for machine learning algorithms.

Feature selection isn’t always necessary or beneficial for every dataset or model. It's important to evaluate the impact of feature selection on model performance and to carefully consider the trade-off between reducing dimensionality and potentially losing important information.

### Python techniques for feature selection

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

There are many techniques for feature selection, ranging from simple threshold-based methods to more complex methods like wrapper and embedded methods. Some popular methods for feature selection include:

- **Filter methods:** These methods select features based on their statistical properties, such as correlation with the target variable or variance. Examples of filter methods in Python include `SelectKBest` and `SelectPercentile` from scikit-learn.
- **Wrapper methods:** These methods evaluate the performance of a model using a particular subset of features, and select the best subset based on the model performance. Examples of wrapper methods in Python include recursive feature elimination (RFE) and forward selection.
- **Embedded methods:** These methods perform feature selection as part of the model training process. Examples of embedded methods in Python include Lasso and Ridge regression, which perform regularization to select relevant features.

Here's an example of using `SelectKBest` from scikit-learn to select the top 5 features based on their correlation with the target variable:

```python
import numpy as np
from sklearn.feature_selection import SelectKBest, f_regression

np.random.seed(42)
X = np.random.rand(100, 10)
y = np.random.rand(100)

# select top 5 features based on correlation with y
selector = SelectKBest(score_func=f_regression, k=5)
X_new = selector.fit_transform(X, y)

# get indices of selected features
mask = selector.get_support()
selected_features = np.arange(X.shape[1])[mask]
print(selected_features)
```

In this example, we generate some sample data - a matrix `X` with 10 features and 100 samples, and a vector `y` with the target variable.We then use `SelectKBest` to select the top 5 features based on their correlation with `y`. We use the `f_regression` scoring function, which computes the correlation between each feature and `y` using an F-test.

The `fit_transform` method of the `SelectKBest` object returns a new matrix `X_new` with only the top 5 features selected. We can also access the selected features using the `get_support` method:

The above code will yield this output:

```javascript
[0 1 3 6 8]
```

So the top 5 features based on their correlation with y are the 0th, 1st, 3rd, 6th, and 8th features.

---

## Feature Store

> A centralized repository for storing, serving, and sharing machine learning features, allowing for the consistent use of features across different models.

Source: https://dagster.io/glossary/feature-store

---

## Filter

> Extract a subset of data based on specific criteria or conditions.

Source: https://dagster.io/glossary/data-filtering

### Data filtering definition:

Filtering data in the context of modern data pipelines refers to the process of selecting a subset of data based on some criteria or condition. This is often used to reduce the amount of data to be processed, to remove irrelevant or redundant data, or to prepare data for further analysis.

### Data filtering example using Python:

In Python, filtering data can be accomplished using various built-in functions such as filter() and list comprehensions. The filter() function applies a function to each element of a sequence and returns a new sequence containing only the elements for which the function returns True. Here is an example:

```python
# Define a list of numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Filter out even numbers
filtered_numbers = list(filter(lambda x: x % 2 == 1, numbers))

# Print the filtered numbers
print(filtered_numbers)
```

This would output:

```plaintext
[1, 3, 5, 7, 9]
```

In this example, the lambda function checks whether the input number is odd or even. The `filter()` function applies this lambda function to each element of the numbers list and returns a new list containing only the odd numbers.

List comprehension provides another way to filter data in Python. Here's an example:

```python
# Define a list of numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Filter out even numbers using a list comprehension
filtered_numbers = [x for x in numbers if x % 2 == 1]

# Print the filtered numbers
print(filtered_numbers)
```

This would also output:

```plaintext
[1, 3, 5, 7, 9]
```

In this example, the list comprehension creates a new list that contains only the odd numbers from the original numbers list. The condition `x % 2 == 1` filters out even numbers.

Filtering data can also be done using libraries such as Pandas or NumPy. These libraries provide efficient ways to filter data in large datasets. For example, the `pandas.DataFrame.query()` method can be used to filter rows in a Pandas DataFrame based on a specified condition.

---

## Fragment

> Break data down into smaller chunks for storage and management purposes.

Source: https://dagster.io/glossary/data-fragmentation

### Data fragmentation definition:

Data fragmentation refers to the breaking down of data into smaller chunks or fragments for storage and management purposes. In modern data engineering, data fragmentation is often used to optimize data processing and storage, as well as to improve data availability and scalability.

Fragmentation can be applied to various types of data, such as files, databases, or objects, and can be done in various ways, such as horizontal fragmentation (splitting data by rows), vertical fragmentation (splitting data by columns), or hybrid fragmentation (a combination of horizontal and vertical fragmentation).

### Data fragmentation example using Python:

*Please note that you need to* [*have the Pandas library installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

Here's a self-contained Python example that demonstrates horizontal fragmentation of a pandas DataFrame:

```python
import pandas as pd

# Create a sample DataFrame with 10 rows and 3 columns
df = pd.DataFrame({'A': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                   'B': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'],
                   'C': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]})

# Split the DataFrame into two fragments: one with rows 0 to 4 and one with rows 5 to 9
df1 = df.iloc[:5]
df2 = df.iloc[5:]

# Print the original DataFrame and the two fragments
print('Original DataFrame:')
print(df)
print('Fragment 1:')
print(df1)
print('Fragment 2:')
print(df2)
```

This will yield the following output:

```plaintext
Original DataFrame:
    A  B    C
0   1  a  0.1
1   2  b  0.2
2   3  c  0.3
3   4  d  0.4
4   5  e  0.5
5   6  f  0.6
6   7  g  0.7
7   8  h  0.8
8   9  i  0.9
9  10  j  1.0
Fragment 1:
   A  B    C
0  1  a  0.1
1  2  b  0.2
2  3  c  0.3
3  4  d  0.4
4  5  e  0.5
Fragment 2:
    A  B    C
5   6  f  0.6
6   7  g  0.7
7   8  h  0.8
8   9  i  0.9
9  10  j  1.0
```

In this example, we create a sample DataFrame with 10 rows and 3 columns. We then split the DataFrame into two fragments using the `iloc` method of pandas: `df1` contains rows 0 to 4, and `df2` contains rows 5 to 9. Finally, we print the original DataFrame and the two fragments to verify that the data has been correctly fragmented. This example demonstrates horizontal fragmentation, as we split the data by rows.

In practice, data fragmentation can be used in a variety of data engineering scenarios, such as distributed computing, parallel processing, or database sharding, to name a few. By breaking down data into smaller, more manageable fragments, we can distribute data processing and storage more efficiently, reduce data access times, and improve overall system performance and scalability.

---

## Full Stack Development

> The development of both front end (client-side) and back end (server-side) portions of a web application.

Source: https://dagster.io/glossary/full-stack-development

---

## Geospatial Analysis

> Analyze data that has geographic or spatial components to identify patterns and relationships.

Source: https://dagster.io/glossary/geospatial-analysis

### Geospatial analysis definition:

Geospatial analysis is the process of analyzing spatial data to extract meaningful insights and patterns. Geospatial data can be in the form of geographical coordinates, satellite images, and other related data. In the context of modern data pipelines, geospatial analysis can help in understanding the relationship between different geographic locations and other data points, such as demographics or climate data.

Geospatial data analysis is used to model and represent how people, objects, and phenomena interact within space, as well as to make predictions based on trends in the relationships between places.

Python has several libraries that can be used for geospatial analysis, including *Geopandas*, *Shapely*, *Fiona*, and *PySAL*. Here is a practical example using Geopandas and Matplotlib:

### Geospatial data analysis in Python

- **Matplotlib** installation instructions [are found here](https://matplotlib.org/stable/users/installing/index.html) but just involve the command `python -m pip install -U matplotlib`.

```python
import matplotlib.pyplot as plt
import geopandas as gpd

# Load the data
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))

# Plot the world map
fig, ax = plt.subplots(figsize=(10, 6))
world.plot(ax=ax, color='white', edgecolor='black')

# Plot the cities
cities.plot(ax=ax, markersize=5, color='red')

# Set the title and axis labels
ax.set_title('Cities of the world')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')

# Show the plot
plt.show()
```

This code loads two datasets from the Geopandas library: world and cities. The world dataset contains the shapes of all countries in the world, while the cities dataset contains the locations of major cities worldwide.

The code then creates a plot of the world map using matplotlib. It sets the color of the countries to white and the color of the borders to black. It then plots the cities on top of the map as red dots.

Finally, the code sets the title and axis labels of the plot and displays it using `plt.show()`.

This is just a simple example, but Geopandas and matplotlib offer a wide range of geospatial analysis tools that can be used for more advanced applications.

![](https://dagster.io/images/glossary/681399f654933b29e12fbcc4_glossary-7.png)

### Geospatial analysis in Python using Xarray-spatial

Here's a basic example of using the Xarray-spatial package for geospatial analysis in Python. This example uses the `hillshade` function to analyze topographical data from a digital elevation model ([DEM](https://kb.geoczech.org/knowledge-base/how-to-get-digital-elevation-model-dem-for-specific-area/)). To keep this example self-contained, I am creating a simple synthetic DEM data.

If you want to work with real-world data, you could substitute this synthetic data with an Xarray DataArray that contains your actual geospatial data. Also, this is a simplified example and does not take into account various geospatial complexities you may encounter in real-world datasets.

```python
# Required Libraries
import numpy as np
import xarray as xr
from xrspatial import hillshade

# Create synthetic Digital Elevation Model (DEM) data
dem_data = np.random.rand(5,5) * 100
dem_data = xr.DataArray(dem_data, dims=["x", "y"])

# Calculate hillshade
hillshade_data = hillshade(dem_data)

# Print the hillshade data
print(hillshade_data)
```

This script will generate hillshade data, a grayscale 3D representation of the surface, with the sun's relative position taken into account for shading the image. Hillshade is used to visualize terrain in a 2D map and is commonly used in geographical and environmental studies.

Please note that `xrspatial.hillshade` function computes the hillshade for a DEM (which is an input 2D DataArray). It uses the sun's azimuth and altitude and the vertical exaggeration of the terrain to calculate the illumination value for each cell in the DEM. It does not require any projection system.

For more advanced and specific operations, the library allows operations like terrain, proximity, focal, zonal, global, and local statistics, generalization, classification, and pathfinding.

Output:

```plaintext
<xarray.DataArray 'hillshade' (x: 5, y: 5)>
array([[       nan,        nan,        nan,        nan,        nan],
       [       nan, 0.27194697, 0.08499345, 0.90350395,        nan],
       [       nan, 0.88516366, 0.2557956 , 0.07063997,        nan],
       [       nan, 0.73102015, 0.8325217 , 0.8833356 ,        nan],
       [       nan,        nan,        nan,        nan,        nan]],
      dtype=float32)
Dimensions without coordinates: x, y
```

---

## Gradient Boosting

> A machine learning technique for regression and classification problems, which builds a model in a stage-wise fashion, optimizing for predictive accuracy.

Source: https://dagster.io/glossary/gradient-boosting

---

## Graph Theory

> A powerful tool to model and understand intricate relationships within our data systems.

Source: https://dagster.io/glossary/graph-theory

### Graph Theory - a Definition:

> Graph theory is the study of graphs—mathematical structures used to model pairwise relations between objects[1](#).

[      Jump to the code example ↓    ](#)

### The value of Graph Theory in Data Engineering

The objects mentioned in our definition above can be people in a social network, web pages connected by hyperlinks, or, in our case, data processing tasks or data assets.  Graph Theory provides a way to represent and study the structure and behavior of data as graphs, which consist of nodes (vertices) and edges (connections) between them.

As data engineers, we're well aware of the complexity that underlies the world of data processing. The pipelines we build are intricate, often resembling complex networks of systems and compute environments, with interconnected tasks or interdependent assets. Graph Theory provides us with a powerful tool to model and understand intricate relationships within our data systems. Think of it as a visual representation that helps us make sense of complex dependencies, optimize workflows, and identify bottlenecks.

In the context of data engineering, graph theory has several important applications:

**Data Modeling:** Graphs can be used to model complex data relationships where entities and their connections are crucial. For example, nodes might represent tables for products, warehouses, and factories, while edges represent the movement of goods.

**Database Design:** Graph databases, such as Neo4j, are designed to store and query graph-structured data efficiently. They are particularly well-suited for scenarios where relationships between data points are as important as the data itself. Graph databases enable efficient traversal of relationships, making them valuable in recommendation engines, fraud detection, and social network analysis.

**Data Integration:** When dealing with data from multiple sources, graph theory can help model the relationships and dependencies between different datasets. This can be useful in building data pipelines and [ETL (Extract, Transform, Load)](https://etl-extract-transform-load) processes, ensuring that data from various sources is integrated and transformed correctly.

**Network Analysis:** Graph theory is widely used in analyzing and optimizing network structures. This can be applied to data engineering in scenarios like optimizing data flows in a distributed system, identifying bottlenecks, or modeling dependencies between data processing tasks in a workflow.

**Semantic Data Modeling:** In semantic data modeling, graphs can be used to represent ontologies, taxonomies, or knowledge graphs. These are crucial for applications like natural language processing, recommendation systems, and data-driven decision-making.

**Dependency Resolution:** When dealing with data pipelines, dependencies between tasks or stages can be complex. Graph theory can help model and visualize these dependencies, making it easier to schedule, [monitor](https://data-monitoring), and troubleshoot data processing workflows.

**Data Quality and Lineage:** Graphs can be used to track the [lineage of data](https://data-lineage), showing how data flows from its source to its consumption. This lineage tracking is valuable for data governance, auditing, and ensuring data quality.

**Data Exploration and Visualization:** In data analysis and [exploration](https://data-exploration), graph theory can help visualize complex relationships within the data, making it easier to discover patterns, [anomalies](https://anomaly-detection), and insights.

### Directed Acyclic Graphs (DAGs)

One of the most significant applications of Graph Theory in data engineering is the use of *Directed Acyclic Graphs*, or DAGs. DAGs are a specific type of graph where edges have a direction, and there are no cycles (i.e., no way to start at a node and return to it by following the edges).

Why are DAGs so important to data engineers? Because they are a natural fit for modeling data processing workflows. Each node in a DAG represents a task or step in the workflow, and directed edges between nodes indicate task dependencies.

The **absence of cycles** ensures that the workflow can be executed without causing infinite loops or circular dependencies.  In the diagram below, the relationships defined in examples 1 and 2 are valid non-cyclical flows, whereas example 3 breaks this rule.

![](https://dagster.io/images/glossary/681399f654933b29e12fbcbd_data-graph-dag-min.png)

Designing our data pipelines with DAGs supports some fundamental capabilities, including workflow optimization, dependency management, data lineage, fault tolerance/retries, and parallelism.

### How Dagster Does Graphs

Dagster takes a unique approach to graphs thanks to its asset-centric framework.

Dagster helps you define, in a declarative way, the key data assets that your organization requires.  It then builds a graph of dependencies and manages the relationships, lineage, prioritization, and observability for you.

Dagster manifests this graph of assets directly within the product. The [asset graph](https://docs.dagster.io/dagster-basics-tutorial) is the center of the programming model, the subject of the development lifecycle, the seat of metadata, and the operational single pane of glass.

 The Dagster Asset Graph - a representation of key data assets in your data pipeline.

### An example of Graph Theory using Python

Let's illustrate the concept of Graph Theory using Python and `matplotlib`.

Imagine you are managing a complex data processing pipeline with multiple tasks, each with its dependencies. You want to optimize the execution of these tasks to minimize the overall processing time.

We can model the data processing pipeline as a DAG, where nodes represent tasks, and directed edges represent dependencies between tasks. We can then use topological sorting to find the optimal order in which to execute these tasks, considering their dependencies.

```python
import networkx as nx
import matplotlib.pyplot as plt

## Create a Directed Acyclic Graph (DAG) representing the data processing pipeline
data_pipeline = nx.DiGraph()

## Define tasks and their dependencies
tasks_and_dependencies = {
    "Task A": [],
    "Task B": ["Task A"],
    "Task C": ["Task A"],
    "Task D": ["Task B", "Task C"],
    "Task E": ["Task D"],
    "Task F": ["Task D"],
    "Task G": ["Task E", "Task F"],
}

## Add nodes and edges to the DAG
for task, dependencies in tasks_and_dependencies.items():
    data_pipeline.add_node(task)
    for dependency in dependencies:
        data_pipeline.add_edge(dependency, task)

## Visualize the DAG (optional)
pos = nx.spring_layout(data_pipeline, seed=42)
nx.draw(data_pipeline, pos, with_labels=True, node_size=800, node_color="skyblue", font_size=10)
plt.title("Data Processing Workflow DAG")
plt.show()

## Find the optimal execution order using topological sorting
optimal_execution_order = list(nx.topological_sort(data_pipeline))

## Print the optimal execution order
print("Optimal Execution Order of Tasks:")
for task in optimal_execution_order:
    print(task)
```

In this example:

- We create a DAG data\_pipeline to represent the data processing pipeline, where tasks are nodes and dependencies are directed edges.
- We define the tasks and their dependencies in the tasks\_and\_dependencies dictionary.
- We add nodes and edges to the DAG based on the defined dependencies.
- Optionally, we visualize the DAG to gain a visual representation of the data processing workflow.
- We use nx.topological\_sort from NetworkX to find the optimal execution order of tasks. Topological sorting ensures that tasks are executed in a way that respects their dependencies and minimizes the overall processing time.
- Finally, we print the optimal execution order of tasks, which is the order in which they should be executed to optimize the pipeline.

Here is what our result looks like:

![](https://dagster.io/images/glossary/681399f654933b29e12fbbfc_graph-theory.png)

This example demonstrates how graph theory, along with topological sorting, can be used to optimize complex data processing workflows in data engineering, ensuring efficient execution while respecting task dependencies.

**Conclusion**

As data engineers, our mission is to build robust, efficient, and scalable data processing pipelines. Graph Theory, especially through the lens of Directed Acyclic Graphs (DAGs), provides us with a framework to achieve these goals. By understanding the relationships and dependencies within our data systems, we can design and optimize workflows that meet the demands of the ever-growing data landscape. So, the next time you're architecting a data pipeline, remember that Graph Theory and DAGs are your trusted companions on the journey to data engineering excellence.

Learn more about [Dagster's Graph-backed Assets in the Dagster docs](https://docs.dagster.io/guides/build/assets/defining-assets#graph-asset).

\[1] See \[Wikipedia entry for Graph Theory]\(<https://en.wikipedia.org/wiki/Graph_theory>)

---

## Hadoop Distributed File System (HDFS)

> A distributed file system designed to run on commodity hardware, providing high-throughput access to application data and fault tolerance.

Source: https://dagster.io/glossary/hadoop-distributed-file-system-hdfs

---

## Hash

> Convert data into a fixed-length code to improve data security and integrity.

Source: https://dagster.io/glossary/data-hashing

### Hashing definition:

Hashing is the process of transforming data of arbitrary size into a fixed-size output called a hash value. The hash value is a unique and consistent identifier for the input data, which makes it useful for a variety of purposes such as data security, indexing, and comparison.

One of the most common uses of hashing is in password storage. When a user creates a password, it is hashed and stored in a database. When the user enters their password during login, it is hashed again and compared to the stored hash value. If the hash values match, the user is granted access.

#### Data hashing example using Python and hashlib:

*Please note that you need to* [*have the hashlib library installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

In Python, the hashlib module provides a way to generate hash values. Here is an example of how to use the sha256 algorithm to hash a string:

```python
import hashlib

input_data = "Hello World"
hash_object = hashlib.sha256(input_data.encode())
hash_value = hash_object.hexdigest()
print(hash_value)
```

This code will output the hash value *b94d27b9934d3e08a‧52e52d7da7dabfac484efe37‧a5380ee9088f7ace2efcde9* (string hyphenated for layout), which is a fixed-length string of 64 characters representing the hash of the input string "Hello World".

Hashing is also commonly used in data processing pipelines for indexing and comparison purposes. For example, if you have a large dataset and want to quickly find all the records that match a specific value, you can hash the value and use it as an index to quickly retrieve the matching records.

---

## Homogenize

> Make data uniform, consistent, and comparable.

Source: https://dagster.io/glossary/data-homogenization

### Data homogenization definition

**Data homogenization** is the process of making data uniform, consistent, and comparable. As data sources multiply and diversify, data can come in various formats, structures, and quality. When we talk about homogenizing data, we're talking about transforming and preparing this diverse data in a way that it becomes coherent and can be used seamlessly across applications, analyses, or systems.

Data homogenization is crucial in scenarios like data integration, business intelligence, analytics, and more, where coherent and consistent data is a requirement for accurate insights or smooth functioning of applications.

### The many steps of data homogenization

Here's a breakdown of what this process might involve:

1. **Standardization**: This is about making sure data adheres to a common standard. For instance, date values from one system might be in the format `MM-DD-YYYY` while another uses `YYYY-MM-DD`. Standardizing would involve picking one format and converting all dates to that format. See entry for [*standardization*](https://dagster.io/glossary/data-standardization)
2. **Normalization**: This is about scaling numerical data to fall within a specific range. For instance, one system might represent a percentage as a value between 0 and 1 (like 0.85) while another represents it between 0 and 100 (like 85). Normalizing would involve converting all these values to a chosen range. See entry for [*normalization*](https://dagster.io/glossary/data-normalization).
3. **Data Cleansing**: This involves identifying and correcting (or removing) errors and inconsistencies in data to improve its quality. This could involve handling missing data, correcting typos or inaccuracies, and more. See entry for [*cleanse*](https://dagster.io/glossary/data-cleaning).
4. **Schema Mapping**: If you're integrating data from multiple databases, each with its schema, you might need to map fields between them. For example, one database might have a field called "mobile\_number", and another has "cell\_phone". Homogenization would involve deciding on a consistent naming convention and structure.  See entry for [*schema mapping*](https://dagster.io/glossary/schema-mapping)
5. **Encoding and Categorization**: Converting categorical data into a common format or encoding, like one-hot encoding, label encoding, etc. See entry for [*encode*](https://dagster.io/glossary/data-encoding).
6. **Addressing Variability**: Sometimes data from different sources might refer to similar things but have slight variability. For instance, company names could be abbreviated in some systems and spelled out in others. Part of homogenization could involve making these consistent. See entry for *integration*.
7. **Time Series Alignment**: If you're dealing with time series data from different sources, they might have different granularities or time zones. Homogenization might involve resampling, aligning, and converting time zones to get consistent time series data. See entry for *time series analysis*.

### Example of data homogenization in Python

Let's look at a scenario where you have to integrate sales data from two e-commerce platforms. Each platform provides data with various inconsistencies. We'll deal with:

1. Different data formats
2. Product categorization differences
3. Currency conversions
4. Data anomalies like outliers and missing values

We'll still use the `pandas` library and also `numpy` for some numerical operations.

```python
import pandas as pd
import numpy as np

# Sample data
data1 = {
    'Date': ['01-01-2023', '01-02-2023', '01-03-2023', '01-04-2023'],
    'Product': ['Smartphone', 'Laptop', 'Tablet', 'Laptop'],
    'Category': ['Electronics', 'Computers', 'Electronics', 'Computers'],
    'Sales': [100, 200, 150, np.nan],
    'Currency': ['USD', 'USD', 'USD', 'USD']
}

data2 = {
    'Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'],
    'Product': ['SPhone', 'Lap', 'Tab', 'Desk'],
    'Category': ['Mobile', 'Laptop', 'Tablet', 'Computer'],
    'Sales': [120, 2500, 160, -5], # Sales in a different currency & has an outlier
    'Currency': ['EUR', 'EUR', 'EUR', 'EUR']
}

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Convert date format in df2 to match df1
df2['Date'] = pd.to_datetime(df2['Date']).dt.strftime('%m-%d-%Y')

# Standardize product names using a mapping dictionary
product_map = {
    'SPhone': 'Smartphone',
    'Lap': 'Laptop',
    'Tab': 'Tablet',
    'Desk': 'Desktop'
}
df2['Product'] = df2['Product'].map(product_map)

# Map categories
category_map = {
    'Mobile': 'Electronics',
    'Laptop': 'Computers',
    'Tablet': 'Electronics',
    'Computer': 'Computers'
}
df2['Category'] = df2['Category'].map(category_map)

# Handling currency conversion
# Let's assume 1 EUR = 1.1 USD for simplicity
currency_conversion = {
    'EUR': 1.1
}
df2['Sales'] = df2.apply(lambda row: row['Sales'] * currency_conversion[row['Currency']] if row['Currency'] in currency_conversion else row['Sales'], axis=1)
df2['Currency'] = 'USD'

# Handle missing values in df1 by forward filling for simplicity
df1['Sales'].fillna(method='ffill', inplace=True)

# Handle outliers (negative sales, which is an anomaly)
df2['Sales'] = df2['Sales'].apply(lambda x: np.nan if x < 0 else x)
df2['Sales'].fillna(method='ffill', inplace=True) # forward fill for simplicity

# Merge dataframes
combined_data = pd.concat([df1, df2]).reset_index(drop=True)

print(combined_data)
```

In this example:

1. We've addressed different date formats.
2. Standardized product names and categories.
3. Converted EUR to USD.
4. Handled missing values using forward fill.
5. Managed sales anomalies by replacing them with NaN and then using forward fill.

Try running this code on your own machine and you will see this result - a homogenized dataset:

```plaintext
         Date     Product     Category   Sales Currency
0  01-01-2023  Smartphone  Electronics   100.0      USD
1  01-02-2023      Laptop    Computers   200.0      USD
2  01-03-2023      Tablet  Electronics   150.0      USD
3  01-04-2023      Laptop    Computers   150.0      USD
4  01-01-2023  Smartphone  Electronics   132.0      USD
5  01-02-2023      Laptop    Computers  2750.0      USD
6  01-03-2023      Tablet  Electronics   176.0      USD
7  01-04-2023     Desktop    Computers   176.0      USD
```

In a more real-world setting, you might leverage external APIs for up-to-date currency conversion rates, use sophisticated imputation methods for missing values, and perhaps integrate statistical methods or machine learning models to handle anomalies more intelligently.

---

## Hybrid Cloud

> An IT architecture that incorporates some degree of workload portability, orchestration, and management across a mix of on-premises data centers, private clouds, and public clouds.

Source: https://dagster.io/glossary/hybrid-cloud

---

## Idempotent

> An operation that produces the same result each time it is performed.

Source: https://dagster.io/glossary/data-idempotency

The concept of idempotency is fundamental in computer science and is especially crucial in data engineering due to the need for reliable and repeatable data operations.

### Definition of idempotent:

An operation is said to be idempotent if performing it multiple times produces the same result as performing it once.

### Importance in Data Engineering:

1. **Data Recovery and Redundancy**: In the world of big data processing, there can be failures - a node in a cluster might crash, or a network hiccup might disrupt data transmission. In such scenarios, if an operation is re-executed (due to retry logic or some failure recovery mechanism), idempotency ensures that the data remains consistent and is not duplicated or corrupted.
2. **Atomicity and Consistency**: Idempotent operations help maintain atomicity and consistency in data operations. When you're updating a dataset, you want to be sure that either all the changes are made or none are. Idempotent operations can be re-run safely without unintended side effects.
3. **Batch Processing**: In batch data processing, if a batch job fails, being able to rerun that job knowing that the operations are idempotent is crucial. It ensures that data is not double counted or wrongly aggregated.
4. **APIs and Data Integration**: In data integration tasks, where data might be ingested from APIs, ensuring idempotency means that if you re-fetch the data (maybe due to a failure or a scheduled update), you won't end up with duplicate or inconsistent records.

### Examples in Data Engineering:

1. **Appending to a Log**: Consider a distributed log like Apache Kafka. If a producer sends a message to a topic and doesn't receive an acknowledgment due to a transient network failure, it might retry sending that message. The system should be designed such that this retry does not result in duplicate records in the log.
2. **Database Writes**: When writing data to a database, especially in distributed systems, ensuring that a write operation can be repeated without side effects can be vital. For instance, using unique constraints or upsert operations where an insert becomes an update if a record with a given key already exists.
3. **Data Transformation**: When applying transformations in systems like Apache Spark, the operations should be such that re-running a failed transformation job on the same data results in the same output.

### The opposite of idempotency:

The opposite of "idempotent" is "non-idempotent." In computing and data operations, if an action or operation is *idempotent*, then applying it multiple times yields the same result as applying it once. Conversely, if an action is *non-idempotent*, applying it multiple times may yield different results than applying it once.

In the context of HTTP methods, for instance:- `GET` is generally considered idempotent. Retrieving a resource multiple times should provide the same result every time, assuming the resource hasn't changed.- `POST` is typically considered non-idempotent. Making a `POST` request multiple times (e.g., to create a new entry in a database) may lead to multiple new entries.

To be clear, non-idempotent doesn't mean that the result will always differ upon repeated applications, but rather that it *can* differ, depending on the operation and circumstances.

### Implementation:

There are multiple ways to achieve idempotency:

1. **Use of Unique Identifiers**: This often involves using some unique identifier for each operation or record. If an operation with a particular ID has been executed, subsequent operations with the same ID will not be re-executed.
2. **State Tracking**: By keeping track of the state of each operation, the system can determine if it needs to execute a particular operation or if it has already been executed.
3. **De-duplication**: In post-processing steps, systems can use de-duplication logic to remove any duplicates that might have arisen due to non-idempotent operations.

In summary, idempotency is a crucial concept in data engineering that ensures consistency and reliability in data processing systems. Achieving idempotency often requires careful design and considerations in the systems' architecture and operations.

### Example of idempotency in Python

Let's use the example of adding a user to a system. If we attempt to add the same user multiple times, an idempotent operation would ensure that after any number of attempts, the user is only added once. This often represents itself in real-world applications such as database upserts, where a record is updated if it exists and inserted if it does not.

Here's a simple example in Python:

```python
class UserManager:
    def __init__(self):
        # Let's use a dictionary to store users by their username
        self.users = {}
    
    def add_user(self, username, details):
        """Idempotent method to add a user."""
        if username not in self.users:
            self.users[username] = details
            return f"User {username} added."
        else:
            return f"User {username} already exists. No action taken."

# Testing the idempotent method:

manager = UserManager()
print(manager.add_user("Alice", {"age": 30, "email": "alice@example.com"}))  # User Alice added.
print(manager.add_user("Alice", {"age": 30, "email": "alice@example.com"}))  # User Alice already exists. No action taken.
```

In this code:- When you first try to add "Alice", she's added to the system.- When you try to add "Alice" again, the system recognizes that she's already in there, and it takes no action, illustrating the idempotency concept. Even after multiple attempts, Alice still exists as a single user in the system.

This simple example will output:

```javascript
User Alice added.
User Alice already exists. No action taken.
```

---

## Impute

> Fill in missing data values with estimated or imputed values to facilitate analysis.

Source: https://dagster.io/glossary/data-imputation

### Imputation definition:

*Imputation* refers to the process of replacing missing values in a dataset with estimated values. This is often necessary when working with real-world datasets that may have missing data due to a variety of reasons such as data entry errors or incomplete data recording.

Imputation can be done using various techniques such as mean imputation, median imputation, mode imputation, and regression imputation, among others. Mean imputation replaces missing values with the mean of the non-missing values in that column. Similarly, median imputation replaces missing values with the median of the non-missing values in that column. Mode imputation replaces missing values with the mode (i.e., most frequent value) of the non-missing values in that column. Regression imputation involves using a regression model to estimate missing values based on the other variables in the dataset.

### Data imputation example using Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

Here's a practical example of mean imputation in Python:

```python
import pandas as pd
import numpy as np

# create a sample dataset with missing values
data = {'A': [1, 2, np.nan, 4, 5],
        'B': [6, np.nan, 8, 9, 10],
        'C': [11, 12, 13, np.nan, 15]}
df = pd.DataFrame(data)

# perform mean imputation on column A
df['A'] = df['A'].fillna(df['A'].mean())

# display the updated dataset
print(df)
```

The code above would yield the following output:

```plaintext
     A     B     C
0  1.0   6.0  11.0
1  2.0   NaN  12.0
2  3.0   8.0  13.0
3  4.0   9.0   NaN
4  5.0  10.0  15.0
```

In this example, we first create a sample dataset with missing values in columns A, B, and C. We then perform mean imputation on column A using the `fillna()` function and the `mean()` method to calculate the mean of the non-missing values in that column. The resulting dataset has the missing value in column A replaced with the mean of the non-missing values in that column.

---

## Index

> Create an optimized data structure for fast search and retrieval.

Source: https://dagster.io/glossary/data-indexing

### Data indexing definition:

Indexing refers to the process of creating an index or a pointer to the location of data within a larger dataset. This allows for faster and more efficient retrieval of specific pieces of data, without the need to search through the entire dataset. Data indexing greatly improves the efficiency of data pipelines, particularly when working with large datasets where searching for specific data points can be compute-heavy.

### Data indexing in Python:

In Python, data can be indexed using various data structures such as lists, dictionaries, and arrays. For example, in a data pipeline, a list of dictionaries might be used to represent a dataset, where each dictionary represents a single data point.

Choosing the appropriate indexing method: There are various indexing methods available in Python, including *hash-based* indexing, *tree-based* indexing, and *inverted* indexing.

- **Optimizing for query performance**: Indexing is typically used to improve query performance. Optimize the indexing strategy to ensure that queries are executed quickly and efficiently. Consider factors like data volume, query frequency, and indexing overhead when optimizing for performance.
- **Balancing indexing overhead and query performance:** Indexing can add overhead to data processing and storage, so it's essential to balance the indexing overhead with the query performance gains. Consider the trade-off between the overhead and performance to ensure that the indexing strategy is optimized.
- **Handling updates and deletes**: Indexing can add complexity to handling data updates and deletes. Ensure that the indexing strategy can handle updates and deletes effectively without affecting query performance or consistency.
- **Considering the impact on storage requirements**: Indexing can significantly impact storage requirements, especially for large datasets. Consider the storage requirements and the available storage resources when deciding on the indexing strategy.
- **Testing and validating the indexing strategy**: It's essential to test and validate the indexing strategy to ensure that it's producing the desired outcome. Use automated testing and validation techniques to ensure that the indexing is accurate and consistent.
- **Ensuring the indexing strategy is scalable**: The indexing strategy should be scalable to handle increasing data volumes and query frequency. Ensure that the indexing method can be scaled horizontally or vertically to meet future requirements.

Your indexing method requires careful consideration of several factors: the data structure, query requirements, and performance characteristics. Queries should be optimized for performance. This said, it's important to balance the indexing overhead with the query performance gains.

**Handling updates and deletes** can add complexity to the indexing strategy, so it's important to ensure that the strategy can handle these effectively without affecting query performance or consistency. Additionally, indexing can significantly impact **storage requirements**, especially for large datasets.

**Testing and validating** the indexing strategy is essential to ensure it produces the desired outcome. **Automated testing and validation** techniques can be used to ensure that the indexing is accurate and consistent. Furthermore, the indexing strategy should be scalable to handle increasing data volumes and query frequency. Therefore, the indexing method should be designed to be scalable horizontally or vertically to meet future requirements.

### Data indexing in Python

Let's now look at a simple, then more advanced example of indexing data in Python.

#### A basic data indexing example using Python:

Here is an example of how data indexing can be used in Python to retrieve specific data points from a list of dictionaries:

```python
# Define a list of dictionaries representing a dataset
dataset = [
    {'name': 'John', 'age': 25, 'city': 'New York'},
    {'name': 'Emily', 'age': 32, 'city': 'Los Angeles'},
    {'name': 'David', 'age': 19, 'city': 'Chicago'},
    {'name': 'Jessica', 'age': 28, 'city': 'San Francisco'},
]

# Create an index of the dataset based on the 'name' key
index = {data['name']: data for data in dataset}

# Retrieve data for a specific name
name = 'John'
data = index.get(name)
print(data)
```

Will yield this output:

```plaintext
{'name': 'John', 'age': 25, 'city': 'New York'}
```

#### A more sophisticated example using multi-level indexing with Pandas

Now let's consider a more advanced example that involves **multi-level indexing** and data operations using the `pandas` library in Python. This approach of multi-level indexing combined with pandas slicing operations can be extremely powerful for quickly analyzing large datasets.

This example assumes that you're familiar with the basics of pandas. You will find [installation instructions here](https://dagster.io/glossary).

Imagine we have a dataset that contains sales data of a retail chain across various cities and stores within those cities. The data includes the product name, the quantity sold, and the sale date. We'd like to analyze the sales data to answer various queries.

In this example, we will:1. Create a mock dataset for sales across various cities and stores.2. Convert the 'Date' column to a datetime datatype for more efficient operations.3. Set a multi-level index using the 'City', 'Store', and 'Date' columns.4. Sort the index for efficient slicing.5. Perform various querying operations to extract specific insights from the dataset.

The `.loc[]` accessor in pandas is used primarily for label-based indexing, which means you're typically using the actual value of the index or the column names to select specific data, but it also supports boolean indexing.

```python
import pandas as pd

# Create a mock dataset
data = {
    'City': ['New York', 'New York', 'New York', 'Los Angeles', 'Los Angeles', 'Los Angeles', 'Chicago', 'Chicago'],
    'Store': ['Store A', 'Store B', 'Store A', 'Store C', 'Store C', 'Store D', 'Store E', 'Store E'],
    'Product': ['Apple', 'Android', 'Apple', 'Apple', 'Android', 'Apple', 'Android', 'Apple'],
    'Quantity': [100, 150, 200, 50, 40, 75, 120, 60],
    'Date': ['2023-09-10', '2023-09-10', '2023-09-11', '2023-09-10', '2023-09-12', '2023-09-13', '2023-09-12', '2023-09-13']
}

df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
df.set_index(['City', 'Store', 'Date'], inplace=True)
df.sort_index(inplace=True)
print(df)

# Query 1
print("\nSales data from New York at Store A:")
print(df.loc[('New York', 'Store A')])

# Query 2
print("\nApple sales in Los Angeles on 2023-09-10:")
apple_sales_in_LA = df.loc[('Los Angeles', slice(None), '2023-09-10')]
apple_sales_value = apple_sales_in_LA[apple_sales_in_LA['Product'] == 'Apple']['Quantity'].sum()
print(apple_sales_value)

# Query 3
print("\nTotal sales in Chicago:")
print(df.loc['Chicago', 'Quantity'].sum())

# Query 4
print("\nSales data from Store C on 2023-09-10:")
print(df.loc[(slice(None), 'Store C', '2023-09-10')])

# Query 5
print("\nSales data after 2023-01-11:")
print(df.loc[pd.IndexSlice[:, :, '2023-01-12':]])
```

Multi-level indexing in pandas can be tricky, but it's a powerful tool once you get the hang of it. Note that in Query #2 we separate out the multi-index slice and the column-based condition, making the operation more straightforward. When using multi-indexing combined with a condition, you'll want to be careful about how you filter the data.

The output of this code will be:

```plaintext
                                Product  Quantity
City        Store   Date                         
Chicago     Store E 2023-09-12  Android       120
                    2023-09-13    Apple        60
Los Angeles Store C 2023-09-10    Apple        50
                    2023-09-12  Android        40
            Store D 2023-09-13    Apple        75
New York    Store A 2023-09-10    Apple       100
                    2023-09-11    Apple       200
            Store B 2023-09-10  Android       150

Sales data from New York at Store A:
           Product  Quantity
Date                        
2023-09-10   Apple       100
2023-09-11   Apple       200

Apple sales in Los Angeles on 2023-09-10:
50

Total sales in Chicago:
180

Sales data from Store C on 2023-09-10:
            Product  Quantity
City                         
Los Angeles   Apple        50

Sales data after 2023-01-11:
                                Product  Quantity
City        Store   Date                         
Chicago     Store E 2023-09-12  Android       120
                    2023-09-13    Apple        60
Los Angeles Store C 2023-09-10    Apple        50
                    2023-09-12  Android        40
            Store D 2023-09-13    Apple        75
New York    Store A 2023-09-10    Apple       100
                    2023-09-11    Apple       200
            Store B 2023-09-10  Android       150
```

---

## Ingest

> The initial collection and import of data from various sources into your processing environment.

Source: https://dagster.io/glossary/data-ingestion

In this Glossary entry:

- [Data ingestion definition](#)
- [Data ingestion: Build or Buy](#)
- [Data ingestion with Dagster](#)
- [A Python example of data ingestion](#)

### Data ingestion definition:

Data ingestion refers to the initial collection and The process of data ingestion can involve both batch and real-time ingestion. **Batch ingestion** involves importing data in large, scheduled groups, while **real-time ingestion**, often used in time-sensitive scenarios, involves importing data immediately as it is generated.

In designing your data pipelines, data ingestion is an important first step as it provides the raw materials for subsequent steps such as data transformation, storage, and analysis. It needs to be robust and efficient to ensure the timely availability of data for further processing.

The goal of data ingestion in data engineering is to facilitate seamless data integration and ensure a reliable flow of data from source systems to destinations, such as a data lake or data warehouse, where it can be harnessed to derive business insights, predictive analytics, or drive data-driven decision-making.

But there are also costs to running ingestion jobs, so you need to map this out carefully.  In designing your data pipeline, you will need to make some compromises between frequency of data updates and cost of operating the data platform.

### Data ingestion: build or buy?

Since the early days of the so-called Modern Data Stack, commercial services have emerged to allow for low/no-code data ingestion services.  These systems provide integrations with popular data sources and data destinations.  Some only offer proprietary integrations, while other support running open-source or custom integrations.

Popular services for traditional SaaS analytics include [Matillion](https://www.matillion.com/), [Airbyte](https://airbyte.com/), [Fivetran](https://www.fivetran.com/), [improvado](https://improvado.io/), [Stitch](https://www.stitchdata.com/), [Meltano](https://meltano.com/), and many others.

Ingestion of streaming data is more specialized, and includes services such as [Amazon Kinesis](https://aws.amazon.com/kinesis/), [Apache Kafka](https://kafka.apache.org/), [Confluent (hosted Kafka)](https://www.confluent.io/), [Streamsets](https://streamsets.com/), [GCP Dataflow](https://cloud.google.com/dataflow)

Designing your own data ingestion process vs. using a commercial ingestion service each come with their own benefits. The right choice often depends on your specific requirements, such as the complexity of your data sources and your need to customize the ingestion, the frequency of updates, the criticality of the data, your team's technical expertise, budget constraints, and other factors.  For less common data sources, you might actually find that commercial ingestion services simply don't support your source or support it poorly (although services like [Portable.io](https://portable.io/) specialize in 'rare' data sources).

**Benefits of Designing Your Own Ingestion Process:**

1. **Customization**: You can build a solution perfectly tailored to your specific needs, including handling unique data sources, custom transformation logic, or special security requirements.
2. **Cost Control**: Building your own ingestion process could potentially save costs in the long term, especially if your data volumes are high and commercial service costs are proportional to the data processed (as many are).
3. **Keep the knowledge in-house**: It provides your team with an opportunity to deeply understand the data ingestion process and the architecture of the data pipeline, which could be beneficial in troubleshooting and future enhancements.
4. **No Vendor Lock-in**: You're not tied to the technologies, pricing changes, upgrade cycles, or terms of a third-party provider.
5. **Data sovereignty**: In some cases, security requirements prevent you from using a 3rd party data processor.

**Benefits of Using a Commercial Ingestion Service:**

1. **Speed and Ease of Deployment**: Commercial services usually offer plug-and-play solutions that can quickly be set up, reducing time to value.  They typically offer free historical backfills, which is handy.
2. **Expertise and Best Practices**: These services are built by teams of experts who incorporate industry best practices, which may translate to more efficient and reliable data ingestion.
3. **Scalability and Performance**: Commercial solutions often provide built-in mechanisms for handling large amounts of data, ensuring high availability and performance.
4. **Support and Maintenance**: Vendors generally provide ongoing support and handle maintenance, upgrades, and security, which can significantly reduce the burden on your team as well as avoid fire drills.  This said many customers remain frustrated at the slow pace of development as vendors try to support a very large library of integrations.
5. **Focus on Core Competencies**: By using a commercial service, your team can focus on other aspects of data processing and analysis rather than spending time on the complexities of data ingestion.

In general, teams will try these data ingestion services using their free trial options, and see if the off-the-shelf integrations will meet the specifics of their use case.  Most large data platforms may lean on different ingestion services depending on the quality of their integration.  It's not unusual to find that a commercial ingestion tool hits a snag if your data has some unexpected values, such as nested JSON objects or uncommon character encoding.

### Data ingestion with Dagster

Python is the most popular programming language for data ingestion solutions.  For example, the [Singer standard](https://www.singer.io/) (a standard for developing ingestion plug-ins) favors Python.  So naturally, writing an ingestion step into your pipeline using Dagster is fairly trivial. Dagster provides [integrations](https://dagster.io/integrations) for several ingestion solutions like [Airbyte](https://dagster.io/integrations/dagster-airbyte), [Fivetran](https://dagster.io/integrations/dagster-fivetran), or [Meltano](https://dagster.io/integrations/dagster-meltano).

You will find several examples of data ingestion in our tutorials, such as [ingesting data from the Bureau of Labor Statistics](https://dagster.io/blog/dagster-google-sheets-tutorial#our-first-data-asset-from-api-call-to-pandas-dataframe), from [the GitHub API](https://github.com/dagster-io/fake-star-detector#trying-the-simpler-model-using-data-from-the-github-api), or [your local ski resort](https://dagster.io/blog)!

### A Python example of data ingestion

In this example, we'll use Python's `requests` module to call a public API, specifically the `JSONPlaceholder` API, which is a simple fake REST API for testing and prototyping. We'll then parse the returned JSON data into a pandas DataFrame for further analysis. `JSONPlaceholder` doesn't require any authentication, so you can run this code as it is.

```python
import requests
import pandas as pd

def ingest_data(url):
    try:
        # Send a GET request to the API
        response = requests.get(url)

        # Raise an exception if the request was unsuccessful
        response.raise_for_status()

    except requests.RequestException as e:
        print(f"Request failed: {e}")
        return None

    else:
        # If the request was successful, load the data into a DataFrame
        data = pd.json_normalize(response.json())
        print("Data ingestion successful. Here are the first few rows of your data:")
        print(data.head())

        return data

# Define the data source
url = 'https://jsonplaceholder.typicode.com/posts'

# Ingest data
df = ingest_data(url)
```

In this script, we send a `GET` request to the "/posts" endpoint of the `JSONPlaceholder` API. The API returns a list of posts, each represented as a JSON object. We then use `pandas.json_normalize` to flatten these JSON objects and convert them into a tabular format.

Here is the output produced:

```plaintext
Data ingestion successful. Here are the first few rows of your data:
   userId  id                                              title                                               body
0       1   1  sunt aut facere repellat provident occaecati e...  quia et suscipit\nsuscipit recusandae consequu...
1       1   2                                       qui est esse  est rerum tempore vitae\nsequi sint nihil repr...
2       1   3  ea molestias quasi exercitationem repellat qui...  et iusto sed quo iure\nvoluptatem occaecati om...
3       1   4                               eum et est occaecati  ullam et saepe reiciendis voluptatem adipisci\...
4       1   5                                 nesciunt quas odio  repudiandae veniam quaerat sunt sed\nalias aut...
```

Again, remember that in a real-world scenario, data ingestion might involve more complexity, such as error handling, data validation, and transformation steps. In this simple example, we're assuming that the API is available, that it returns data in the expected format, and that there are no network errors - assumptions that might not hold in a production environment.

Such error handling, scheduling, and staying on top of changing APIs are some of the top reasons that teams will adopt third-party data ingestion services.

---

## Interactive Query

> A query mechanism allowing users to ask spontaneous questions and receive rapid responses, used in analyzing datasets.

Source: https://dagster.io/glossary/interactive-query

---

## Interpolate

> Use known data values to estimate unknown data values.

Source: https://dagster.io/glossary/data-interpolation

### Interpolation definition

**Data Interpolation** is a statistical technique used to estimate or predict unknown values within a known range of data points, based on existing data. Interpolation assumes that there is a consistent trend or pattern between the known data points and uses this to fill gaps in the data. Its accuracy relies on the assumption that the trend between existing points extends to unknown points.

### An example of data interpolation in Python

Interpolation is often used in data engineering to fill missing values or to make predictions within the range of existing data. A common method for doing this is using *polynomial interpolation*.

Let's consider an example where we use numpy's `polyfit` function to fit a polynomial to some data, then use this polynomial to interpolate some values.

```python
import numpy as np
import matplotlib.pyplot as plt

# Set a random seed for reproducibility
np.random.seed(42)

# Define the true function
def f(x):
    return np.sin(2 * np.pi * x)

# Generate some synthetic data
X = np.sort(np.random.rand(10))
y = f(X) + np.random.normal(scale=0.3, size=10)

# Fit a polynomial of degree 3 to the data
coefficients = np.polyfit(X, y, 3)
polynomial = np.poly1d(coefficients)

# Generate points for interpolation
X_interp = np.linspace(0, 1, 100)
y_interp = polynomial(X_interp)

# Plot the original data
plt.scatter(X, y, label='Original data', color='blue')

# Plot the interpolated data
plt.plot(X_interp, y_interp, label='Interpolation', color='red')

# Set title and legend
plt.title('Polynomial Interpolation')
plt.legend()

# Show the plot
plt.show()
```

Here we first define a function `f(x)` which is the true underlying function that we are trying to learn. We then generate some data by sampling `x` values randomly, calculating the corresponding `y` values using the function, and adding some random noise.

Next, we fit a 3rd degree polynomial to the data using `np.polyfit`, and use this polynomial to interpolate `y` values at 100 points evenly spaced between 0 and 1.

The resulting plot shows the original data as blue points and the interpolated values as a red line. As you can see, the interpolated values fit the original data well, suggesting that the polynomial is a good model of the underlying function.

![](https://dagster.io/images/glossary/681399f654933b29e12fbc45_interpolate-1.png)

---

## Key-Value Store

> A type of NoSQL database that uses a simple key/value method to store data, suitable for storing large amounts of data.

Source: https://dagster.io/glossary/key-value-store

---

## Lineage

> Understand how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.

Source: https://dagster.io/glossary/data-lineage

### Data Lineage definition:

Data lineage refers to the visualization and understanding of how data moves through an ecosystem, including its origin, transformations, dependencies, and ultimate consumption. Effective data lineage provides a clear and comprehensive view of where data comes from, how it moves and changes, and where it ends up within a system or workflow.

Lineage is a critical concept in data engineering, data analysis, and data governance.

Here's a breakdown of the concept:

1. **Origin or Source**: Where does the data come from? This could be databases, external systems, flat files, APIs, or other sources.
2. **Transformations**: As data moves from its origin to its final destination, it might go through various transformations. This could be an ETL (Extract, Transform, Load) processes, data cleaning operations, or any other type of manipulation a data engineer or data scientist might define.
3. **Dependencies**: Understanding what datasets, processes, or tools depend on each other is key. If a source changes or has an issue, it's vital to know what downstream processes or datasets might be affected.
4. **Consumption**: This refers to the final resting place of the data or where it gets consumed. It might end up in a data warehouse, a report, a machine learning model, or any other type of application or system.

### Why is data lineage important?

1. **Trust & Data Quality**: Understanding the full path of data helps ensure data quality and build trust in the data. If there's an anomaly in a report, you can trace back to see where the data came from and how it was transformed.
2. **Impact Analysis**: If a system or process needs to change, data lineage can show what downstream processes or datasets might be affected.
3. **Compliance & Audit**: Many industries have regulatory requirements around data. Knowing the full lineage of data can help ensure compliance, as you can demonstrate where data comes from and how it's used.
4. **Optimization & Debugging**: Having a clear understanding of data lineage speeds up troubleshooting.
5. **Knowledge Sharing**: For large organizations, not everyone might be aware of all data processes. You might have data scientists, data engineers, software engineers and data architects all collaborating on building data pipelines. Data lineage provides a clear map for everyone to understand the data flow, and hels answer pointed questions from data consumers.

To sum this up, data lineage is all about transparency, trust, and understanding in a data-driven ecosystem. Given the increasing complexity of modern data pipelines and the critical importance of data in decision-making, maintaining clear lineage is essential for most organizations and data teams.

### Data lineage in Machine Learning

Lineage plays a significant role in machine learning (ML). Given that machine learning models are data-driven, understanding where data comes from, how it's processed, and how it's used is crucial for both model development and deployment. Here's an exploration of the role of data lineage in machine learning:

1. **Reproducibility**: One of the data engineering challenges in ML is ensuring that results (model training, evaluations, predictions) can be reproduced. Knowing the lineage of the data used to train and test a model provides clear documentation of the data's origins and the transformations it underwent, which is essential for reproducing results.
2. **Model Interpretability**: If an ML model produces unexpected or erroneous results, understanding lineage can help pinpoint potential issues in the data sourcing or preprocessing stages that might be contributing to the problem.
3. **Model Auditing**: For regulatory and compliance purposes, organizations might need to audit ML models, especially in critical domains like finance or healthcare. Data lineage provides a trail of evidence showing where the model's data originated and how it was processed, aiding in transparency and accountability.
4. **Data Quality Assurance**: Poor data quality can significantly degrade the performance of ML models. Tracking data lineage allows teams to trace back and identify sources of noise, missing values, or anomalies that might affect model quality.
5. **Feature Engineering**: Features, derived from raw data, play a crucial role in ML. Understanding the lineage of features—how they were derived, transformed, and encoded—is essential for model tuning and optimization.
6. **Model Versioning**: Just as code versioning is crucial in software development, model versioning is essential in ML, and lineage is a significant part of this. If you retrain a model on new data or with different preprocessing, tracking lineage can help you understand differences between model versions.
7. **Collaboration & Knowledge Transfer**: ML teams often comprise multiple roles - data engineers, data scientists, ML engineers, and domain experts. A clear understanding of data lineage ensures that all team members have a shared understanding of the data, its sources, and its transformations.
8. **Operational Monitoring**: After deploying an ML model, it's crucial to monitor its performance. If the model starts to drift or produce unexpected results, data lineage can help teams quickly determine if changes or anomalies in input data might be the cause.
9. **Bias & Fairness**: Ensuring models are fair and unbiased is crucial. Successful ML engineering means tracing potential sources of bias in the data collection or preprocessing stages.

Data lineage in computer science provides transparency, traceability, and accountability. As ML models become increasingly integrated into critical applications, the need for clear documentation and understanding of data sources and transformations grows more vital. Data lineage tools and practices help meet this need.

Read more about [orchestrating Machine Learning pipelines in Dagster](https://dagster.io/blog/dagster-ml-pipelines).

### Data Lineage Tools & Technologies:

Many data governance and data management tools offer data lineage features. These tools can automatically track and visualize data lineage, helping data engineers and architects understand complex data flows. Dagster builds data lineage directly into the orchestration process thanks to [its unique asset-centric approach](https://dagster.io/blog/software-defined-assets).

### Coding example of data lineage in Python

Data lineage is a hard concept to capture in a single coding example, but we can illustrate the idea, even though real-world data engineering jobs involve more complexity and typically employ specialized tools.

Let's consider a data pipeline with multiple data sources and data processing stages. We'll use Python classes to encapsulate these stages and maintain lineage metadata along the way. We will mock a data warehouse and an API.

Here's a simple mock-up of a data pipeline that tracks lineage:

```python
class DataSource:
    def __init__(self, name, data):
        self.name = name
        self.data = data
        self.lineage = [f"Source: {name}"]

    def get_data(self, user_id=None):
        if user_id is None:
            return self.data, self.lineage
        return [record for record in self.data if record['user_id'] == user_id][0], self.lineage


class Transformation:
    def __init__(self, name, function):
        self.name = name
        self.function = function

    def apply(self, data, lineage):
        transformed_data = self.function(data)
        lineage.append(f"Transformed by: {self.name}")
        return transformed_data, lineage


class DataSink:
    def __init__(self, name):
        self.name = name

    def save(self, data, lineage):
        # For simplicity, we're just printing here.
        print(f"Data saved to {self.name}: {data}")
        print("Lineage:")
        for step in lineage:
            print(" -> ", step)


# Mock multiple data entries for Database_A
data_entries_db_a = [
    {"user_id": 1, "name": "Thom Yorke", "use_case": "lead singer"},
    {"user_id": 2, "name": "Jonny Greenwood", "use_case": "guitarist"},
    {"user_id": 3, "name": "Ed O'Brien", "role": "guitarist"  },
    {"user_id": 4, "name": "Philip Selway", "role": "drums"},
    {"user_id": 5, "name": "Colin Greenwood", "role": "bass"}
]

# Mock multiple data entries for API call
data_entries_api_b = [
    {"user_id": 1, "born": "1968"},
    {"user_id": 2, "born": "1971"},
    {"user_id": 3, "born": "1968"},
    {"user_id": 4, "born": "1967"},
    {"user_id": 5, "born": "1969"}
]

source1 = DataSource("Database_A", data_entries_db_a)
source2 = DataSource("API_B", data_entries_api_b)

transform1 = Transformation("Merge Data", lambda data: {**data[0], **data[1]})
transform2 = Transformation("Add Status", lambda data: {**data, "status": "processed"})

sink = DataSink("Warehouse_X")


def pipeline():
    # Process each entry from Database_A
    for data1_entry in source1.data:
        user_id = data1_entry["user_id"]
        data1, lineage1 = data1_entry, source1.lineage
        data2, lineage2 = source2.get_data(user_id)

        merged_data, merged_lineage = transform1.apply([data1, data2], lineage1 + lineage2)
        processed_data, final_lineage = transform2.apply(merged_data, merged_lineage)
        sink.save(processed_data, final_lineage)
        print("-----------------------")  # Separator for clarity


pipeline()
```

In this example:

1. We've structured `DataSource`, `Transformation`, and `DataSink` classes to maintain lineage information.
2. Each time data is pulled or transformed, its lineage is updated with relevant metadata.
3. The pipeline combines data from two sources, applies two transformations, and then saves the data. The lineage is printed, showing the journey of the data.

The output of our example would be:

```plaintext
Data saved to Warehouse_X: {'user_id': 1, 'name': 'Thom Yorke', 'use_case': 'lead singer', 'born': '1968', 'status': 'processed'}
Lineage:
 ->  Source: Database_A
 ->  Source: API_B
 ->  Transformed by: Merge Data
 ->  Transformed by: Add Status
-----------------------
Data saved to Warehouse_X: {'user_id': 2, 'name': 'Jonny Greenwood', 'use_case': 'guitarist', 'born': '1971', 'status': 'processed'}
Lineage:
 ->  Source: Database_A
 ->  Source: API_B
 ->  Transformed by: Merge Data
 ->  Transformed by: Add Status
-----------------------
[...]
```

Naturally, real-world data pipelines would likely be more complex, involve permanent data storage, and might not involve Radiohead band members. Advanced data engineers would typically employ dedicated tools for data modeling and analyzing data, especially in larger data ecosystems with cloud computing.

A solution like Dagster naturally tracks lineage for you and provides the tools for data engineers to rapidly observe, track and debug data pipelines.  Furthermore, data lineage is directly related to the set of dependencies that exist within data pipelines, and Dagster's built-in tracking of dependencies allows data engineers, software engineers, and data architects to tap into these when building data pipelines.

We hope this overview helps you in building your data engineering skills.  Mapping data provenance will help manage your data processing systems, support data analysts, maintain solid data architecture, and keep track of sources like data warehouses and data lakes.

---

## Linearizability

> Ensure that each individual operation on a distributed system appear to occur instantaneously.

Source: https://dagster.io/glossary/linearizability

#### Linearizability definition:

Linearizability is a correctness condition that ensures that all operations on a distributed system (such as reads, writes, and updates to a data store) appear to occur instantaneously at some point between their start and end times. It constrains what outputs are possible when an object is accessed by multiple processes concurrently. This means that if one operation finishes before another operation starts, the first operation must appear to happen before the second in the system's global order of operations.

As a concept, Linearizability is used in the field of distributed systems and data engineering as it pertains to the consistency of operations in a distributed data store. It is a specific form of **consistency model** that aims to simplify the understanding and reasoning about the behavior of distributed systems.

Linearizability is *not* related to the concept of [linearizing data](https://data-linearizing).

##### Key Properties of Linearizability

- **Atomicity**: Each operation is atomic, meaning it either happens entirely or not at all. There's no partial visibility of operations to the clients.
- **Ordering**: Operations are ordered in a way that respects real-time. If operation A completes before operation B starts, then operation A must appear to have happened before operation B in the system's history.
- **Consistency**: The system's state after any sequence of operations must be the same as if the operations had been executed in some sequential order that is consistent with the real-time ordering of the operations.

##### Importance of Linearizability in Distributed Systems

Linearizability is crucial for distributed systems because it provides a straightforward and intuitive way of understanding the system's behavior. When a system is linearizable, developers and users can reason about its state and operations as if they were dealing with a single, atomic system, even though the operations may be distributed across multiple nodes and may involve complex coordination.

##### Examples and Applications of Linearizability

- **Distributed databases**: Ensuring that all replicas of a piece of data reflect the same sequence of updates, even in the presence of network partitions and failures.
- **Concurrent data structures**: Like concurrent queues or maps in multi-threaded programming, where operations by different threads must reflect a single, coherent order of execution.

##### Challenges and Trade-offs

Implementing linearizability in distributed systems comes with its challenges, primarily related to performance and availability. Ensuring strict ordering and atomicity of operations can lead to increased latency, as operations might need to wait for coordination among nodes. Additionally, in the presence of network partitions or node failures, maintaining linearizability can lead to decreased availability since the system might need to block operations until it can guarantee their order and atomicity.

In practice, systems often have to balance between strict linearizability and other considerations like availability and partition tolerance (as per the [CAP theorem](https://www.scylladb.com/glossary/cap-theorem/)), leading to various consistency models such as eventual consistency, causal consistency, and others that relax some constraints to achieve better performance or availability under certain conditions.

#### Linearizability and Data Orchestration

Data orchestration handles the complexities of dependencies, scheduling, and the execution of tasks across distributed architectures.

Therefore, the relationship between linearizability and data orchestration in a distributed system is foundational to ensuring that data remains consistent and that workflows execute reliably. Since linearizability provides a guarantee that operations on data are executed in a manner that respects the chronological order of events, it is crucial for data orchestration, which relies on the predictable and consistent state of data to manage dependencies between tasks effectively.

For instance, in a data pipeline that involves reading data from a distributed database, transforming it, and then writing it back to another store, linearizability ensures that the read operations see the most recent writes in the correct order. This consistency is vital for the orchestration layer to correctly sequence tasks, manage dependencies, and ensure that each step of the pipeline operates on the correct version of the data.

### An example of linearizability in Python

Linearizability is a difficult concept to capture outside of a distributed system.  This said, to illustrate the concept using just a simple Python example, let's simulate a basic scenario involving a bank account with threaded operations like deposit and withdrawal.

In a linearizable system, if these operations are performed in a certain order, their effects should appear in that same order, regardless of any delays or the distribution of these operations across different threads or processes.

We'll create a simple `BankAccount` class with thread-safe deposit and withdrawal methods, ensuring that if one operation completes before another starts, its effect is visible in the order of operations. We'll use threading to simulate concurrent operations and locks to ensure atomicity and maintain linearizability.

Artificial delays were added to the deposit and withdrawal operations to simulate the real-world latency that can occur in a distributed system. These delays help illustrate the importance of linearizability, as the system still ensures that all operations are processed in a consistent and orderly manner, despite the added complexity of concurrent operations and timing differences.

```python
import threading
import random
import time


class BankAccount:
    def __init__(self):
        self.balance = 0
        self.lock = threading.Lock()

    def deposit(self, amount):
        with self.lock:  # Ensuring atomicity and order
            # Simulate a delay
            time.sleep(random.uniform(0.01, 0.1))
            self.balance += amount
            print(f"Deposited €{amount}, New Balance: {'€{:,.2f}'.format(self.balance)}")

    def withdraw(self, amount):
        with self.lock:  # Ensuring atomicity and order
            # Simulate a delay
            time.sleep(random.uniform(0.01, 0.1))
            if self.balance >= amount:
                self.balance -= amount
                print(f"Withdrew €{amount}, New Balance: {'€{:,.2f}'.format(self.balance)}")
            else:
                print(f"Insufficient funds for withdrawal of {amount}")


def perform_transactions(account):
    for _ in range(10):
        action = random.choice(['deposit', 'withdraw'])
        amount = round(random.randint(1, 100000)/100,2)
        if action == 'deposit':
            account.deposit(amount)
        else:
            account.withdraw(amount)


## Create a bank account then fund it
account = BankAccount()
account.balance = 1000

## Simulate concurrent deposits and withdrawals with random amounts and delays
threads = [threading.Thread(target=perform_transactions, args=(account,)) for _ in range(10)]

for thread in threads:
    thread.start()

for thread in threads:
    thread.join()

final_balance= "€{:,.2f}".format(account.balance)
print(f"Final Balance: {final_balance}")
```

Our example will run for a while, executing transactions with random amounts, each subjected to a simulated network delay:

```plaintext
...
Deposited €178.81, New Balance: €5,075.31
Deposited €828.37, New Balance: €5,903.68
Withdrew €164.68, New Balance: €5,739.00
Withdrew €203.17, New Balance: €5,535.83
Withdrew €638.13, New Balance: €4,897.70
Deposited €456.11, New Balance: €5,353.81
Withdrew €281.57, New Balance: €5,072.24
Withdrew €480.37, New Balance: €4,591.87
Final Balance: €4,591.87
```

This example illustrates the three key aspects of linearizability meantioned earlier:

- **Atomicity**: Each deposit and withdrawal operation is atomic, meaning it's treated as a single, indivisible action thanks to the lock. This ensures that the operation either fully completes or doesn't happen at all if it's interrupted.
- **Ordering**: The lock also guarantees that operations are processed in the order they are received. If two threads try to perform operations at the same time, the lock forces one to wait until the other is finished, maintaining a sequential order of effects on the balance.
- **Consistency**: The final state of the bank account (its balance) reflects the total sequence of operations performed on it, as if they had been executed one after another, in some order, without interference from other operations.

This simple Python example models a linearizable system where the sequence and effects of operations (deposits and withdrawals) are predictable and consistent, mirroring the key principles of linearizability in distributed systems and data engineering.

---

## Linearize

> Transforming the relationship between variables to make datasets approximately linear.

Source: https://dagster.io/glossary/data-linearizing

### Data linearizing - a definition

Linearizing data is a process where the relationship between variables in a dataset is transformed or adjusted to make it linear or approximately linear. This transformation is often done in data engineering to simplify analysis, improve model performance, or meet assumptions of certain statistical methods.

Methods for linearizing data can vary depending on the nature of the dataset and the specific goals of the analysis. Common techniques include logarithmic transformations, exponential transformations, polynomial transformations, and [Box-Cox transformations](https://www.statisticshowto.com/probability-and-statistics/normal-distributions/box-cox-transformation/). Additionally, feature engineering techniques can be employed to create new linear combinations of variables that better capture the underlying relationships in the data.

### When to use data linearizing in data engineering

Linearizing data can be useful in data engineering for several reasons:

**Data Modeling**: Many statistical and ML models assume a linear relationship between variables. By linearizing the data, we can make these models more applicable and effective.  And example here is [ANOVA (Analysis of Variance)](https://www.investopedia.com/terms/a/anova.asp): ANOVA tests for significant differences in the means of two or more groups. In its simplest form (one-way ANOVA), it assumes a linear relationship between a categorical independent variable (factor) and a continuous dependent variable.

**Normalization**: Linearizing data can also involve [normalizing it](https://data-normalization), which makes the data comparable and easier to work with across different scales. Normalization can involve techniques such as scaling variables to have a mean of zero and a standard deviation of one.

**Error Reduction**: In some cases, linearizing data can help reduce errors or biases in the analysis. For example, when dealing with non-linear relationships, errors might be larger in certain parts of the dataset. Linearizing the data can help mitigate this issue.

**Interpretability**: Linear relationships are often easier to interpret than non-linear ones. By linearizing the data, we can make the relationships between variables more transparent and understandable.

**Assumptions of Analysis Techniques**: Certain statistical techniques, such as linear regression, assume that the relationship between variables is linear. Linearizing the data ensures that these assumptions are met, thereby improving the validity of the analysis.

### An example of data linearizing in Python

To demonstrate the concept of linearizing data, we will use a common non-linear dataset that follows a power law relationship and then apply a linear transformation to linearize it. This process is often used in data analysis to apply linear regression techniques on datasets that do not initially exhibit a linear relationship.

Consider a dataset that follows the equation `y = ax^b`, where `a` and `b` are constants. This is a common form of a power law relationship. To linearize this data, we can apply a logarithmic transformation to both sides of the equation, resulting in a linear relationship that can be analyzed with linear regression techniques.

The transformed equation becomes `log(y) = log(a) + blog(x)`.

Here is a step-by-step Python example:

1. Generate a synthetic dataset that follows a power law relationship.
2. Plot the original dataset to show its non-linear nature.
3. Apply a logarithmic transformation to both `x` and `y` to linearize the data.
4. Plot the transformed (linearized) dataset.
5. Perform a linear regression on the transformed data as a demonstration of how linearization allows for linear modeling techniques to be applied to non-linear data.

The dataset includes random noise, simulating data variability. The plots and linear regression analysis demonstrate how the logarithmic transformation still effectively linearizes the data, allowing for linear regression to be applied. The added variability makes the data analysis scenario more representative of real-world data challenges.

Noise is added as a small fraction of the `y_power_law` value to minimize the risk of `y` becoming non-positive. This approach should prevent the occurrence of `NaN` values during the logarithmic transformation and allow the linear regression analysis to proceed without errors.

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run this code.*

Running this code will output a plot along these lines:

![](https://dagster.io/images/glossary/681399f654933b29e12fbc2f_linearize-plot.png)

This example demonstrates how to linearize non-linear data to analyze it using linear methods. The first plot (blue) shows the original non-linear relationship, while the second plot (red) shows the data after applying a logarithmic transformation, making it linear. The linear regression model is then fitted to the transformed data, demonstrating the effectiveness of linearization in analyzing non-linear relationships.

---

## Mask

> Obfuscate sensitive data to protect its privacy and security.

Source: https://dagster.io/glossary/data-masking

### Data masking definition:

Data masking is the process of obscuring or masking specific data elements within a dataset to protect sensitive information. This is commonly used in situations where the data is being shared or used for analysis, but certain elements of the data need to remain confidential.

The importance of data masking is to ensure that sensitive information is not exposed to unauthorized users, while still allowing the use of the data for various purposes.

There are several techniques for data masking, including:

- Substitution: replacing sensitive data with a different value, such as a pseudonym or a hash value.
- Shuffling: randomizing the order of sensitive data within a dataset.
- Truncation: removing a portion of sensitive data, such as only showing the first few digits of a social security number.
- Noise addition: adding random noise to sensitive data to make it harder to identify.

### Data masking example using Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

Here is an example of data masking using Python:

Given the input file `ss_numbers.csv`:

```plaintext trailing-newline
| first\_name | last\_name | social\_security\_number | email                                                     |
| ----------- | ---------- | ------------------------ | --------------------------------------------------------- |
| Aisha       | Khan       | 555221111                | [aisha.khan@company.com](mailto:aisha.khan@company.com)   |
| Mia         | Tran       | 555332222                | [mia.tran@company.com](mailto:mia.tran@company.com)       |
| Jasmine     | Lee        | 555443333                | [jasmine.lee@company.com](mailto:jasmine.lee@company.com) |
| Rohan       | Gupta      | 555554444                | [rohan.gupta@company.com](mailto:rohan.gupta@company.com) |
| Imran       | Khan       | 555198888                | [imran.khan@company.com](mailto:imran.khan@company.com)   |
| Aisha       | Shah       | 555209999                | [aisha.shah@company.com](mailto:aisha.shah@company.com)   |
| Jalen       | Davis      | 555210000                | [jalen.davis@company.com](mailto:jalen.davis@company.com) |
```

We can mask the data using:

```python
import pandas as pd
import numpy as np

# load data
df = pd.read_csv('ss_numbers.csv')

# mask sensitive data
df['social_security_number'] = df['social_security_number'].apply(lambda x: "********" if str(x).isdigit() else x)
df['email'] = df['email'].apply(lambda x: 'masked_email@example.com' if str(x).endswith('@company.com') else x)

# save masked data
df.to_csv('masked_data.csv', index=False)
```

In this example, we load a CSV file containing sensitive data such as social security numbers and email addresses. We then apply different masking techniques to the data depending on the level of sensitivity. For example, we replace social security numbers with ‘\*’, and we replace email addresses that end with '@company.com' with a masked email address. Finally, we save the masked data to a new CSV file which will look like this:

```python trailing-newline
| first\_nme | last\_name | social\_security\_number | email                                                        |
| ---------- | ---------- | ------------------------ | ------------------------------------------------------------ |
| Aisha      | Khan       | \*\*\*\*\*\*\*\*         | [masked\_email@example.com](mailto:masked_email@example.com) |
| Mia        | Tran       | \*\*\*\*\*\*\*\*         | [masked\_email@example.com](mailto:masked_email@example.com) |
| Jasmine    | Lee        | \*\*\*\*\*\*\*\*         | [masked\_email@example.com](mailto:masked_email@example.com) |
| Rohan      | Gupta      | \*\*\*\*\*\*\*\*         | [masked\_email@example.com](mailto:masked_email@example.com) |
| Aisha      | Shah       | \*\*\*\*\*\*\*\*         | [masked\_email@example.com](mailto:masked_email@example.com) |
| Jalen      | Davis      | \*\*\*\*\*\*\*\*         | [masked\_email@example.com](mailto:masked_email@example.com) |
```

#### Best practices for data masking in Python

- **Identify sensitive data**: Before masking data, it is important to identify what data is considered sensitive. This could include personal identifiable information (PII) such as names, addresses, and social security numbers, as well as financial information such as credit card numbers and bank account numbers.
- **Choose a masking technique**: There are several techniques for data masking, including substitution, shuffling, and encryption. The choice of technique will depend on the sensitivity of the data and the desired level of protection.
- **Mask the data**: Once the technique has been chosen, the data can be masked. Here is an example of using substitution to mask a social security number using [regular expressions](https://docs.python.org/3/library/re.html):

```python
import re

ssn = "123-45-6789"

# Replace any three concurrent digits with X's
masked_ssn = re.sub(r"\d{3}", "XX", ssn)

print(masked_ssn) # Output: XX-45-XX9
```

- **Test the masked data:** It is important to test the masked data to ensure that it is still usable for its intended purpose, but without exposing sensitive information. This can involve checking that the data is still accurate and consistent.
- **Document the masking process**: It is important to document the masking process to ensure that it can be replicated and audited in the future. This can include recording the masking technique used and any parameters or rules that were applied.

#### Masking vs. Obfuscation vs. Hashing

Data *masking*, data *obfuscation*, and data *hashing* are techniques used to protect sensitive data, but they differ in how they accomplish this.

**Data masking** is a technique used to hide sensitive information within a dataset by replacing the original data with a modified version that cannot be used to identify the original information. The masked data still has the same statistical properties as the original data but is no longer identifiable. Data masking is reversible, meaning that the original data can be retrieved if needed.

**Data obfuscation**, on the other hand, is a technique used to make data unintelligible or difficult to understand. It involves altering the data in a way that makes it difficult to interpret or make sense of without additional context or knowledge. Obfuscated data is not reversible, meaning that the original data cannot be retrieved.

**Data hashing** is a technique used to convert data into a fixed-length string of characters, called a hash. The hash is unique to the data being hashed, and even small changes to the original data will result in a completely different hash. Hashing is a one-way function, meaning that it is impossible to retrieve the original data from the hash. The main purpose of hashing is to ensure the integrity of data during transmission and storage by verifying that the hash of the data has not changed.

---

## Master Data Management (MDM)

> A method that defines and manages the critical data of an organization to provide a single point of reference across the organization.

Source: https://dagster.io/glossary/master-data-management-mdm

---

## Materialize

> Executing a computation and persisting the results into storage.

Source: https://dagster.io/glossary/data-materialization

### Definition of data materialization

"Materialization" refers to the process of executing a computation and persisting the results into a physical storage medium, such as a disk or memory. Materialization has particular significance in the context of large-scale data processing and transformation operations.

![](https://dagster.io/images/glossary/681399f654933b29e12fbe38_67eaca6bee9b55d17ac74c42_dagster-primary-mark.svg)

 Asset materialization is a core concept in the Dagster framework.\
[Find out more](https://docs.dagster.io/guides/automate/declarative-automation).  

### Data materialization in context

Here are the primary contexts in which "materialization" is used in data engineering:

1. **Lazy vs. Eager Evaluation**: Many modern computational frameworks use a technique called lazy evaluation, where operations are only computed when absolutely necessary. In these systems, data processing operations are built into a computational plan or a directed acyclic graph (DAG). The actual computations are delayed until an action that requires the results (like saving data or collecting it to the driver node) is called. At that point, the operations are materialized. The opposite approach, eager evaluation, materializes the result immediately after each operation.  Dagster also adopts the concepts of [Lazy](https://docs.dagster.io/guides/automate/declarative-automation) and [Eager](https://docs.dagster.io/guides/automate/declarative-automation) data asset meterialization policies.
2. **Intermediate Results in Data Pipelines**: In distributed data processing systems like Apache Spark or Apache Beam, data goes through a series of transformations. Each transformation might create an intermediate dataset. Depending on the design of the pipeline, these intermediate datasets might be materialized to disk or memory to optimize performance or fault tolerance.
3. **Stream Processing**: In stream processing, materialization may refer to storing the current state of a computation so that it can be restored after failures or for querying purposes. Systems like Apache Kafka's Streams API provide state stores that can materialize data for such scenarios.

### Benefits (and downsides) of data materialization

Materialization can offer several benefits, including:- Improved performance for frequently accessed computations.- Versioning of a data asset over time.- Fault tolerance, by reducing the need to recompute data after data pipeline failures.- Smart incremental processing, whereby a data pipeline can skip specific upstream branches if no data dependency on that branch has chnaged.

However, materialization can also introduce overheads related to storage and the time taken to write results to physical media. Effective data engineering involves making judicious decisions about when and where to materialize data based on the specific requirements of a use case.

### Materializing a data asset vs. materializing a database view

The term "materialization" is also used in database systems, where a view is a virtual table representing the results of a query. Whenever a user queries a view, the underlying query runs from scratch against the base tables. On the other hand, a **materialized view** is a physical copy of the data resulting from a query. It's essentially a snapshot of the data at a certain point in time. Materialized views can be refreshed periodically or based on specific triggers.

Using materialized views improves query performance by avoiding the overhead of recomputing the virtual table's data every time. This is especially useful for complex and frequently accessed queries. Of course, the snapshot may not reflect the very latest data since it's a static copy. So you need to define a strategy for how often to refresh your cached view.

### Materialization vs. Caching

Both data materialization and caching involve storing data to optimize access times and reduce computational or retrieval overhead. However, they differ in their primary objectives, usage scenarios, and lifecycles.

Data Materialization refers to the persisting of intermediate or final results of data processing and transformation tasks, often in the context of data engineering or databases. It is also used in big data processing where intermediate results are persisted to optimize iterative computations or enhance fault tolerance.*Materialized* data might have a longer lifecycle, and its refresh or update cycle is typically well-defined. For instance, a materialized view might be refreshed nightly or weekly.

Caching on the other hand refers to a strategy of temporarily storing frequently accessed data close to the point of use to reduce data retrieval times and offload source systems. It is common in web applications where frequent data (like user profiles) is stored in memory for rapid access. It is also used in databases and content delivery networks to speed up data or content delivery by temporarily storing it in a location closer to the user or application.

*Cached* data usually has a shorter lifecycle than materialized data and may be evicted based on policies (e.g., [Least Recently Used](https://www.educative.io/implement-least-recently-used-cache) (LRU) policy). The nature of caching is transient; if a cache is cleared, the system should still be able to fetch the original data, albeit with a performance hit.

So, while both mechanisms aim to improve data access times, data materialization is more about persisting data for optimized processing and computations over potentially longer periods whereas caching is about temporarily storing data for quick access, often accepting the possibility of stale data for the sake of speed.

### An example of data materialization in Python

We can demonstrate data materialization using Python and SQLite, a lightweight database system. This example will focus on the difference between views and materialized views.

Consider you have a dataset of products and their sales. We'll create two tables:

1. `products` - listing products and their details.
2. `sales` - listing individual sales records.

Our goal is to get the total sales amount for each product.

Here's how we can use SQLite in Python to demonstrate this:

```python
import sqlite3

# Set up an in-memory SQLite database
conn = sqlite3.connect(':memory:')
cursor = conn.cursor()

# Create products table
cursor.execute('''
CREATE TABLE products (
    product_id INTEGER PRIMARY KEY,
    product_name TEXT NOT NULL,
    price REAL NOT NULL
)
''')

# Create sales table
cursor.execute('''
CREATE TABLE sales (
    sale_id INTEGER PRIMARY KEY,
    product_id INTEGER,
    quantity_sold INTEGER,
    FOREIGN KEY (product_id) REFERENCES products (product_id)
)
''')

# Insert sample data into products
products = [(1, 'Laptop', 2500),
            (2, 'Mouse', 40),
            (3, 'Keyboard', 80)]
cursor.executemany('INSERT INTO products VALUES (?, ?, ?)', products)

# Insert sample data into sales
sales = [(1, 1, 5),  # 5 laptops sold
         (2, 2, 10), # 10 mice sold
         (3, 3, 7),  # 7 keyboards sold
         (4, 2, 5)]  # 5 more mice sold
cursor.executemany('INSERT INTO sales VALUES (?, ?, ?)', sales)

# Create a VIEW for total sales for each product
cursor.execute('''
CREATE VIEW product_sales AS
SELECT p.product_id, p.product_name, SUM(p.price * s.quantity_sold) as total_sales
FROM products p
JOIN sales s ON p.product_id = s.product_id
GROUP BY p.product_id
''')

# Now, let's materialize this view into a physical table called product_sales_materialized
cursor.execute('''
CREATE TABLE product_sales_materialized AS
SELECT * FROM product_sales
''')

# Query the materialized data
cursor.execute('SELECT * FROM product_sales_materialized')
print(cursor.fetchall())

conn.close()
```

In this example:- First, we set up an in-memory SQLite database.- We then defined two tables: `products` and `sales`.- After inserting some sample data, we created a view named `product_sales` that calculates the total sales for each product.- To demonstrate materialization, we copied the data from the view into a physical table named `product_sales_materialized`.- Finally, we queried the materialized table to get our results.

We can now print out our materialized data which will appear as follows:

```plaintext
[(1, 'Laptop', 12500.0), (2, 'Mouse', 600.0), (3, 'Keyboard', 560.0)]
```

In a real-world scenario, using materialized views can help in speeding up frequently accessed and complex queries by storing their results in a physical table, thus avoiding recalculating the results every time.

---

## Memoize

> Store the results of expensive function calls and reusing them when the same inputs occur again.

Source: https://dagster.io/glossary/data-memoization

### Memoization definition:

Memoization is a technique used to optimize computational efficiency by storing the results of expensive function calls or operations, and reusing them when the same inputs are encountered again. This approach is especially useful in situations where the cost of computation is high and the same results are needed multiple times.

While this can increase the speed of data processing, it can also lead to increased memory usage due to the storage of previous results. Therefore, it should be used judiciously, considering the trade-offs between computation time and memory usage. Therefore, it's a technique best used when the same inputs are likely to occur many times, and the benefits of faster computation outweigh the cost of additional memory use.

A common use case for memoization in data engineering could be when pulling data from an API. Data from APIs is often rate-limited, meaning you can only make a certain number of requests per minute or hour. If you're making repeated requests for the same data, memoization can help prevent hitting these rate limits by storing the results of previous requests.

### Data memoization example using Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

Here's a basic example in Python using the [`functools`](https://docs.python.org/3/library/functools.html) library's `cache` decorator, which implements memoization. We will use the Bureau of Labor Statistics’ public API to pull in [Consumer Price Index](https://data.bls.gov/timeseries/CUUR0000SA0) data. The [BLS API 2.0](https://www.bls.gov/developers/api_signature_v2.htm) allows us to make an anonymous call to pull small amounts of data so we don’t need an API key for this.

Note: The keys used for the memoization need to be immutable to ensure that they do not change after they are created, which would otherwise invalidate the cache.  Also note that Python's built-in \`functools.cache\` does not persist across different runs of the script. The cache is stored in memory and is discarded when the script ends or the function is garbage collected. If you need the cache to persist across different script runs, you would need to use a different caching strategy, such as writing the cache to a file or a database.

```python trailing-newline
import requests
import pandas as pd
from functools import cache
import json
import time


def get_bls_data(series_id, start_year, end_year):
    """Fetch data from the BLS API for a given series, caching the results to avoid unnecessary repeated requests."""
    headers = {"Content-type": "application/json"}
    series = {"seriesid": [series_id], "startyear": start_year, "endyear": end_year}
    p = requests.post("https://api.bls.gov/publicAPI/v2/timeseries/data/", json=series, headers=headers)
    json_data = json.loads(p.text)
    return json_data


@cache
def process_bls_data(series_id, start_year, end_year):
    """Fetch data from the BLS API and convert it into a pandas DataFrame."""
    data = get_bls_data(series_id, start_year, end_year)

    # Check if the API request was successful
    if data['status'] == 'REQUEST_SUCCEEDED':
        # Convert the data into a pandas DataFrame
        df = pd.DataFrame(data['Results']['series'][0]['data'])
        return df
    else:
        print(f"Failed to fetch data: {data['message']}")

# Usage
series_id = "CUUR0000SA0"
start_year = "2022"
end_year = "2023"

start_time = time.time()
df = process_bls_data(series_id, start_year, end_year)
print(df)
first_time = time.time()
df = process_bls_data(series_id, start_year, end_year)
print(df)
second_time = time.time()

print(f"Execution time was {first_time - start_time} on the first function call and {second_time - start_time} on the second one.")
```

This function uses the `cache` decorator to memoize results. If the `process_bls_data` function is called again with the same endpoint, it won't call on `get_bls_data` to make a new HTTP request but will instead return the result from the cache. This can save time and prevent hitting API rate limits.

Because the BLS API is slow, you will see that the first time this function runs, it will take a while.  But the second execution is almost immediate as your program is simply returning a cached result.

Eventually it will print out the most recent CPI data (twice):

```plaintext
| year | period | periodName | latest |   value | footnotes |
| ---- | ------ | ---------- | ------ | ------- | --------- |
| 2023 | M04    | April      |  true  | 303.363 | \[{}]     |
| 2023 | M03    | March      |    –   | 301.836 | \[{}]     |
| 2023 | M02    | February   |    –   | 300.840 | \[{}]     |
| 2023 | M01    | January    |    –   | 299.170 | \[{}]     |
```

and it will indicate the difference in the execution time:

```plaintext
Execution time was 75.35774612426758 on the first function call and 0.006146907806396484 on the second one.
```

Our memoization of the API call resulted in a 12,259 X improvement in execution time.

Finally, be aware that while this can save time, it can also lead to using outdated data if the data at the endpoint changes frequently (which this example does not, it changes monthly). You should only use this approach if the data does not change frequently or if it's acceptable to use slightly outdated data.

### Memoizing vs. Caching

"Caching" and "memoization" are terms used in computing that refer to the general concept of storing the results of expensive or frequently used operations to speed up subsequent accesses. Although they share similarities, there are some nuances in their usage and context.

1. **Memoization**: This term is usually used in the context of functional programming or algorithms. Memoization is a specific form of caching where the results of a deterministic function (a function that always produces the same output for the same input) are cached, so that future calls to the function with the same arguments can return the cached result rather than re-computing the value. It is often used as an optimization technique for recursive or dynamic programming algorithms. As shown in our example above, in Python, you can use the `functools.lru_cache` or `functools.cache` decorators to automatically memoize a function.
2. **Caching**: This term is used more broadly and can apply to many different layers of a system. For instance, a web browser may cache web pages so that it can display them more quickly if you visit them again. A database may cache query results to improve performance on repeated queries. Even hardware like CPUs use cache to store frequently used data close to the processor to reduce access times. In contrast to memoization, caching is not always tied to deterministic functions and can be used to store any data that may be expensive to fetch, compute, or generate.

In the context of data engineering, caching often refers to strategies to store data or computation results that are expensive to retrieve or compute, and that are expected to be re-used. This could be anything from intermediate results in a data processing pipeline, to the results of database queries, to API responses. Memoization, on the other hand, would more likely be used when writing specific functions or algorithms that are part of the data processing pipeline.

---

## Merge

> Combine data from multiple datasets into a single dataset.

Source: https://dagster.io/glossary/data-merging

### Data merging definition:

Data merging is the process of combining two or more datasets into a single dataset. It is a critical step in modern data pipelines when working with data from multiple sources or with different formats that need to be merged for analysis.

### Data merging example using Python:

*Please note that you need to* [***have the Pandas library installed in your Python environment***](https://dagster.io/glossary) *to run the following code examples.*

In Python, one of the most popular libraries for merging data are Pandas and Polars. The Pandas library provides several functions for merging data, including **`merge()`** , **`concat()`** , and **`join()`** .

#### The merge() function

The **`merge()`** function allows you to merge two data frames on one or more common columns. For example, let's say we have two data frames:`‍`

```python
import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': [1, 2, 3, 4]})
df2 = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'value': [5, 6, 7, 8]})
```

We can merge these two data frames on the 'key' column like this:`‍`

```python
merged_df = pd.merge(df1, df2, on='key', how='inner')
print(merged_df)
```

For an output of:`‍`

```plaintext
  key  value_x  value_y
0   B        2        5
1   D        4        6
```

#### The concat() function

The **`concat()`** function allows you to concatenate two or more data frames vertically or horizontally. For example, let's say we have two data frames:`‍`

```python
df1 = pd.DataFrame({'col1': ['A', 'B', 'C', 'D'], 'col2': [1, 2, 3, 4]})
df2 = pd.DataFrame({'col1': ['E', 'F', 'G', 'H'], 'col2': [5, 6, 7, 8]})
```

We can concatenate these two data frames horizontally like this:`‍`

```python
concatenated_df = pd.concat([df1, df2], axis=1)
print(concatenated_df)
```

This will yield an output of:`‍`

```plaintext
  key  value key  value
0   A      1   B      5
1   B      2   D      6
2   C      3   E      7
3   D      4   F      8
```

#### The join() function

The **`join()`** function is similar to **`merge()`** , but it joins two data frames on their index rather than on a column. For example, let's say we have two data frames:`‍`

```python
df1 = pd.DataFrame({'value1': [1, 2, 3, 4]}, index=['A', 'B', 'C', 'D'])
df2 = pd.DataFrame({'value2': [5, 6, 7, 8]}, index=['B', 'D', 'E', 'F'])
```

We can join these two data frames like this:`‍`

```python
joined_df = df1.join(df2, how='inner')
print(joined_df)
```

In this example, we join **`df1`** and **`df2`** on their index and keep only the rows that are present in both data frames (specified by **`how='inner'`** ).

This will yield an output of:`‍`

```plaintext
   value1  value2
B       2       5
D       4       6
```

### Data merge vs. data join vs. data matching

It's worth disambiguating a few terms here as these refer to different ways of combining datasets:

- Data merging combines datasets by simply appending columns from different datasets
- Data join combines datasets based on a common key or column.
- Data matching combines records or data elements from different datasets based on their similarity or matching criteria.

#### Data merge vs. data join

As opposed to data merging discussed above, data *join* is a specific type of data merging that involves combining two or more datasets **based on a common column or key**. In a data join, the resulting dataset will only include rows where the key matches in both datasets. This means that any rows that do not have a matching key in both datasets will not be included in the final output. Data join is often used in relational databases, where tables can be joined based on a common key.

Data join is more selective in its output than data merging. Data join only includes rows where the key matches in both datasets, while data merging includes all rows from both datasets.

### Data merge vs. data match

Data *matching* involves comparing two or more datasets to identify and extract common records or data elements. The goal of data matching is to find matching records or data elements in different datasets and combine them into a single, unified record or dataset. Data matching can be done using a variety of methods, including fuzzy matching, exact matching, and probabilistic matching.

While data merging is a relatively straightforward process that involves identifying a common variable or column and combining datasets based on that variable, data matching can be a more complex process that involves comparing multiple datasets and identifying common records or data elements based on complex algorithms and rules.

---

## Metadata

> Data that provides information about other data, such as data structure or content details.

Source: https://dagster.io/glossary/metadata

---

## Micro-Batching

> A data processing method that deals with relatively small batches of data, providing a middle ground between batch processing and stream processing.

Source: https://dagster.io/glossary/micro-batching

---

## Microsoft SSIS (SQL Server Integration Services)

> A platform for data integration and workflow applications.

Source: https://dagster.io/glossary/microsoft-ssis-sql-server-integration-services

---

## Model

> Create a conceptual representation of data objects.

Source: https://dagster.io/glossary/data-modeling

### Data modeling definition:

Data modeling is the process of creating a conceptual representation of data objects, their relationships, and the rules that govern them. In the context of modern data pipelines, data modeling is crucial to organizing and structuring data in a way that enables efficient data processing, analysis, and visualization.

There are various data modeling techniques such as [Entity-Relationship (ER) modeling](https://en.wikipedia.org/wiki/Entity%E2%80%93relationship_model), [UML modeling](https://en.wikipedia.org/wiki/Unified_Modeling_Language), and [Data Flow Diagrams](https://en.wikipedia.org/wiki/Data-flow_diagram) (DFDs). In addition, modern data pipelines rely heavily on the use of machine learning algorithms for predictive modeling and pattern recognition.

### Data modeling example using Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

In Python, there are several libraries and frameworks for data modeling, including:

- **Scikit-learn**: a popular machine-learning library that includes a wide range of tools for classification, regression, and clustering.
- **TensorFlow**: an open-source machine learning framework developed by Google that is widely used for building and training deep neural networks.
- **PyTorch**: another popular open-source machine learning framework that is known for its ease of use and flexibility.
- **Pandas**: a data manipulation library that provides data structures and functions for working with structured data.

Here is an example of using scikit-learn for data modeling:

Given an input file `data.csv` as follows:

```plaintext
feature_1,feature_2,target
1,2,10
2,4,20
3,6,30
4,8,40
5,10,50
```

The code:

```python
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import pandas as pd

# load data
data = pd.read_csv('data.csv')

# split data into training and testing sets
train_data, test_data = train_test_split(data, test_size=0.2)

# prepare training data
X_train = train_data.drop('target', axis=1)
y_train = train_data['target']

# create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# prepare testing data
X_test = test_data.drop('target', axis=1)
y_test = test_data['target']

# make predictions on test data
predictions = model.predict(X_test)

# calculate mean squared error
mse = mean_squared_error(y_test, predictions)
print('Mean squared error:', mse)
```

Will yield an output of:

```plaintext
Mean squared error: 1.262177448353619e-29
```

In this example, we are using the scikit-learn library to create a simple linear regression model. We load our data from a CSV file, split it into training and testing sets, and prepare the training and testing data. We then create and train the linear regression model on the training data and use it to make predictions on the testing data. Finally, we calculate the mean squared error to evaluate the performance of the model.

---

## Multi-tenancy

> A reference to the mode of operation of software where multiple independent instances of one or multiple applications operate in a shared environment.

Source: https://dagster.io/glossary/multi-tenancy

---

## Multiprocessing

> Optimize execution time with multiple parallel processes.

Source: https://dagster.io/glossary/multiprocessing

### A definition of Multiprocessing in Python

**Multiprocessing** refers to a paradigm where multiple processes are employed to execute different tasks simultaneously in order to optimize the execution time and improve the efficiency of a pipeline, particularly for large volumes of data or heavy compute requirements. This is achieved using the `multiprocessing` module in Python which allows the creation of process-based parallelism.

Here are some important points about multiprocessing in Python:

1. Each process in a multiprocessing environment runs in its own memory space, which means they don't share global variables and run independently. This is advantageous in data processing tasks as it prevents data corruption due to simultaneous read/write operations from different processes.
2. This module provides functionalities for distributing input data across different processes (data parallelism), pooling processes to limit total number of active processes, synchronizing and sharing states between processes, and more.
3. Python multiprocessing is particularly useful in data engineering where CPU-intensive tasks like data cleaning, data transformation, etc., need to be performed on large datasets. By distributing these tasks among different CPUs or cores, multiprocessing can significantly reduce execution time.
4. Multiprocessing comes with its own overheads such as inter-process communication, process creation time, etc. For small datasets or tasks where the processing time is less than the overhead time, multiprocessing will not provide significant benefits, or may even slow down the execution.

### Example of multiprocessing in Python

Let's look at an example of a [fan-out](https://dagster.io/glossary/fan-out) process with multiprocessing. We will generate a list of numbers and use the `multiprocessing` module in Python to spawn a number of worker processes. Each worker process will apply a function to a subset of the list, allowing us to utilize multiple cores for our computation.

We start a number of worker processes equal to the number of cores available on the machine (`multiprocessing.cpu_count()`). Each worker process applies a function to a number from the list and stores the results in a shared queue. Once all the numbers have been processed, the main process retrieves the results from the shared Queue.

Please note that due to the use of `multiprocessing.Queue`, this code is not intended to be run in a notebook environment as it may cause the notebook kernel to crash. Run this in a standalone Python environment.

After using multiprocessing to process the numbers, it will also process the numbers using a single process and compare the execution times.

```python
import multiprocessing
import os
import time

def generate_data_file():
    with open('numbers.txt', 'w') as f:
        for i in range(100000):
            f.write(f"{i}\n")

def worker(input_queue, output_queue):
    print(f"Worker process id: {os.getpid()}")
    result = []
    for number in iter(input_queue.get, 'STOP'):
        result.append(number**2)  # some computational task
    output_queue.put(result)

def single_process(numbers):
    return [number**2 for number in numbers]

def fan_out_process():
    # Generate data file
    generate_data_file()

    # Read data from file
    with open('numbers.txt', 'r') as f:
        numbers = [int(line.strip()) for line in f]

    num_workers = multiprocessing.cpu_count()

    # Create queues
    input_queue = multiprocessing.Queue()
    output_queue = multiprocessing.Queue()

    # Start worker processes
    for _ in range(num_workers):
        multiprocessing.Process(target=worker, args=(input_queue, output_queue)).start()

    # Send data to worker processes
    for number in numbers:
        input_queue.put(number)

    # Signal worker processes to stop when they're done
    for _ in range(num_workers):
        input_queue.put('STOP')

    # Collect results
    results = []
    for _ in range(num_workers):
        results.extend(output_queue.get())

    return results

if __name__ == "__main__":
    # Measure time taken using multiprocessing
    start_time = time.time()
    results = fan_out_process()
    multi_proc_time = time.time() - start_time
    print(f"Computation with multiprocessing took {multi_proc_time:.2f} seconds")
    print(f"First 10 results with multiprocessing: {results[:10]}")

    # Measure time taken using single process
    start_time = time.time()
    with open('numbers.txt', 'r') as f:
        numbers = [int(line.strip()) for line in f]
    results = single_process(numbers)
    single_proc_time = time.time() - start_time
    print(f"Computation with single process took {single_proc_time:.2f} seconds")
    print(f"First 10 results with single process: {results[:10]}")

    # Compare times
    time_difference = single_proc_time / multi_proc_time
    if time_difference > 1:
        print(f"Multiprocessing was {time_difference:.2f} times faster")
    else:
        print(f"Multiprocessing was {1/time_difference:.2f} times slower")
```

Note that the time difference will vary based on the hardware, operating system, and the specifics of the task being performed. In general, for CPU-bound tasks (like our squaring operation), you can expect to see a speedup roughly proportional to the number of cores on your machine when using multiprocessing.

Here is the output on my machine:

```plaintext
Worker process id: 9700
Worker process id: 9704
Worker process id: 9703
Worker process id: 9699
Worker process id: 9705
Worker process id: 9708
Worker process id: 9701
Worker process id: 9702
Worker process id: 9706
Worker process id: 9707
Computation with multiprocessing took 1.28 seconds
First 10 results with multiprocessing: [1610361, 1635841, 1661521, 1687401, 1713481, 1739761, 1766241, 1792921, 1819801, 1846881]
Computation with single process took 0.03 seconds
First 10 results with single process: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Multiprocessing was 38.11 times slower
```

### Why do we see different results?

The first thing that might jump out at you is that the first 10 results are different for multiprocessing and single-process execution.  You may (or may not) see this if you execute the code locally.  So what is happening here?

When you use a queue to receive the output of each worker process, the order in which the results are placed in the queue depends on the timing and scheduling of the processes. The operating system determines the execution order of the processes, and it may vary between different runs of the program or on different systems.

If you need to maintain the order of the results, you can include additional information in the output, such as an index or a timestamp, and sort the results based on that information after retrieving them from the queue. Alternatively, you can use the `Pool` class from the multiprocessing module, which provides a map function that guarantees the order of the results.

So in the case above, the first 10 results added to the queue where not from the worker processing the first numbers in the sequence.  To test this, you can reduce the number of items in the range in the line `for i in range(100000):`.  If you have less than 100 items, your computer will return the same result for both approaches.  You will also notice that as there are less numbers to crunch, the overhead of  multiprocessing significantly affects relative the difference in execution time between these methods.

### Why multiprocessing can be slower

OK, so in our example, multiprocessing was almost fourty times *SLOWER* than single-threaded execution. What happened?

While multiprocessing in Python allows us to utilize multiple cores and therefore speed up CPU-bound tasks, there are several factors that can cause multiprocessing to be slower than single-threaded execution:

1. **Overhead of Inter-Process Communication (IPC):** In Python, different processes don't share memory space, so any communication between them (using Queues or Pipes, as in this example) requires pickling/unpickling data, which can be time-consuming, especially for large data sets.
2. **Overhead of Process Creation:** Creating a new process is more resource-intensive than creating a new thread. If the number of processes being created is large, or if processes are being created and destroyed frequently, this overhead can become significant.
3. **GIL (Global Interpreter Lock) does not affect multiprocessing:** The GIL is a mechanism used in CPython interpreter that allows only one thread to execute Python bytecodes at a time, even on a multi-core system. However, GIL does not prevent multiple processes from running on different cores at the same time. This means that multiprocessing can utilize multiple cores, but this comes with the overheads mentioned above.
4. **The nature of the task:** If the task being performed by each process is not CPU-bound (i.e., it doesn't require a lot of computational power), then the overhead of IPC and process creation can outweigh the benefits of running tasks in parallel. In our case, squaring a number is a relatively simple operation, so it's possible that the overheads of multiprocessing are larger than the time it takes to simply square all the numbers in a single process.

To get the benefits of multiprocessing, you generally want to use it for CPU-bound tasks that take a significant amount of time to run. For IO-bound tasks (such as downloading web pages), threading or asynchronous IO can be more efficient. For tasks that are neither CPU-bound nor IO-bound, running them in a single process may be the most efficient approach.

Before designing your final architecture, be sure to test these approaches and research `Threading` as an alternative approach:

[      Jump to the entry for 'Threading'.    ](https://dagster.io/glossary/data-threading)

---

## Munge

> See 'wrangle'.

Source: https://dagster.io/glossary/data-munging

### Definition of data munging

The process of converting unstructured data into a structured format is typically referred to as "data wrangling" or "data munging". Both terms refer to the process of transforming and mapping data from a raw form into another format that allows for more convenient consumption of the data.

However, different people and organizations may use these terms in slightly different ways. For example, some people may use "data munging" to specifically refer to the cleaning and preprocessing of data, while "data wrangling" might be used in a broader sense to include tasks such as gathering and exploring the data.

[      Jump to the entry for 'wrangle'.    ](https://dagster.io/glossary/data-wrangling)

---

## Named Entity Recognition

> Locate and classify named entities in text into pre-defined categories.

Source: https://dagster.io/glossary/named-entity-recognition

### Named entity recognition definition:

**Named Entity Recognition (NER)** is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. Here is a simple Python program for NER using Spacy, a popular library for Natural Language Processing (NLP).

### An example of named entity recognition in Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

To run this example, you'll need to install the `spacy` library and download a language model. You can do this using pip:

```python
pip install spacy
python -m spacy download en_core_web_sm
```

Now run the Python code:

```python
import spacy

# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load("en_core_web_sm")

# Process whole documents
text = ("Dagster is a new data orchestration solution for modern data engineering."
        "Unlike other solutions, Dagster provides a declarative approach to building data pipelines."
        "With Dagster, your data engineering team can speed up development cycles, "
        "observe your critical data assets, "
        "and confidently test your code so that you can push to production in full confidence."
        )

doc = nlp(text)

# Analyze syntax
print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])

# Find named entities, phrases and concepts
for entity in doc.ents:
    print(entity.text, entity.label_)
```

In this example, we first import the `spacy` library and load the English language model. We then process a text string with the `nlp` function, which returns a `Doc` object that holds all information about the tokens, their linguistic features and their relationships.

We print out all noun phrases and verbs in the text, and then iterate over the `ents` property of the `Doc` object, which gives us the named entities. For each entity, we print its text and its label.

Please note that the model may not be perfect and might not catch all named entities correctly, especially with informal language, slang, or typos.

This should yield the following output:

```plaintext
Noun phrases: ['Dagster', 'a new data orchestration solution', 'modern data engineering', 'other solutions', 'Dagster', 'a declarative approach', 'data pipelines', 'Dagster', 'your data engineering team', 'development cycles', 'your critical data assets', 'your code', 'you', 'production', 'full confidence']
Verbs: ['provide', 'build', 'speed', 'observe', 'test', 'push']
Dagster PERSON
Dagster PERSON
Dagster PERSON
```

In running this example, you migth run into the error \`def TFE\_ContextOptionsSetAsync(arg1, async):\`. If this occurs, consult \[the solution here]\(<https://github.com/tensorflow/tensorflow/issues/20690>).

---

## Normality Testing

> Assess the normality of data distributions to ensure validity and reliability of statistical analysis.

Source: https://dagster.io/glossary/normality-testing

### Normality testing definition:

**Normality testing** is a statistical technique used to determine if a given sample or population data follows a normal distribution. A normal distribution, also known as a Gaussian distribution, is a bell-shaped curve that is symmetric around the mean. Normality testing comes in handy if you want to test the assumption that your data follows a normal distribution.

There are several methods and libraries/functions for normality testing when working in Python, the three most used ones being:

- **Shapiro-Wilk test**: This test calculates a W statistic that tests the null hypothesis that the data was drawn from a normal distribution. The test returns a **p-value** which can be used to determine if the null hypothesis can be rejected or not.
- **Kolmogorov-Smirnov test**: This test calculates a D statistic that measures the distance between the empirical distribution function of the sample and the cumulative distribution function of the normal distribution. The test returns a **p-value** which can be used to determine if the null hypothesis can be rejected or not.
- **Anderson-Darling test**: This test calculates an A2 statistic that measures the distance between the sample and the theoretical distribution. The test returns a **critical value** which can be compared with the A2 statistic to determine if the null hypothesis can be rejected or not.

### Normality testing example using Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

#### Shapiro-Wilk test

Here is a practical example of how to perform a normality test using the Shapiro-Wilk test in Python:

```python
from scipy import stats
import numpy as np

# Generate a random sample of 1000 numbers from a normal distribution
sample = np.random.normal(loc=0, scale=1, size=1000)

# Perform Shapiro-Wilk test on the sample
shapiro_test = stats.shapiro(sample)

# Print the test statistics and the p-value
print('Shapiro-Wilk test statistics: ', shapiro_test[0])
print('Shapiro-Wilk test p-value: ', shapiro_test[1])
```

In this example, we generated a random sample of 1000 numbers from a normal distribution and then performed the Shapiro-Wilk test on the sample. The `stats.shapiro()` function from the [scipy library](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html) is used to perform the test, and it returns two values: the test statistic and the p-value. Since the numbers are random, the output will vary, but will look something like this:

```plaintext
Shapiro-Wilk test statistics:  0.9981188774108887
Shapiro-Wilk test p-value:  0.33603352308273315
```

The test statistic is used to determine if the sample follows a normal distribution or not. If the p-value is less than the significance level (usually 0.05), then we reject the null hypothesis and conclude that the sample does not follow a normal distribution. If the p-value is greater than the significance level, then we fail to reject the null hypothesis and conclude that the sample follows a normal distribution.

#### Kolmogorov-Smirnov test

The Kolmogorov-Smirnov test (K-S test) is a type of non-parametric test that you can use to compare a sample with a reference probability distribution (one-sample K-S test), or to compare two samples (two-sample K-S test). It is a method to identify if your data significantly deviates from a specified distribution.

Here's a simple Python program for K-S test using the SciPy library, which is a library for scientific computations in Python. For this example, we'll use the one-sample K-S test to test if a given sample follows a normal distribution.

First, you will need to install the necessary libraries, `numpy`, `scipy` and `matplotlib`.We also provide a full list of [Python libraries with install commands here](https://dagster.io/glossary/packages).Either way, for this example can do this using pip:

```javascript
pip install numpy scipy matplotlib
```

Here is the Python code:

```python
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

# generate a gaussian random variable sample
np.random.seed(0)  # for reproducibility
sample_size = 1000
sample = np.random.normal(loc=0, scale=1, size=sample_size)

# perform Kolmogorov-Smirnov test
D, p_value = stats.kstest(sample, 'norm')

print(f'K-S statistic: {D}')
print(f'p-value: {p_value}')

# visual check
plt.figure(figsize=(10, 7))
plt.hist(sample, bins=30, density=True, alpha=0.5, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = stats.norm.pdf(x, 0, 1)
plt.plot(x, p, 'k', linewidth=2)
plt.show()
```

This script first generates a sample of 1000 data points from a normal distribution with mean 0 and standard deviation 1. Then it performs the K-S test using the `kstest` function from the `scipy.stats` module, comparing the sample to a standard normal distribution. The test returns the K-S statistic `D` and the p-value of the test.

If the K-S statistic is small or the p-value is high (greater than the significance level, say 0.05), then we cannot reject the hypothesis that the distributions of the two samples are the same. Otherwise, we reject the hypothesis and conclude that the distributions are different.

Finally, a histogram of the sample and the PDF (probability density function) of the standard normal distribution are plotted.

As our sample is randomly generated, we cannot predict your output, but it will look something like this:

![](https://dagster.io/images/glossary/681399f654933b29e12fbcc6_glossary-12.png)

```plaintext
K-S statistic: 0.03737519429804048
p-value: 0.11930823166569182
```

---

## Normalize

> Standardize data values to facilitate comparison and analysis. Organize data into a consistent format.

Source: https://dagster.io/glossary/data-normalization

### Data normalization definition:

Data normalization is the process of transforming data into a common scale. It is an important technique in data processing, as it ensures that different features of the data contribute equally to the analysis. Data normalization can be achieved through several techniques, such as Min-Max scaling, Z-score normalization, and Robust scaling.

Normalization is achieved through a set of rules known as *normal forms*. The most commonly used normal forms are first,second and third normal form (1NF,2NF,3NF), and Boyce-Codd normal form (BCNF).

### Data normalization example using Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run the following code examples.*

Lets look at two examples of data normalization using Python:

Suppose we have a dataset of customers' orders with the following columns: customer ID, order ID, order date, product name, and quantity. We can normalize this data by separating it into two tables: customers and orders. The customers table would have columns for customer ID, customer name, and customer address. The orders table would have columns for order ID, customer ID (which would be a foreign key referencing the customer ID column in the customers table), order date, product name, and quantity.

Here's the code to normalize this data:

```python
import pandas as pd

# create a dataframe with the original data
data = {'customer_id': [1, 1, 2, 3, 3],
        'order_id': [1001, 1002, 1003, 1004, 1005],
        'order_date': ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'],
        'product_name': ['product1', 'product2', 'product3', 'product1', 'product2'],
        'quantity': [2, 1, 3, 1, 2]}
df = pd.DataFrame(data)

# create the customers table by dropping duplicates and resetting the index
customers = df[['customer_id']].drop_duplicates().reset_index(drop=True)

# add columns for customer name and address
customers['customer_name'] = ['John', 'Samantha', 'Peter']
customers['customer_address'] = ['123 Main St', '456 Maple Ave', '789 Oak Rd']

# create the orders table by merging the original dataframe with the customers table
orders = pd.merge(df, customers, on='customer_id')

# drop the customer ID column from the orders table
orders = orders.drop(columns=['customer_id'])

# print the resulting tables
print(customers)
print(orders)
```

This code will output the following tables:

```plaintext
   customer_id customer_name customer_address
0            1          John     123 Main St
1            2      Samantha   456 Maple Ave
2            3         Peter      789 Oak Rd

   order_id  order_date product_name  quantity customer_name customer_address
0      1001  2021-01-01     product1         2          John     123 Main St
1      1002  2021-01-02     product2         1          John     123 Main St
2      1003  2021-01-03     product3         3      Samantha   456 Maple Ave
3      1004  2021-01-04     product1         1         Peter      789 Oak Rd
4      1005  2021-01-05     product2         2         Peter      789 Oak Rd
```

In this example, we normalized the data by creating two tables: one for customers and one for orders.

Here is a second practical example in Python using Min-Max scaling:

```python
from sklearn.preprocessing import MinMaxScaler
import pandas as pd

# create a sample dataframe with two columns
df = pd.DataFrame({'column1': [12, 28, 31, 49], 'column2': [50, 60, 70, 80]})

# create a MinMaxScaler object
scaler = MinMaxScaler()

# fit and transform the data
normalized_data = scaler.fit_transform(df)

# create a new dataframe with the normalized data
df_normalized = pd.DataFrame(normalized_data, columns=df.columns)

print(df_normalized)
```

Here, we use the `MinMaxScaler` object from the `sklearn.preprocessing` module to scale the data. We fit the scaler object to the original dataframe and transform the data into a normalized scale. Finally, we create a new dataframe with the normalized data and print it to the console.  The output will look like this:

```plaintext
    column1   column2
0  0.000000  0.000000
1  0.432432  0.333333
2  0.513514  0.666667
3  1.000000  1.000000
```

The resulting dataframe will have the same number of rows and columns as the original dataframe, but with values in the range \[0, 1].

Best practices for normalizing data include:

- **Choosing the appropriate normalization technique**: There are various normalization techniques, including min-max normalization, z-score normalization, and log transformation. Choose the appropriate normalization technique based on the data distribution, the intended use case, and the desired outcome.
- **Handling missing data**: Normalization techniques can be sensitive to missing data. Decide on a strategy to handle missing data, whether it's through imputation or exclusion of missing data points.
- **Scaling and centering the data**: Normalization often involves scaling and centering the data, so the values are comparable and have the same mean and standard deviation. Use Python libraries like Scikit-learn or Pandas to perform scaling and centering.
- **Evaluating the effect on the data distribution:** Normalization can change the distribution of the data, which can impact downstream analysis. Evaluate the effect of normalization on the data distribution to ensure that it aligns with the intended outcome.
- **Addressing outliers**: Outliers can affect the normalization process and the results of the analysis. Consider removing or transforming outliers before normalization or using a normalization technique that is robust to outliers.
- **Ensuring the normalization is reversible**: The normalization process should be reversible to ensure that the data can be reconstructed in its original form if needed. Ensure that the normalization method used can be easily reversed.
- **Testing and validating the normalization process**: It's essential to test and validate the normalization process to ensure that it's producing the desired outcome. Use automated testing and validation techniques to ensure that the normalization is accurate and consistent.

---

## Obfuscate

> Make data unintelligible or difficult to understand.

Source: https://dagster.io/glossary/data-obfuscation

### Data obfuscation definition:

**Data obfuscation** is a technique used to make data unintelligible or difficult to understand. It involves altering the data in a way that makes it difficult to interpret or make sense of without additional context or knowledge.  It involves changing the original data values to new values that are not meaningful or significant, but still maintain the same data format.

Obfuscated data is not reversible, meaning that the original data cannot be retrieved.

### Data obfuscation example using Python:

In Python, one simple example of data obfuscation is using the random module to generate random values to replace the original data:

```python
import random

# define the original data
original_data = "Sensitive data that needs to be obfuscated."

# obfuscate the data by replacing characters with random characters
obfuscated_data = ""
for char in original_data:
    if char.isalpha():
        if char.isupper():
            obfuscated_data += random.choice("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
        else:
            obfuscated_data += random.choice("abcdefghijklmnopqrstuvwxyz")
    elif char.isdigit():
        obfuscated_data += random.choice("0123456789")
    else:
        obfuscated_data += char

print("Original data:", original_data)
print("Obfuscated data:", obfuscated_data)
```

The output of the above code sample would be something like:

```plaintext
Original data: Sensitive data that needs to be obfuscated.
Obfuscated data: HuItOmwtpwu rwqudnlw ugxcq wxh mebgakjthay.
```

In this example, the original sensitive data is replaced with random characters while preserving the original data format. The resulting obfuscated data is unreadable and unusable to unauthorized individuals, while still functional for authorized users.  Note that it will also change each time you execute the code.

---

## Object-Relational Mapping (ORM)

> A programming technique to convert data between incompatible type systems in object-oriented programming languages.

Source: https://dagster.io/glossary/object-relational-mapping-orm

---

## Operational Data Store (ODS)

> A database designed to integrate data from multiple sources for additional operations on the data, serving as an intermediary between the data warehouse and the process of data sources.

Source: https://dagster.io/glossary/operational-data-store-ods

---

## Parallelize

> Boost execution speed of large data processing by breaking the task into many smaller concurrent tasks.

Source: https://dagster.io/glossary/parallelization-in-data-engineering

### A definition of parallelization

Parallelization refers to the breaking down of data processing tasks into smaller units of work that can be executed concurrently, either on a single machine with multiple processing units or across distributed computing resources. It aims to enhance the efficiency and speed of data processing by leveraging parallel computing techniques.

Data engineers often deal with large volumes of data that require complex transformations, aggregations, or analysis. Parallelization enables them to distribute these tasks across multiple computing resources, such as CPU cores or machines, to achieve faster and more scalable data processing.

In Python, parallelization can be achieved in data pipelines using various techniques:

1. **Parallel Execution**: Data engineers can split the processing tasks into smaller chunks and execute them in parallel using techniques like multiprocessing, threading, or asynchronous programming. This allows for concurrent execution of tasks, taking advantage of multiple CPU cores or threads, and reducing the overall processing time.
2. **Distributed Computing**: For larger datasets or computationally intensive operations, data engineers can employ distributed computing frameworks like Apache Spark, Dask, or Ray. These frameworks enable the execution of data processing tasks across a cluster of machines, providing scalability and fault tolerance.
3. **Batch Processing**: Data engineers can utilize batch processing frameworks such as Apache Hadoop or Apache Airflow, which enable the parallel execution of tasks in a predefined workflow. These frameworks schedule and distribute tasks across multiple nodes, allowing for parallel processing of data pipelines.
4. **Data Parallelism**: When dealing with data-intensive operations, data engineers can partition the data into smaller subsets and perform parallel computations on each subset. This approach is particularly useful for tasks like data aggregation, map-reduce operations, or parallel database operations.

### An example of process parallelization in Python using Dask

In the following steps, we will be utilizing Python's built-in `os` module, `pandas` for data manipulation, `uuid` for generating unique identifiers and `dask` for parallel processing.

First, let's create a directory named `sales_data`, and populate it with twelve CSV files representing sales data for each month:

```python
import os
import pandas as pd
import numpy as np
from uuid import uuid4

# Function to check if a directory exists and create if it doesn't
def create_directory(directory_name):
    if not os.path.exists(directory_name):
        os.makedirs(directory_name)

# Create the directory
directory_name = "sales_data"
create_directory(directory_name)

# Function to generate random sales data
def create_sales_data():
    return pd.DataFrame({
        "item_id": [uuid4() for _ in range(1000)],
        "price": np.random.rand(1000) * 100,
        "quantity": np.random.randint(1, 10, size=1000),
    })

# Generate and save the sales data to CSV for each month
for month in range(1, 13):
    sales_data = create_sales_data()
    sales_data.to_csv(f"{directory_name}/sales_data_{month}.csv", index=False)
```

Now that we have our data, we can write a Dask process to read all the CSV files, compute the total sales (price \* quantity) for each item sold each month, and then calculate the total sales over the entire period.

```python
import dask.dataframe as dd

# Construct a Dask DataFrame from all the CSV files
ddf = dd.read_csv(f"{directory_name}/*.csv")

# Calculate the total sales for each item by multiplying price with quantity
ddf['total_sales'] = ddf['price'] * ddf['quantity']

# Compute the total sales over the entire period
total_sales = ddf['total_sales'].sum().compute()

print(f"Total sales over the period: ${total_sales:,.2f}")
```

In this script, `dask.dataframe.read_csv` function constructs a Dask DataFrame representing all of the data in the CSV files. This doesn't actually load any data yet, but instead lazily represents it as computations to perform.

The line `ddf['total_sales'] = ddf['price'] * ddf['quantity']` adds a new column to the DataFrame that represents the total sales for each item. Again, this doesn't perform any computations yet.

Finally, `total_sales = ddf['total_sales'].sum().compute()` performs all of the lazy computations. It sums the total sales for each item to get the total sales for the entire period, then uses `compute()` to execute the computations in parallel.

### Comparing performance

Let's add some timing to our previous script to see the performance difference between Dask and Pandas.

We will use Python's built-in `time` module to measure the execution time of the Dask computation and the equivalent Pandas computation.

First, let's perform the computation with Dask and measure the time taken:

```python
import time

# Dask
start_time = time.time()

ddf = dd.read_csv(f"{directory_name}/*.csv")
ddf['total_sales'] = ddf['price'] * ddf['quantity']
total_sales = ddf['total_sales'].sum().compute()

end_time = time.time()

dask_time = end_time - start_time

print(f"Total sales over the period (Dask): ${total_sales:.2f}")
print(f"Time taken with Dask: {dask_time:.2f} seconds")
```

Now, let's do the same thing with Pandas:

```python trailing-newline
# Pandas
start_time = time.time()

total_sales = 0
for month in range(1, 13):
    df = pd.read_csv(f"{directory_name}/sales_data_{month}.csv")
    df['total_sales'] = df['price'] * df['quantity']
    total_sales += df['total_sales'].sum()

end_time = time.time()

pandas_time = end_time - start_time

print(f"Total sales over the period (Pandas): ${total_sales:.2f}")
print(f"Time taken with Pandas: {pandas_time:.2f} seconds")
```

Finally, let's compare the relative performance of each approach:

```python
# Compare times
time_difference = pandas_time / dask_time
if time_difference > 1:
    print(f"Dask was {time_difference:.2f} times faster")
else:
    print(f"Dask was {1 / time_difference:.2f} times slower")
```

With this, you should be able to see the performance difference between Dask and Pandas.

```plaintext
Total sales over the period: $2,987,126.20
Total sales over the period (Dask): $2,987,126.20
Time taken with Dask: 0.02 seconds
Total sales over the period (Pandas): $2,987,126.20
Time taken with Pandas: 0.01 seconds
Dask was 1.78 times slower
```

In general, Dask should be faster for larger datasets (that don't fit into memory), as it can perform computations in parallel and only load parts of the data into memory at a time. But as we see in this example, **for smaller datasets the overhead of parallelization can make Dask slower than Pandas**.  In fact this difference in performance would only get larger if we scale the number of files involved as the task is simple, so the overhead of starting each new process compounds. It is important to understand the relative performance of concurrent execution techniques in order to properly optimize your pipelines.

For example, you can try expanding the size of the dataset as follows, and eventually Dask becomes the more performant technique:

```python trailing-newline
# Function to generate random sales data
def create_sales_data():
    return pd.DataFrame({
        "item_id": [uuid4() for _ in range(100000)],
        "price": np.random.rand(100000) * 100,
        "quantity": np.random.randint(1, 10, size=100000),
        "description": generate_random_string(12),
        "item_name": generate_random_string(255),
        "description2": generate_random_string(255),
        "store_id2": generate_random_string(6),
    })
```

With the above dataset you might see results such as the ones below, and the performance difference will increase along with the size of your datasets:

```plaintext
Time taken with Dask: 2.13 seconds
Time taken with Pandas: 3.08 seconds
Dask was 1.45 times faster
```

Here is the complete code for the examples above:

```python
import os
import pandas as pd
import numpy as np
from uuid import uuid4
import dask.dataframe as dd
import time

# Function to check if a directory exists and create if it doesn't
def create_directory(directory_name):
    if not os.path.exists(directory_name):
        os.makedirs(directory_name)

# Create the directory
directory_name = "sales_data"
create_directory(directory_name)

# Function to generate random sales data
def create_sales_data():
    return pd.DataFrame({
        "item_id": [uuid4() for _ in range(1000)],
        "price": np.random.rand(1000) * 100,
        "quantity": np.random.randint(1, 10, size=1000),
    })

# Generate and save the sales data to CSV for each month
for month in range(1, 13):
    sales_data = create_sales_data()
    sales_data.to_csv(f"{directory_name}/sales_data_{month}.csv", index=False)


# Construct a Dask DataFrame from all the CSV files
ddf = dd.read_csv(f"{directory_name}/*.csv")

# Calculate the total sales for each item by multiplying price with quantity
ddf['total_sales'] = ddf['price'] * ddf['quantity']

# Compute the total sales over the entire period
total_sales = ddf['total_sales'].sum().compute()

print(f"Total sales over the period: ${total_sales:,.2f}")

# Dask
start_time = time.time()

ddf = dd.read_csv(f"{directory_name}/*.csv")
ddf['total_sales'] = ddf['price'] * ddf['quantity']
total_sales = ddf['total_sales'].sum().compute()

end_time = time.time()

dask_time = end_time - start_time

print(f"Total sales over the period (Dask): ${total_sales:,.2f}")
print(f"Time taken with Dask: {dask_time:.2f} seconds")


# Pandas
start_time = time.time()

total_sales = 0
for month in range(1, 13):
    df = pd.read_csv(f"{directory_name}/sales_data_{month}.csv")
    df['total_sales'] = df['price'] * df['quantity']
    total_sales += df['total_sales'].sum()

end_time = time.time()

pandas_time = end_time - start_time

print(f"Total sales over the period (Pandas): ${total_sales:,.2f}")
print(f"Time taken with Pandas: {pandas_time:.2f} seconds")

# Compare times
time_difference = pandas_time / dask_time
if time_difference > 1:
    print(f"Dask was {time_difference:.2f} times faster")
else:
    print(f"Dask was {1 / time_difference:.2f} times slower")
```

---

## Parse

> Interpret and convert data from one format to another.

Source: https://dagster.io/glossary/data-parsing

### Data parsing definition:

*Data parsing* is the process of interpreting and converting data from one format to another. It involves taking data, typically structured in a specific manner such as CSV, JSON, XML, etc., and transforming it into a format that is more useful or easier to process and analyze. This process usually involves a set of rules or patterns to understand the input data and to determine how the transformation should occur. Data parsing is a common task in areas like data preprocessing, web scraping, and data integration.

### Data parsing example using Python:

Let's look at an example of parsing CSV (Comma-Separated Values) data, which is a common task in data analysis. Please note that you need to [have the 'csv' library installed in your Python environment](https://dagster.io/glossary) to run this code.

Assume we have a CSV file named `data.csv` with the following content:

```plaintext
Name,Age,Occupation
Alice,30,Engineer
Bob,25,Doctor
Charlie,35,Teacher
```

Here is a Python script that parses this file using the `csv` module. Please ensure that the 'data.csv' file is in the same directory as your Python script, or provide an absolute path to the file.

```python trailing-newline
import csv

def parse_csv_file(file_name):
    with open(file_name, 'r') as file:
        reader = csv.reader(file)
        header = next(reader)  # This will store the header row (['Name', 'Age', 'Occupation'])
        rows = []
        for row in reader:
            # Convert each row to a dictionary with keys as the header and values as the row values
            rows.append(dict(zip(header, row)))
        return rows

data = parse_csv_file('data.csv')

for person in data:
    print(f"Name: {person['Name']}, Age: {person['Age']}, Occupation: {person['Occupation']}")
```

This script first opens the file and creates a CSV reader. It then stores the header row (which contains the field names) and iterates over the rest of the rows, converting each one to a dictionary where the keys are the field names and the values are the corresponding values from the row. This way, you get a list of dictionaries where each dictionary represents a person.

Run the script, and your parsed data will show as follows:

```plaintext
Name: Alice, Age: 30, Occupation: Engineer
Name: Bob, Age: 25, Occupation: Doctor
Name: Charlie, Age: 35, Occupation: Teacher
```

---

## Partition

> Data partitioning is a technique that data engineers and ML engineers use to divide data into smaller subsets for improved performance.

Source: https://dagster.io/glossary/data-partitioning

### Data partitioning definition:

Partitioning is a technique that helps data engineers and ML engineers organize data and the computations that produce that data. Partitioning also makes data pipelines more performant and cost-efficient by letting them operate on subsets of data instead of all of it at once.

Below we share some starter examples on partitioning in Python. For more advanced information on partitioning, read [Partitions in Data Pipelines](https://dagster.io/blog/partitioned-data-pipelines).

### Partitioning considerations in data engineering:

If you are new to partitioning in data pipelines, here are some key things to consider:

- **Choose the appropriate partition key**: The partition key should be chosen based on the most common types of queries that will be performed on the data. A good partition key will ensure that the data is distributed evenly across partitions and minimize the need for shuffling data during query processing.
- **Avoid over-partitioning**: Over-partitioning can negatively impact query performance as it increases the number of small partitions that need to be processed. It can also result in increased storage requirements and complexity. A good rule of thumb is to limit the number of partitions to 10,000 or less (although systems like Dagster can support up to 25,000 partitions per asset, beyond which you will see performance degradation in the UI).
- **Use a consistent partitioning scheme**: Consistency in the partitioning scheme ensures that the same data is stored in the same partition across different runs of the pipeline. This can help to reduce the complexity of data processing and improve query performance.
- **Use partition pruning**: Most modern data processing systems support partition pruning, which allows queries to scan only the relevant partitions instead of scanning the entire dataset. This can significantly improve query performance, especially when dealing with large datasets.
- **Consider data skew**: Data skew can occur when the data is not evenly distributed across partitions, resulting in some partitions processing significantly more data than others. This can lead to longer query times for those partitions and can impact overall query performance. Techniques such as [data shuffling](https://dagster.io/glossary/data-shuffling) or using a different partition key can be used to mitigate data skew.
- **Monitor partition usage**: It is important to monitor partition usage to identify any hotspots or data skew issues that may impact query performance. Regularly analyzing the partition usage can help to identify opportunities for optimization and fine-tuning the partitioning scheme.
- **Plan for future growth**: As the data grows, the partitioning scheme may need to be adjusted to ensure optimal query performance. It is important to plan for future growth and be prepared to adjust the partitioning scheme as needed.

### A simple example of partitioning data in Python:

Let's consider a very simple example where we want to partition a list of integers into two partitions, say one for even numbers and one for odd numbers. Here's a simple Python script that does that:

```python
def partition_data(data):
    even_partition = []
    odd_partition = []

    for num in data:
        if num % 2 == 0:
            even_partition.append(num)
        else:
            odd_partition.append(num)

    return even_partition, odd_partition

# Now let's use the function with some data
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

even_partition, odd_partition = partition_data(data)

print("Even Partition: ", even_partition)
print("Odd Partition: ", odd_partition)
```

       When you run this script, it would print:

```plaintext
Even Partition:  [2, 4, 6, 8, 10]
Odd Partition:  [1, 3, 5, 7, 9]
```

So in effect we have partitioned our dataset into two, based on a business rule.  The sum of all of your partitions should constitute your entire dataset with no duplicate entries.  The most common form of partitioning would be by date (data partitioned by day, week, or month for example), but other partitioning approaches are also used.

### Partitioning using Parquet files in Pandas

Let's look at a slightly more advanced example using parquet and dataframes.  Pandas needs the pyarrow or fastparquet library to handle Parquet files. You can install it with pip (`pip install pyarrow` or `pip install fastparquet`).

*Please note that you need to* [*have other Python libraries like Pandas installed in your Python environment*](https://dagster.io/glossary) *to run this code.*

Here we will first write a parquet file to disk, then retrieve the same file before we partition it (yes, this is just an example, not a real life practice!)

```python
import pandas as pd

# Sample data
data = {
    'name': ['Thom Yorke', 'Jonny Greenwood', 'Ed OBrien', 'Philip Selway', 'Colin Greenwood'],
    'role': ['vocals', 'guitar', 'guitar', 'drums', 'bass'],
    'created_date': ['1968-10-07', '1971-10-05', '1968-04-15', '1967-05-23', '1969-06-26'],
}

# Create a DataFrame from the data
df = pd.DataFrame(data)

# Write the DataFrame to a Parquet file
df.to_parquet('radiohead.parquet')

# ------------------------------------------

# read data from parquet file
df = pd.read_parquet('radiohead.parquet')

# Convert 'created_date' column to datetime
df['created_date'] = pd.to_datetime(df['created_date'])

# Extract year and month from 'created_date' to a new column 'year_month'
df['year_month'] = df['created_date'].dt.to_period('M')

# Now group the dataframe by 'year_month'
grouped = df.groupby('year_month')

# Now each group in 'grouped' is a partition of customers created in the same month
for name, group in grouped:
    print(f"{name}:")
    print(group)
```

Running the script will partition the group Radiohead into four partitions based on the birth year of the band members:

```plaintext
1967:
            name   role created_date  year
3  Philip Selway  drums   1967-05-23  1967
1968:
         name    role created_date  year
0  Thom Yorke  vocals   1968-10-07  1968
2   Ed OBrien  guitar   1968-04-15  1968
1969:
              name  role created_date  year
4  Colin Greenwood  bass   1969-06-26  1969
1971:
              name    role created_date  year
1  Jonny Greenwood  guitar   1971-10-05  1971
```

---

## Pickle

> Convert a Python object into a byte stream for efficient storage.

Source: https://dagster.io/glossary/data-pickling

### Data pickling definition:

In Python programming, 'pickling' refers to the process of converting a Python object into a byte stream, which can be stored in a file or transferred across a network. This process is also known as serialization or marshaling.

The byte stream created during pickling contains all the information about the object's state, such as its variables, methods, and attributes. When the byte stream is unpickled, the object is reconstructed with all its original attributes and methods.

Pickling is a useful feature in Python as it allows you to save and retrieve complex data structures easily. For example, you can use pickling to store the state of a machine learning model or save the state of a game so that it can be resumed later.

### Data pickling example using Python:

Python provides the built-in `pickle` module, which provides functions to serialize and deserialize Python objects. The `dump` function is used to serialize the object to a file, and the `load` function is used to deserialize the object from the file.

Here is a simple example of using Python’s `pickle` library:

```javascript
import pickle

# Define a sample dictionary
data = {'name': 'John', 'age': 30, 'city': 'New York'}

# Serialize the dictionary using Pickle
with open('data.pickle', 'wb') as f:
    pickle.dump(data, f)

# Deserialize the dictionary from the Pickle file
with open('data.pickle', 'rb') as f:
    new_data = pickle.load(f)

# Print the deserialized dictionary
print(new_data)
```

---

## PostgreSQL

> Advanced, open-source object-relational database management system.

Source: https://dagster.io/glossary/postgresql

---

## Pre-aggregate

> See 'aggregate'.

Source: https://dagster.io/glossary/data-preaggregate

### Data preaggregation definition

Preaggregation is a specific type of aggregation that occurs before data is stored, aimed at optimizing query performance and reducing computational resources.

[      Jump to the entry for 'aggregate'.    ](https://dagster.io/glossary/data-aggregation)

In data engineering, **data aggregation** and **data pre-aggregation** are important techniques used to process and organize data, but they are applied at different stages and for different purposes. Here's a breakdown of the key differences and when to use each approach:

#### Quick recap of Data Aggregation vs. Pre-aggregation:

**Definition:**- **Data aggregation** is the process of summarizing and condensing data into a more manageable format at the time it is queried. It typically happens at runtime, when users or systems request specific insights from raw data.- Example: Summing up daily sales data to get monthly or yearly sales numbers.

#### Data Pre-Aggregation:

**Definition:**- **Data pre-aggregation** involves summarizing and condensing data into pre-defined aggregates **before** it is queried. This means aggregating data at predefined intervals or categories during data processing, rather than at query time.- Example: Pre-aggregating daily sales data into monthly or yearly sales figures during data loading or batch processing.

#### Quick comparison:

CharacteristicData AggregationData Pre-Aggregation**Timing**Happens at query time (on-demand)Happens before query time (precomputed)**Flexibility**High: Can aggregate in different ways dynamicallyLow: Limited to predefined aggregates**Performance/Latency**Potentially slower, especially for large datasetsFaster query performance (lower latency)**Granularity**Raw, detailed data remains availablePre-aggregated data; raw data may also be retained**Resource Usage**Requires more computational resources at runtimeReduces runtime computation, but increases storage needs**Query Types**Best for ad hoc and varied queriesBest for predictable, repetitive queries**Use Cases**Suitable for flexible reporting and analysisIdeal for dashboards and reports with known metrics**Scalability**May struggle with very large datasetsBetter suited for handling large datasets**Storage Requirements**Lower storage needs (raw data only)Higher storage needs (pre-aggregated and raw data)**Maintenance**Minimal, as raw data is queried directlyRequires maintaining pre-aggregated data in sync with updates

**When to Use Aggegation:**- When there is a need for **real-time flexibility** in how data is summarized or aggregated.- When data queries vary widely and cannot be predicted in advance.- When you have the necessary computational resources to handle on-demand aggregation without significantly impacting performance.- When the underlying dataset is not too large to cause major performance bottlenecks.

**When to Use Pre-aggregation:**- When performance and **low-latency queries** are critical, especially in dashboards, reports, or applications that rely on pre-defined insights.- When the types of queries are **predictable**, and you can anticipate what aggregates will be needed (e.g., daily, weekly, monthly summaries).- When dealing with **large datasets** where runtime aggregation would be too slow or computationally expensive.- In **batch processing scenarios** where data can be pre-processed in regular intervals, making it easier to aggregate large amounts of data ahead of time.

#### Best Practices:

1. **Use Pre-Aggregation for Performance-Critical Applications:**
2. If your application requires fast responses to queries and you know in advance which aggregations are needed, pre-aggregation is the way to go. This is common in business intelligence tools, dashboards, or where real-time performance is crucial.
3. **Use On-Demand Aggregation for Flexibility:**
4. For ad hoc querying where the aggregation needs might change frequently, it is better to rely on dynamic aggregation. This approach allows for more flexible exploration of the data without being limited by predefined summaries.
5. **Hybrid Approach:**
6. Often, a hybrid approach is best. For frequently queried aggregates, you can pre-aggregate data to improve performance. At the same time, you retain raw data so that less frequent or ad hoc queries can still be performed dynamically.
7. **Data Volume Considerations:**
8. Pre-aggregation makes sense when the volume of raw data is so large that aggregating on the fly would negatively impact performance. But if the data volume is manageable, on-demand aggregation can provide greater flexibility without major performance trade-offs.
9. **Storage and Maintenance:**
10. Pre-aggregating data will increase storage requirements because both raw data and pre-aggregated results may need to be retained. You should also account for the maintenance overhead of keeping pre-aggregated data in sync with raw data updates.

By carefully analyzing the performance requirements, the flexibility of query needs, and the data volume, you can decide when to use data aggregation or data pre-aggregation in your data engineering pipelines.

---

## Preprocess

> Transform raw data before data analysis or machine learning modeling.

Source: https://dagster.io/glossary/data-preprocessing

### Data preprocessing definition:

**Data Preprocessing** is a fundamental step in any data science or data engineering project.

Data preprocessing is a data mining technique that transforms raw data into an understandable and efficient format. This step typically takes place before the data analysis or machine learning modeling stages. Data preprocessing may involve several processes like:

- [**Data Cleaning**](https://dagster.io/glossary/data-cleaning): Removing noise and handling missing or inconsistent data. This step ensures that the quality of data is good enough for the subsequent stages.
- [**Data Transformation**](https://dagster.io/glossary/data-transformation): Normalization, aggregation, or general scaling of data to prepare it for the machine learning algorithms.
- [**Data Reduction**](https://dagster.io/glossary/data-reduction): Reducing the volume but producing the same or similar analytical results.
- [**Data Discretization**](https://dagster.io/glossary/data-discretization): The process of converting continuous data to categorical data.
- [**Feature Extraction**](https://dagster.io/glossary/feature-extraction) or [**Feature Selection**](https://dagster.io/glossary/feature-selection): The process of creating new features or modifying existing features to improve the performance of machine learning models.

### A simple example of data preprocessing using Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Here is a simple example of text preprocessing. This script uses NLTK and re (regular expressions) to preprocess a simple text string. The preprocessing tasks include:

1. Text lowercase
2. Remove numbers
3. Remove punctuation
4. Remove whitespaces
5. Remove stopwords
6. Stem (reduce words to their root form.)

```python
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

nltk.download('punkt')
nltk.download('stopwords')

# Define the text string
text_string = "Rein in the chaos and maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any of 100 issues and identify performance improvement opportunities."

# Step 1: Convert text to lowercase
text_string = text_string.lower()

# Step 2: Remove numbers
text_string = re.sub(r'\d+', '', text_string)

# Step 3: Remove punctuation
text_string = re.sub(r'[^\w\s]', '', text_string)

# Step 4: Remove whitespaces
text_string = text_string.strip()

# Step 5: Remove stopwords
stop_words = set(stopwords.words('english'))
tokens = nltk.word_tokenize(text_string)
tokens = [token for token in tokens if token not in stop_words]

# Step 6: Stemming
porter = PorterStemmer()
stemmed = [porter.stem(word) for word in tokens]

print(f"Original Text: {text_string}")
print("Preprocessed Text: ", ' '.join(stemmed))
```

If you execute this code you will see the following output:

```javascript
Original Text: rein in the chaos and maintain control over your data as the complexity scales centralize your metadata in one tool with builtin observability diagnostics cataloging and lineage spot any of  issues and identify performance improvement opportunities
Preprocessed Text:  rein chao maintain control data complex scale central metadata one tool builtin observ diagnost catalog lineag spot issu identifi perform improv opportun
```

### Data preprocessing vs. Data Preparation:

Data preparation is a broader term than data preprocessing that includes all activities to construct and consolidate a dataset for analysis or operational use. This can involve:

- **Data Acquisition**: Collecting or integrating data from various sources.
- **Data Integration**: Combining data from different sources and providing users with a unified view of the data.
- **Data Preprocessing**: As described above, which includes cleaning, transformation, reduction, etc.
- **Data Partitioning**: Splitting the data into training, validation, and test sets for machine learning models.

In essence, data preprocessing can be seen as a subset of data preparation. While data preparation encompasses the entire journey from raw data to the ready-for-analysis or operational-use data, data preprocessing is specifically concerned with refining and transforming the data after it's been acquired and integrated, and before it's analyzed or fed into a machine learning model. Both steps are critical to ensuring the accuracy, reliability, and efficiency of the downstream processes and outcomes.

---

## Primary Key

> A unique identifier for a record in a database table that helps maintain data integrity.

Source: https://dagster.io/glossary/primary-key

### What is a Primary Key in Data Engineering?

A **Primary Key** is a unique identifier for a record in a database table. It is a specific field (or a combination of fields) **that ensures each record is distinct, preventing duplicate entries**. The Primary Key is crucial for maintaining data integrity, enabling efficient data retrieval, and establishing relationships between tables in relational databases.

In this Glossary entry:

- [What is a Primary Key in Data Engineering?](#)
- [SQL Example: Using a Primary Key to Define a Table:](#)
- [Key Functions and Benefits of Using Primary Keys](#)
- [Best Practices in Working with Primary Keys](#)
- [How Dagster helps manage primary keys](#)
- [Dagster Code Example](#)

### SQL Example: Using a Primary Key to Define a Table:

```sql
CREATE TABLE Users (
    UserID INT PRIMARY KEY NOT NULL,         -- Primary Key with NOT NULL constraint
    Username VARCHAR(50) NOT NULL UNIQUE,
    Email VARCHAR(100) NOT NULL UNIQUE,
    CreatedAt TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
```

       This `Users` table would look like this:

**UserIDUsernameEmailCreatedAt**1dagsterdagster\@glossary.com2024-08-19 10:15:002airbyteairbyte\@glossary.com2024-08-19 10:30:003cubecube\@glossary.com2024-08-19 11:00:004GXgx\@glossary.com2024-08-19 11:45:00

As the Primary Key, the column **`UserID`:** contains unique identifiers for each user (1, 2, 3, 4).The `UserID` column serves as the primary key, ensuring that each row in the table is uniquely identifiable.

### Key Functions and Benefits of Using Primary Keys:

Primary Keys are required to deliver several critical features of modern databases:

**Uniqueness and Data Integrity**: The primary function of a Primary Key is to enforce the uniqueness of records in a table, preventing duplicate records and, in turn, data inconsistencies, errors in data processing, and challenges in data management.

**Efficient Data Retrieval**: Primary Keys significantly enhance the efficiency of data retrieval operations in large datasets. Database systems use Primary Keys to quickly locate and retrieve data without scanning the entire table.

**Relationships Between Tables**: In relational databases, tables are linked through relationships, such as one-to-one, one-to-many, or many-to-many relationships. The Primary Key in one table can be referenced as a Foreign Key in another table, establishing a link between the two tables. This relationship allows for complex queries that can combine data from multiple tables, enabling a more comprehensive analysis and reporting.

**Referential Integrity**: By using Primary Keys and Foreign Keys, databases enforce referential integrity, ensuring that relationships between tables are consistent. For example, a Foreign Key in one table must match a Primary Key in another table, preventing orphaned records or broken links between related data. This integrity is crucial for maintaining accurate and reliable data.

**Indexing and Performance Optimization**: Most database management systems automatically create an index on the Primary Key column(s). Indexing improves the speed of query operations, especially for search, sort, and join operations that involve the Primary Key. As a result, databases can handle large volumes of data more effectively, providing faster response times and better performance.

**Consistency in Data Models**: In the broader context of data engineering, the use of Primary Keys contributes to the consistency and standardization of data models. By defining clear, unique identifiers for each table, data engineers can create more structured and predictable data architectures, facilitating data integration, migration, and scalability across systems.

### Best Practices in Working with Primary Keys:

When selecting a Primary Key, it is important to choose a column (or combination of columns) that will *always* have a unique value for each record. Common choices include unique IDs, serial numbers, or other natural identifiers like email addresses. In cases where a natural unique identifier does not exist, a surrogate key, often an auto-incrementing number, is used.

In summary, Primary Keys are essential for maintaining the structure, integrity, and efficiency of relational databases in data engineering. They ensure that each record is unique, enable quick and reliable data retrieval, and support the complex relationships and operations that are central to data-driven applications.

### Databases without Primary Keys

So, is it conceivable to have a database that does not use Primary Keys?  Surprisingly, the answer is yes.

It is worth noting that modern DBMSs like MySQL, PostgreSQL, SQL Server, Oracle, etc. do allow you to create tables without primary keys, but it’s generally not advised for the many reasons mentioned above.

NoSQL databases like MongoDB do not strictly require a primary key, although they automatically create a unique identifier for each document (\_id field in MongoDB).In Key-Value Stores (e.g., Redis), the key itself acts as a unique identifier for the value, somewhat analogous to a primary key, but the concept differs from traditional relational databases.Column Stores (e.g., Cassandra) allow for more flexible schema designs, and while you can define a partition key, it doesn’t function the same way as a traditional primary key.

As for flat-file Databases (opening the age-old debate of "Is Excel a database?"), we should note that CSV Files, Excel Sheets, etc. don’t inherently have primary keys. However, users often designate a column manually to act as a unique identifier.

### How Dagster Helps Manage Primary Keys

Dagster provides a robust framework for data orchestration. Database interactions in a data pipeline benefit from proper management of primary keys. Here's how Dagster helps in this context:

**Data Quality and Validation**: Dagster allows data engineers to implement data quality checks within their pipelines. This includes validating primary keys to ensure they are unique and not null, which is crucial for maintaining data integrity.

**Asset-Oriented Approach**: Dagster's asset-based model allows data engineers to define and manage data assets explicitly. When dealing with primary keys, this means you can create assets that represent tables or datasets with primary keys, ensuring that the relationships and constraints are well-defined and maintained.

**Traceability and Lineage**: Dagster provides features for tracking the lineage of data assets. This helps data engineers trace how primary keys are used and transformed across different stages of the data pipeline. Understanding the lineage of primary keys can be critical for debugging and ensuring data consistency.

**Version Control**: By storing SQL queries and transformations as code, Dagster enables version control for database operations. This includes operations involving primary keys, allowing data engineers to track changes and revert to previous versions if necessary.

**Integration with Databases**: Dagster integrates seamlessly with various databases and data warehouses. Data engineers can use Dagster to orchestrate tasks that involve creating, updating, or validating primary keys in these databases.

**Scheduling and Automation**: Dagster's scheduling capabilities allow data engineers to automate tasks that involve primary keys. For example, you can schedule regular checks to ensure primary keys are unique and consistent across your datasets.

**Error Handling and Retries**: Dagster's execution engine handles errors and retries, which is useful when dealing with primary keys. If a task fails due to a primary key constraint violation, Dagster can retry the task or alert the data engineer to address the issue.

By leveraging these features, data engineers can ensure that Primary Keys are correctly managed and maintained throughout their data pipelines, leading to more reliable and consistent data operations.

#### Dagster Code Example:

Let's build a simple step-by-step workflow to illustrate all this:

![](https://dagster.io/images/glossary/681399f654933b29e12fbcbe_primay-key-asset-min.png)

   A single Dagster asset to illustrate the concept of Primary Keys.  

```python
from dagster import asset, IOManager, io_manager, OutputContext, InputContext, Definitions
import sqlite3

class MyDatabaseIOManager(IOManager):
    def __init__(self, db_path):
        self.db_path = db_path

    def _connect(self):
        return sqlite3.connect(self.db_path)

    def handle_output(self, context: OutputContext, obj):
        conn = self._connect()
        cursor = conn.cursor()

        # Assuming obj is a list of dictionaries representing rows
        table_name = context.asset_key.path[-1]
        columns = obj[0].keys()
        placeholders = ', '.join('?' * len(columns))
        column_names = ', '.join(columns)

        # Create table if it doesn't exist
        cursor.execute(f"""
        CREATE TABLE IF NOT EXISTS {table_name} (
            id INTEGER PRIMARY KEY,
            {', '.join([f'{col} TEXT' for col in columns if col != 'id'])}
        )
        """)

        # Insert or update rows
        for row in obj:
            values = tuple(row[col] for col in columns)
            cursor.execute(f"""
            INSERT INTO {table_name} ({column_names})
            VALUES ({placeholders})
            ON CONFLICT(id) DO UPDATE SET
            {', '.join([f'{col}=excluded.{col}' for col in columns if col != 'id'])}
            """, values)

        conn.commit()
        conn.close()

    def load_input(self, context: InputContext):
        conn = self._connect()
        cursor = conn.cursor()

        table_name = context.asset_key.path[-1]
        cursor.execute(f"SELECT * FROM {table_name}")
        rows = cursor.fetchall()

        conn.close()
        return rows

@io_manager
def my_database_io_manager():
    return MyDatabaseIOManager(db_path='my_database.db')

@asset(io_manager_key="my_database_io_manager", description="Creating an asset with primary key management.")
def my_data_asset():
    # Example data with primary key 'id'
    data = [
        {'id': 1, 'name': 'Alice', 'email': 'alice@example.com'},
        {'id': 2, 'name': 'Bob', 'email': 'bob@example.com'},
        {'id': 3, 'name': 'Charlie', 'email': 'charlie@example.com'}
    ]
    return data

defs = Definitions(
    assets=[my_data_asset],
    resources={
        "my_database_io_manager": my_database_io_manager
    }
)
```

This Dagster code provides a basic illustration of how to manage Primary Keys and use [IO Managers](https://docs.dagster.io/guides/build/io-managers) to handle data storage and retrieval within a data pipeline.

Here is what the code does, in a nutshell:

1. **Asset Execution**:
2. When `my_data_asset` is executed, it generates the example data.
3. The data is passed to the `handle_output` method of `MyDatabaseIOManager`.
4. **Data Storage**:
5. The `handle_output` method connects to the SQLite database.
6. It creates the table if it doesn't exist, with `id` as the primary key.
7. It inserts or updates rows in the table based on the `id` column.
8. **Data Retrieval**:
9. When data needs to be read from the database, the `load_input` method is called.
10. It connects to the SQLite database, retrieves all rows from the table, and returns them.

Let's break down the key components:

##### Dagster Components and Their Roles

1. **Custom IO Manager (`MyDatabaseIOManager`)**:
2. This class is responsible for managing the connection to a database or external resource (in this case SQLite) and handling the input and output operations for assets.
3. **Initialization**: The `__init__` method initializes the IO Manager with the path to the SQLite database.
4. **Connection Handling**: The `_connect` method establishes a connection to the database.
5. **Output Handling (`handle_output`)**:

   - This method takes the output data (assumed to be a list of dictionaries) and writes it to the database.
   - It creates a table if it doesn't exist, with an `id` column as the primary key.
   - It uses an `INSERT INTO ... ON CONFLICT(id) DO UPDATE` SQL statement to handle upserts, ensuring that if a row with the same primary key (`id`) already exists, it will be updated instead of inserted.
6. **Input Handling (`load_input`)**:

   - This method reads data from the database table and returns it.
7. **IO Manager Definition (`my_database_io_manager`)**:
8. This function returns an instance of `MyDatabaseIOManager` with the specified database path.
9. **Asset Definition (`my_data_asset`)**:
10. This asset generates example data with a primary key (`id`) and uses the custom IO Manager for storage.
11. The data is a list of dictionaries, each representing a row with an `id`, `name`, and `email`.
12. **Definitions (`defs`)**:
13. This object ties together the asset and the IO Manager, making them available for execution within the Dagster framework.

##### What our Example Illustrates Regarding Primary Keys:

1. **Primary Key Management**:
2. The `handle_output` method in `MyDatabaseIOManager` ensures that the `id` column is treated as the primary key.
3. The SQL statement `INSERT INTO ... ON CONFLICT(id) DO UPDATE` ensures that if a row with the same `id` already exists, it will be updated rather than inserted.
4. **IO Manager Usage**:
5. The custom IO Manager (`MyDatabaseIOManager`) abstracts away the details of how data is stored and retrieved from the SQLite database.
6. The `handle_output` method handles the logic for writing data to the database, including creating the table if it doesn't exist and performing upserts.
7. The `load_input` method handles the logic for reading data from the database, making it available for downstream assets or operations.

---

## Protobuf (Protocol Buffers)

> Developed by Google, it is a method developed to serialize structured data, like XML and JSON. It is both simpler and more efficient than both XML and JSON. Protobuf is language-agnostic, making it highly versatile for different systems.

Source: https://dagster.io/glossary/protobuf-protocol-buffers

---

## Purge

> Delete data that is no longer needed or relevant to free up storage space.

Source: https://dagster.io/glossary/data-purging

### Data purging definition:

Data purging is the process of permanently deleting data from a system or database. This process is

### Data purging example using Python:

Please note that you need to [have Pandas installed in your Python environment](https://dagster.io/glossary) to run this code.

In Python, data purging can be achieved using various libraries and frameworks. For example, the following code demonstrates how to use the Pandas library to delete rows containing null values in a DataFrame:

```python trailing-newline
import pandas as pd

# create a sample DataFrame
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'],
        'age': [25, 30, None, 40],
        'city': ['New York', 'Paris', None, 'London']}
df = pd.DataFrame(data)

# remove rows with null values
df = df.dropna()

# print the updated DataFrame
print(df)
```

In the above example, the `dropna()` method is used to remove all rows with `null` values in the DataFrame. This method removes any row that contains at least one null value. The resulting DataFrame will only contain the rows that have complete data.

The above example would purge the record for `Charlie` and yield the output:

```plaintext trailing-newline
    name   age      city
0  Alice  25.0  New York
1    Bob  30.0     Paris
3  David  40.0    London
```

It is important to note that data purging should be done with caution, as it is a permanent process and may lead to the loss of valuable information. It is recommended to backup the data before purging and to carefully consider the retention policy for the data.

---

## PyTorch

> An open-source machine learning library for Python, developed by Facebook’s AI Research lab.

Source: https://dagster.io/glossary/pytorch
Further reading: https://pytorch.org/

---

## Race Condition

> Handling conflicts when accessing a shared resource.

Source: https://dagster.io/glossary/race-condition

- [Race Condition Definition](#)
- [How Dagster Helps Avoid Race Conditions](#)
- [Race Conditions: An Example in Python](#)

### Race Condition Definition:

In data engineering, a "race condition" refers to a situation where the behavior of a system or application depends on the sequence or timing of uncontrollable events such as the order in which threads or processes execute. This can lead to unpredictable and erroneous outcomes because the system does not properly handle the concurrency of operations.

Here are some key aspects of race conditions in data engineering:

1. **Concurrency Issues**: Race conditions often occur in concurrent systems where multiple processes or threads access shared resources (such as databases, files, or variables) simultaneously. If these accesses are not properly synchronized, inconsistent or incorrect data can result.
2. **Example Scenario**: Consider a database system where two users attempt to update the same record at the same time. If the system does not handle these concurrent updates correctly, one update might overwrite the other, leading to data loss or corruption.
3. **Critical Sections**: Race conditions typically involve critical sections of code where shared resources are accessed. Without proper locking mechanisms (like mutexes or semaphores), multiple threads might enter a critical section simultaneously, causing race conditions.
4. **Impact**: The impact of race conditions can range from minor bugs to significant data corruption, security vulnerabilities, or system crashes. In data engineering, this can mean incorrect data analytics, faulty data processing pipelines, and unreliable data systems.
5. **Prevention**: Preventing race conditions involves proper synchronization techniques, such as:
6. **Locks**: Using locks to ensure that only one thread can access a critical section at a time.
7. **Atomic Operations**: Utilizing atomic operations that are completed in a single step, preventing interruptions.
8. **Transaction Management**: Implementing transaction management in databases to ensure consistency and isolation of concurrent operations.
9. **Thread-safe Data Structures**: Using data structures designed to handle concurrent access safely.

By understanding and mitigating race conditions, data engineers can ensure the reliability, consistency, and accuracy of their data systems and applications.

### How Dagster Helps Avoid Race Conditions

As the leading data orchestration solution, Dagster helps avoid race conditions through several key features and design principles:

1. **Resource Management**:

Dagster allows you to define resources that your pipelines depend on, such as database connections, APIs, or compute resources. By managing these resources explicitly, you can control access and usage, preventing multiple processes from accessing the same resource simultaneously in an uncontrolled manner.

1. **Concurrency Control**:

Dagster provides mechanisms to limit concurrency within your pipelines. For example, you can configure job concurrency to ensure that only a certain number of jobs run simultaneously, which helps prevent race conditions by reducing the chances of multiple jobs competing for the same resources.

1. **Partitioning**:

Dagster supports partitions and partition sets, which allow you to slice your dataset by time or other dimensions. This can help in organizing and executing batch computations in a way that avoids race conditions, as each partition can be processed independently.

1. **Federated Architecture**:

Dagster’s architecture enables teams to isolate their pipelines, which helps avoid issues like starvation, monolithic dependencies, and coupled failures.

1. **Observability and Monitoring**:

Dagster offers detailed insights into pipeline runs, including logs and execution timing. This observability helps in identifying and diagnosing race conditions quickly, allowing you to take corrective actions before they cause significant issues.

By leveraging these features, Dagster provides a robust framework for managing data pipelines in a way that minimizes the risk of race conditions, ensuring reliable and consistent execution of your data workflows.

#### Race Conditions: An Example in Python

A race condition occurs when two or more threads access shared data and try to change it at the same time. In the context of data engineering, this often happens when multiple processes attempt to read from and write to the same file simultaneously, leading to unpredictable results and potential data corruption.

Below is a Python example demonstrating a race condition using threading. The example involves two threads attempting to read from and write to the same file.

##### Python Example of Race Condition with Threads

1. **Setup:** First, let's create a sample file to work with.
2. **Threads:** We will create two threads: one for reading from the file and one for writing to the file.
3. **Race Condition:** Due to the lack of synchronization, the two threads will likely interfere with each other.

###### Setup

Create a sample file named `data.txt` with some initial content.

```javascript
## Create a sample file with initial content
with open('data.txt', 'w') as f:
    f.write("Initial Content\n")
```

###### Threads with Race Condition

Here is the code to demonstrate the race condition:

```python
import threading
import time

## Function to write to the file
def write_to_file():
    for i in range(5):
        with open('data.txt', 'a') as f:
            f.write(f"Writing line {i}\n")
        time.sleep(0.1)

## Function to read from the file
def read_from_file():
    for i in range(5):
        with open('data.txt', 'r') as f:
            print(f.read())
        time.sleep(0.1)

## Create threads for writing and reading
writer_thread = threading.Thread(target=write_to_file)
reader_thread = threading.Thread(target=read_from_file)

## Start threads
writer_thread.start()
reader_thread.start()

## Wait for threads to complete
writer_thread.join()
reader_thread.join()
```

###### Explanation

- **write\_to\_file:** This function opens the file in append mode (`'a'`) and writes a line to it. It then sleeps for 0.1 seconds to simulate processing time.
- **read\_from\_file:** This function opens the file in read mode (`'r'`) and prints its contents. It also sleeps for 0.1 seconds.

When the two threads run simultaneously, you might see inconsistent output due to the race condition. For example, the read operation might happen while the file is being written to, leading to partial reads or other unexpected behaviors.

###### Expected Output

The output may vary with each run, but an example output might look like this:

```plaintext
Initial Content
Writing line 0
Writing line 0
Writing line 0
Initial Content
Writing line 0
Initial Content
Writing line 0
Writing line 1
...
```

The actual output will vary, illustrating the unpredictable nature of race conditions. You might see partial reads or mixed content because the threads are not synchronized.

##### Resolving the Race Condition

To resolve this race condition, you can use thread synchronization mechanisms such as locks.

Here is the modified code with a lock:

```python
import threading
import time

## Create a lock object
lock = threading.Lock()

## Function to write to the file with a lock
def write_to_file():
    for i in range(5):
        with lock:
            with open('data.txt', 'a') as f:
                f.write(f"Writing line {i}\n")
        time.sleep(0.1)

## Function to read from the file with a lock
def read_from_file():
    for i in range(5):
        with lock:
            with open('data.txt', 'r') as f:
                print(f.read())
        time.sleep(0.1)

## Create threads for writing and reading
writer_thread = threading.Thread(target=write_to_file)
reader_thread = threading.Thread(target=read_from_file)

## Start threads
writer_thread.start()
reader_thread.start()

## Wait for threads to complete
writer_thread.join()
reader_thread.join()
```

In this version, the `lock` ensures that only one thread can access the file at a time, thus avoiding the race condition. This will result in consistent and predictable output as follows:

```plaintext
Writing line 0

Writing line 0
Writing line 1

Writing line 0
Writing line 1
Writing line 2

Writing line 0
Writing line 1
Writing line 2
Writing line 3

Writing line 0
Writing line 1
Writing line 2
Writing line 3
Writing line 4
```

---

## Rebalance

> Redistributing data across nodes or partitions for optimal performance.

Source: https://dagster.io/glossary/data-rebalancing

### Data rebalancing definition:

Data rebalancing refers to the process of **redistributing data across nodes or partitions in a distributed system to ensure optimal utilization of resources and balanced load**. As data is added, removed, or updated, or as nodes are added or removed, imbalances can emerge, which might lead to hotspots (some nodes being heavily used while others are under-utilized) or inefficient data access patterns.

### Why is data rebalancing important?

1. **Performance Optimization**: Without rebalancing, some nodes can become overloaded while others are under-utilized, leading to performance bottlenecks.
2. **Fault Tolerance**: In distributed storage systems like Hadoop's HDFS or Apache Kafka, data is often replicated across multiple nodes for fault tolerance. Proper rebalancing ensures that data replicas are well-distributed, enhancing the system's resilience to node failures.
3. **Scalability**: As a cluster grows or shrinks, rebalancing helps in efficiently integrating new nodes or decommissioning old ones.
4. **Storage Efficiency**: Ensuring data is evenly distributed helps in making the best use of available storage capacity across the cluster.

### How is data rebalancing done?

1. **Manual Rebalancing**: Some systems offer tools or commands that allow administrators to manually trigger a rebalancing operation.
2. **Automatic Rebalancing**: Some distributed systems automatically detect imbalances and trigger rebalancing without human intervention.
3. **Consistent Hashing**: This is a technique often used in distributed caching systems (like Cassandra). Instead of directly hashing an item to a specific node, the item is hashed to a point on a continuum or "ring". As nodes are added or removed, only a fraction of the keys need to be remapped and moved, making rebalancing more efficient.

### Examples in distributed systems:

![](https://dagster.io/images/glossary/681399f654933b29e12fbcbc_rebalance-kafka-min.png)

Apache Kafka: Kafka is a distributed streaming platform where data is stored in topics that are split into partitions. These partitions are distributed across different [broker nodes](https://www.youtube.com/watch?v=jHnyBSUVcOU\&t=2s). As data grows, or when brokers are added or removed, Kafka might need to rebalance these partitions for load distribution.    

![](https://dagster.io/images/glossary/681399f654933b29e12fbb73_rebalance-hadoop-min.png)

Hadoop's HDFS: Hadoop’s distributed filesystem creates replicas of data blocks. When new nodes are added, or data grows disproportionately, HDFS can redistribute blocks to ensure that each node in the cluster is utilized effectively.    

![](https://dagster.io/images/glossary/681399f654933b29e12fbb8f_rebalance-casandra-mongo-min.png)

Distributed Databases: Systems like [Cassandra](https://cassandra.apache.org/_/index.html) or [MongoDB](https://www.mongodb.com/docs/manual/core/sharding-balancer-administration/#std-label-sharding-balancing) may also need to rebalance data across \[sharded clusters]\(/glossary/shard) when the data distribution becomes uneven or when nodes are added or removed.        

![](https://dagster.io/images/glossary/681399f654933b29e12fbb3f_rebalance-glusterfs-min.png)

GlusterFS: GlusterFS supports adding new bricks (storage units) to a volume. When this happens, [data might need to be rebalanced across the bricks](https://docs.gluster.org/en/main/Administrator-Guide/Managing-Volumes/#shrinking-volumes).        

![](https://dagster.io/images/glossary/681399f654933b29e12fbcbf_rebalance-ceph-min.png)

Ceph: As an object storage system, Ceph uses [the CRUSH algorithm and CRUSH maps](https://docs.ceph.com/en/quincy/rados/operations/crush-map/) to decide where to store data. When new nodes or storage devices are added, Ceph may rebalance data to ensure efficient utilization.        

![](https://dagster.io/images/glossary/681399f654933b29e12fbc11_rebalance-openstack-min.png)

Swift: In OpenStack Swift, data is stored as replicas across nodes. When a new node is added or an existing one fails, [Swift rebalances the data to ensure the desired replication factor is maintained](https://platform.swiftstack.com/docs/introduction/openstack_swift.html#building-a-ring).        

![](https://dagster.io/images/glossary/681399f654933b29e12fbc80_rebalance-riak-min.png)

Riak: Riak also [requires rebalancing](https://docs.riak.com/riak/kv/latest/setup/planning/best-practices/index.html#disk-leeway) when adding or removing nodes from the cluster. It redistributes the data to maintain an even distribution.        

![](https://dagster.io/images/glossary/681399f654933b29e12fbba7_rebalance-cockroach-min.png)

CockroachDB: As a distributed SQL database, [CockroachDB automatically rebalances data](https://catalog.redhat.com/software/container-stacks/detail/5e9872712989e6a90307acd6#:) as nodes are added or removed to ensure that each node has roughly the same amount of data.        

![](https://dagster.io/images/glossary/681399f654933b29e12fbbdb_rebalance-minio-min.png)

MinIO: MinIO's erasure-coded setup can be expanded by adding new nodes. When this occurs, [data might need to be rebalanced](https://blog.min.io/minio-adds-manual-rebalancing) to make use of the new storage capacity.        

### The role of data orchestration in data rebalancing

**Data Orchestration in Data Rebalancing**

Data orchestration plays a pivotal role in data rebalancing by automating, coordinating, and optimizing the data movement processes across disparate storage resources. It is responsible for making decisions on when to rebalance data, which data to move, and where to place it. This involves:

1. **Monitoring Workloads**: By continually monitoring data access patterns and node workloads, orchestration systems can detect imbalances early and respond proactively.
2. **Intelligent Movement**: Instead of rudimentary data transfers, orchestration ensures data is moved intelligently, considering factors like network bandwidth, storage capacity, and anticipated future workloads.
3. **Minimizing Disruption**: A key aspect of data orchestration is to perform rebalancing with minimal disruption to ongoing operations. This requires smart algorithms and strategies to transfer data without significantly affecting system performance or availability.
4. **Automation and Policies**: Data orchestration tools allow administrators to set policies for rebalancing, ensuring that the system adheres to predefined guidelines about data locality, redundancy, and other factors. Automation aids in minimizing manual intervention, thus reducing potential human-induced errors.
5. **Fault Tolerance and Recovery**: In case of node failures, data orchestration plays a role in restoring data distribution by triggering rebalancing actions, ensuring that the system maintains its resilience and service levels.

In essence, data orchestration provides the tools and frameworks needed to carry out rebalancing seamlessly. As data grows and systems evolve, data orchestration becomes the linchpin, ensuring that data is always optimally placed, accessible, and resilient.

For many platforms the cloud providers (such as Amazon or Google) or the technology platform itself will handle data distribution and rebalancing for us behind the scenes in their storage services, abstracting those details from the end-users.  In other circumstances where we want greater control, we can orchestrate rebalancing tasks ourselves.

### Challenges:

1. **Service Impact**: Rebalancing can be resource-intensive, which might impact the performance of the system during the operation.
2. **Data Transfer Costs**: Especially in cloud environments, transferring large amounts of data during rebalancing can lead to increased costs.
3. **Complexity**: Rebalancing algorithms need to be efficient, and ensuring optimal distribution without frequent unnecessary data movement can be complex.

### An example of data rebalancing in Python:

To keep our example simple we will simulate nodes on a network using a basic local program. We'll represent each node as a list in Python, and our distributed system will be a list of these nodes (lists).

Suppose we have a distributed system with multiple nodes that store data.  Over time, data can be added to these nodes, leading to imbalances. Our goal is to rebalance the data such that each node has an approximately equal number of data items.

Certainly! Let's build an example that delves into how a distributed key-value store with hash partitioning can be rebalanced when a node is added.

1. **Initialization**:
2. We start by creating a basic distributed key-value store with a single node, then we add nodes and fresh data.
3. Each node will be responsible for a range of hash values.
4. Each key-value pair added to the store is hashed and directed to a node based on its hash.
5. **Rebalancing**:
6. When a new node is added, the system will need to redistribute keys so that each node still handles roughly the same amount of data.
7. We'll compute which keys need to move to the new node and then transfer them.
8. **Hashing**:
9. We'll use a simple modulo-based hash partitioning for this example.

Let's get started:

```python
import hashlib


class Node:
    def __init__(self, node_id):
        self.node_id = node_id
        self.data = {}

    def __repr__(self):
        return f"Node({self.node_id})"


class KeyValueStore:
    def __init__(self):
        self.nodes = []
        self.node_count = 0

    def add_node(self):
        new_node = Node(self.node_count)
        self.nodes.append(new_node)
        self.node_count += 1
        self.rebalance()

    def hash_key(self, key):
        hashed = int(hashlib.md5(key.encode()).hexdigest(), 16)
        return hashed % self.node_count

    def insert(self, key, value):
        node_idx = self.hash_key(key)
        self.nodes[node_idx].data[key] = value

    def add_data(self, grouping, count):
        for i in range(count):
            key= f"key{grouping}{i}"
            node_idx = self.hash_key(key)
            self.nodes[node_idx].data[key] = f"value{grouping}{i}"

    def get(self, key):
        node_idx = self.hash_key(key)
        return self.nodes[node_idx].data.get(key, None)

    def rebalance(self):
        if self.node_count <= 1:
            return

        # Identify keys that need to move
        for node in self.nodes[:-1]:  # Exclude the last node (new one)
            keys_to_move = [k for k in node.data if self.hash_key(k) != node.node_id]

            for k in keys_to_move:
                value = node.data.pop(k)
                self.insert(k, value)


store = KeyValueStore()

# Inserting some data
store.add_node()
store.add_data("A", 15000)

print("\nStep 1: Data distribution with a single node:")
for node in store.nodes:
    print(node, len(node.data))

# Adding a new node and triggering rebalancing
store.add_node()


# Display nodes and their data
print("\nStep 2: Data distribution with an added node:")
for node in store.nodes:
    print(node, len(node.data))

# Inserting some data
store.add_node()
print("\nStep 3: Data distribution with an added node:")
for node in store.nodes:
    print(node, len(node.data))

store.add_data("B", 5000)

# Display nodes and their data
print("\nStep 4: Data distribution with new data and an extra node added:")
for node in store.nodes:
    print(node, len(node.data))

print("\nStep 5: Rebalancing with no changes")
store.rebalance()
for node in store.nodes:
    print(node, len(node.data))
```

       In this example:

This example is an oversimplification, and real-world data rebalancing would involve a lot more complexities, including considerations for data locality, fault tolerance, minimizing data transfers, and dealing with real-time data access needs. However, this example should provide a basic idea of what data rebalancing means in the context of distributed systems.

The output will look something like this:

```plaintext
Step 1: Data distribution with a single node:
Node(0) 15000

Step 2: Data distribution with an added node:
Node(0) 7555
Node(1) 7445

Step 3: Data distribution with an added node:
Node(0) 4998
Node(1) 4978
Node(2) 5024

Step 4: Data distribution with new data and an extra node added:
Node(0) 6628
Node(1) 6671
Node(2) 6701

Step 5: Rebalancing with no changes
Node(0) 6628
Node(1) 6671
Node(2) 6701
```

Note that when rebalancing, the distribution of the data across nodes is not perfectly equal, and we would expect a certain amount of variance.Also note that in step 5 no rebalancing takes place.  Remember that rebalancing data on large distributed systems is expensive, so we need to only rebalance the distributed system when there is significant business benefit in doing so.

In conclusion, data rebalancing is a crucial aspect of maintaining the health, performance, and resilience of distributed systems in data engineering. Proper tools and strategies are needed to execute rebalancing efficiently and with minimal service impact.

---

## Reduce

> Convert a large set of data into a smaller, more manageable form without significant loss of information.

Source: https://dagster.io/glossary/data-reduction

#### Data reduction definition:

Data Reduction in data engineering refers to the process of transforming, encoding, or otherwise converting a large set of data into a smaller, more manageable form without significant loss of information. This is usually done to increase efficiency, reduce data storage requirements, and improve the speed of subsequent data processing tasks.

Methods for data reduction can include dimensionality reduction techniques, data compression, data sampling, binning, feature selection, feature extraction, and aggregation, among others. The aim is to maintain the data's integrity and usefulness while making it more manageable.

Data reduction is especially crucial in big data applications, where the sheer volume of data can become a hindrance to data processing and analysis. By intelligently reducing data size, we can optimize storage, improve performance, and make the data more understandable.

#### Data reduction example in Python:

*Please note that you need to* [*have the necessary Python libraries installed in your Python environment*](https://dagster.io/glossary) *to run this code.*

Let's look at a simple data reduction method by applying a PCA (Principal Component Analysis) technique to a sample dataset. In a modern data pipeline, we often need to reduce the dimension of data, especially for high-dimensional data, to save storage and computational power.

We'll be using the famous Iris dataset from sklearn for the demonstration. This data set has four features, and we'll reduce it to two features using PCA.

Here's an example:

```python
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

## Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target

## Original dataset dimensions
print(f"Original dataset shape: {X.shape}")

## Create the PCA object, specifying the number of components (or dimensions) to reduce to
pca = PCA(n_components=2)

## Fit and apply the PCA to the data (this is done in one step)
X_pca = pca.fit_transform(X)

## Reduced dataset dimensions
print(f"Reduced dataset shape: {X_pca.shape}")

## Convert numpy array to pandas dataframe
df = pd.DataFrame(data = X_pca, columns = ['Principal Component 1', 'Principal Component 2'])
df['target'] = y

## Visualize 2D Projection
plt.figure(figsize=(8,6))
sns.scatterplot(x='Principal Component 1', y='Principal Component 2', hue='target', data=df, palette='Set1')
plt.show()
```

In this script, we load the iris data set, which has 4 features. Then, we use PCA to reduce it to two features. We then plot these two features as a scatter plot to visualize the reduced data, which produces a graph as follows:

![](https://dagster.io/images/glossary/681399f654933b29e12fbad7_glossary-13.png)

It will print out to the terminal:

```plaintext
Original dataset shape: (150, 4)
Reduced dataset shape: (150, 2)
```

This script demonstrates a fundamental data reduction technique that is commonly used in modern data pipelines, particularly when dealing with high-dimensional data.

### Data reduction vs. data compaction vs. data compression

While related, reduction, compaction, and compression are slightly different techniques:

```javascript trailing-newline
| **Category**     | **Reduction**                                                                                           | **Compaction**                                                                       | **Compression**                                                                                   |
| ---------------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- |
| **Aim**          | Reduce the overall amount of data while retaining its critical information.                             | Minimize storage space while preserving data contents and structure.                 | Reduce data size by encoding it in a more space-efficient format, minimizing transfer or storage. |
| **Techniques**   | Aggregation, downsampling, encoding, filtering out less relevant data points, dimensionality reduction. | Data compression, merging smaller segments into larger ones, and optimizing indexes. | Encoding data in a compressed form to represent it in fewer bits.                                 |
| **Key benefits** | Improve data manageability, simplify analytics, and speed up query performance.                         | Improve storage efficiency and reduce costs.                                         | Reduce transfer times, save storage space, and improve transmission efficiency.                   |
```

Reduction, compression, and compaction are complimentary and often used together to achieve our data engineering objectives.

---

## Repartition

> Redistribute data across multiple partitions for improved parallelism and performance.

Source: https://dagster.io/glossary/data-repartitioning

### Data repartitioning definition:

Data repartitioning is the process of redistributing data across a cluster to achieve a more optimal data partitioning. It is a critical operation for optimizing performance in distributed computing environments such as Apache Spark. Repartitioning can help improve data locality, reduce data skew, and balance computational loads across worker nodes.

Note that repartitioning can be an expensive operation, as it involves shuffling data across the network. It should be used judiciously and only when necessary for performance optimization.

### Data repartitioning example using Python and PySpark:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

In Apache Spark, data repartitioning can be achieved using the `repartition()` or `coalesce()` method on a DataFrame or RDD. The `repartition()` method shuffles the data and creates a new set of partitions with a specified number. The `coalesce()` method reduces the number of partitions without shuffling the data.

Here's an example of using `repartition()` to increase the number of partitions of a DataFrame in Apache Spark:

```python
from pyspark.sql import SparkSession

# create SparkSession
spark = SparkSession.builder.appName("RepartitioningExample").getOrCreate()

# read input data
df = spark.read.csv("input_data.csv", header=True, inferSchema=True)

# check current number of partitions
print("Current number of partitions:", df.rdd.getNumPartitions())

# repartition to 4 partitions
df = df.repartition(4)

# check new number of partitions
print("New number of partitions:", df.rdd.getNumPartitions())

# write output data
df.write.csv("output_data", mode="overwrite", header=True)
```

In this example, the input data is read from a CSV file, and the `getNumPartitions()` method is used to check the current number of partitions. The `repartition()` method is then used to create a new DataFrame with 4 partitions, and the `getNumPartitions()` method is used again to check the new number of partitions. Finally, the output data is written to a CSV file.

It will print to the terminal:

```plaintext
Current number of partitions: 1
New number of partitions: 4
```

And will save the results to a folder called `` `output_data ``.\`

When working with data pipelines at scale, you will be defining partitioning rules in code, and letting the orchestrator manage the partitions and backfills.  You can find the details on Dagster’s [partitioning API here](https://docs.dagster.io/api/dagster/partitions).

---

## Replica Set

> A group of database nodes that maintains the same data set, providing redundancy and increasing data availability with multiple copies of data on different database servers.

Source: https://dagster.io/glossary/replica-set

---

## Replicate

> Create a copy of data for redundancy or distributed processing.

Source: https://dagster.io/glossary/data-replication

### Data replication definition:

Data replication in the context of modern data pipelines involves creating multiple copies of data and distributing them across multiple nodes or clusters to ensure high availability and fault tolerance. This is typically done to minimize the risk of data loss due to hardware failure or other issues.

### Data replication example using Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

In Python, data replication can be achieved using various distributed computing frameworks such as Apache Hadoop, Apache Spark, or Dask. These frameworks provide mechanisms for replicating data across multiple nodes or clusters.

For example, using Apache Spark, we can create a replicated RDD (Resilient Distributed Dataset) by calling the \`repartition()\`\` function with a desired number of partitions:

```python
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Data Replication Example").getOrCreate()

# Load data from a CSV file
data = spark.read.csv("data.csv", header=True, inferSchema=True)

# Replicate the data across two nodes
replicated_data = data.repartition(2)

# Do some processing on the replicated data
result = replicated_data.groupBy("column1").count()

# Save the result to a CSV file
result.write.csv("replicate_result", mode="overwrite", header=True)
```

In this example, we load data from a CSV file using Spark and then replicate the data across two nodes using the `repartition()` function. We then perform some processing on the replicated data and save the result to a CSV file in the folder `replicate_result`. The replicated data ensures high availability and fault tolerance in case of hardware failure or other issues.

---

## Reshape

> Change the structure of data to better fit specific analysis or modeling requirements.

Source: https://dagster.io/glossary/data-reshaping

### Data reshaping definition:

Data reshaping is the process of changing the layout or structure of data from one form to another, in order to better suit the needs of an analysis or downstream processing. This can involve rearranging rows and columns, changing the data type, or converting between wide and long formats.

#### Data transformation example using Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

In Python, the `reshape()` function in the NumPy library can be used for data reshaping. Let’s look at a simple example:

```python
import numpy as np

# create a 3x4 array of data
data = np.array([[1, 2, 3, 4],
                 [5, 6, 7, 8],
                 [9, 10, 11, 12]])

# reshape the data into a 2x6 array
reshaped_data = data.reshape(2, 6)

print(reshaped_data)
```

This will print out the new array as follows:

```plaintext
[[ 1  2  3  4  5  6]
 [ 7  8  9 10 11 12]]
```

This example reshapes a 3x4 array into a 2x6 array. The `reshape` function takes the new dimensions of the array as arguments. Note that the total number of elements in the original array must equal the total number of elements in the reshaped array, or an error will occur.

---

## Sample

> Extract a subset of data for exploratory analysis or to reduce computational complexity.

Source: https://dagster.io/glossary/data-sampling

### Data sampling definition:

Data sampling refers to the process of selecting a subset of data from a larger dataset for analysis or processing. This is often done to reduce the computational requirements of working with the entire dataset or to obtain a representative sample for testing or experimentation.

There are several techniques for data sampling, including random sampling, stratified sampling, and cluster sampling. Random sampling involves selecting data points randomly from the dataset, while stratified sampling involves dividing the dataset into subgroups and selecting data points from each subgroup. Cluster sampling involves selecting clusters of data points that are representative of the entire dataset.

### Data sampling example using Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Here is an example of how to perform random sampling in Python using the pandas library:

```python
import pandas as pd

# Load the dataset
data = pd.read_csv('data.csv')

print(f"Full dataset is {len(data)} items long")

# Randomly sample 10 rows from the dataset
sampled_data = data.sample(n=10)

print(f"Sampled dataset is {len(sampled_data)} items long")
```

In this example, we load a dataset from a CSV file using pandas and then use the sample() method to randomly select 1000 rows from the dataset. The resulting sampled\_data object contains the randomly selected rows, which can then be used for further analysis or processing.

Depending on your input `data.csv` this code might print out:

```javascript
Full dataset is 57 items long
Sampled dataset is 10 items long
```

Here's an example of performing stratified sampling on a dataset using Python's scikit-learn library.  Assuming an input file `data.csv` with a column `label`:

```python
import pandas as pd
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit

# Load dataset
data = pd.read_csv('data.csv')

# Create stratified sample
splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in splitter.split(data, data['label']):
    train_data = data.iloc[train_index]
    test_data = data.iloc[test_index]

# Check class distribution in training set
print(train_data['label'].value_counts() / len(train_data))
```

In this example, we first load a dataset from a CSV file using Pandas. We then create a `StratifiedShuffleSplit` object with a test set size of 20% and a random seed of 42. We pass in the entire dataset as well as the labels we want to stratify by (`data['label']`). We then iterate over the single split generated by the splitter and assign the resulting train and test indices to new variables. Finally, we print the class distribution of the training set to ensure it was stratified correctly.

---

## Scaling

> Increasing the capacity or performance of a system to handle more data or traffic.

Source: https://dagster.io/glossary/data-scaling

### Data scaling definition:

In data engineering, scaling refers to the process of increasing the capacity or performance of a system to handle more data or traffic. There are two primary approaches to scaling a system: horizontal scaling and vertical scaling.

#### Horizontal Scaling:

Horizontal scaling, also known as “scaling out”, involves adding more resources or machines to a system to increase its capacity. This approach involves distributing the load across multiple machines in a cluster or network, with each machine handling a portion of the workload. Horizontal scaling is typically achieved by adding more machines or instances of a service to a system, which can be done dynamically based on demand. Horizontal scaling provides the ability to handle larger workloads, but it requires additional management and coordination to ensure that the workload is distributed evenly across all the machines in the cluster.

#### Vertical Scaling:

Vertical scaling, also known as “scaling up”, involves increasing the capacity or performance of a single machine by adding more resources such as CPU, RAM, or storage. This approach involves upgrading the hardware or infrastructure of a machine to increase its processing power and capability. Vertical scaling is typically achieved by adding more CPU cores, RAM, or storage to an existing machine. Vertical scaling can provide increased performance and capability for a single machine, but it may have limitations due to the physical constraints of the hardware, such as the maximum number of cores or amount of memory that can be installed.

The main differences between horizontal and vertical scaling can be summarized as follows:

- **Horizontal scaling** involves adding more machines to a system, while vertical scaling involves upgrading the hardware of a single machine.
- **Horizontal scaling** provides the ability to handle larger workloads by distributing the load across multiple machines, while vertical scaling provides increased performance and capability for a single machine.
- **Horizontal scaling** requires additional management and coordination to ensure that the workload is distributed evenly across all the machines in the cluster, while vertical scaling does not require additional management overhead.

The choice between horizontal and vertical scaling depends on the specific needs and requirements of a system. Horizontal scaling is typically preferred for systems that require high availability and fault tolerance, while vertical scaling is preferred for systems that require high performance and low latency.

---

## Schema Inference

> Automatically identify the structure of a dataset.

Source: https://dagster.io/glossary/schema-inference

### Schema inference definition:

Schema inference is a process where the structure of a dataset (such as the data types and column names) is automatically identified, often when working with semi-structured or unstructured data.

### An example of schema inference in Python using Pandas and PyArrow

In Python, schema inference can be demonstrated using libraries such as `Pandas` for data manipulation and `PyArrow` for more advanced schema inference, especially useful with large datasets or in the context of distributed computing.

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Say we have a simple source file as follows:

```plaintext
| id | name            | age | date\_of\_birth | role   | is\_related |
| -- | --------------- | --- | --------------- | ------ | ----------- |
| 1  | Thom Yorke      | 55  | 1968-10-07      | vocals | False       |
| 2  | Jonny Greenwood | 52  | 1971-10-05      | guitar | True        |
| 3  | Ed OBrien       | 55  | 1968-04-15      | guitar | False       |
| 4  | Philip Selway   | 56  | 1967-05-23      | drums  | False       |
| 5  | Colin Greenwood | 54  | 1969-06-26      | bass   | True        |
```

1. **Read .csv File**: Use Pandas to read a .csv file. Pandas infers the data types for each column.
2. **Advanced Schema Inference with PyArrow**: After reading the data with Pandas, we'll convert the Pandas DataFrame into a PyArrow table for more advanced schema inference, which is particularly useful in big data scenarios.

Assuming we have the .csv file saved locally as `our_data.csv` we can run the following simple script:

```python
import pandas as pd
import pyarrow as pa

def infer_schema_with_pandas(file_path):
    """
    Infer schema using Pandas
    """
    df = pd.read_csv(file_path)
    return df.dtypes

def convert_to_pyarrow_table(df):
    """
    Convert Pandas DataFrame to PyArrow Table
    """
    return pa.Table.from_pandas(df)

def infer_schema_with_pyarrow(table):
    """
    Infer schema using PyArrow
    """
    return table.schema

def print_full_schema(schema):
    """
    Print out the schema fields
    """
    for field in schema:
        print(f"Field name: {field.name}, Type: {field.type}, Nullable: {field.nullable}, Metadata: {field.metadata}")

# Path to your CSV file
file_path = 'our_data.csv'

# Infer schema with Pandas
pandas_schema = infer_schema_with_pandas(file_path)
print("Schema inferred by Pandas:")
print(pandas_schema)

# Advanced Schema Inference with PyArrow
df = pd.read_csv(file_path)
arrow_table = convert_to_pyarrow_table(df)
arrow_schema = infer_schema_with_pyarrow(arrow_table)
print("\nSchema inferred by PyArrow:")
print_full_schema(arrow_schema)
```

In this very basic example:- `infer_schema_with_pandas` function reads the .csv file using Pandas and prints the inferred data types.- `convert_to_pyarrow_table` converts the Pandas DataFrame to a PyArrow Table.- `infer_schema_with_pyarrow` uses PyArrow to print a more detailed schema, which can include more nuanced data types and is more suitable for big data applications.

The output will be as follows:        

```javascript
Schema inferred by Pandas:
id                int64
name             object
age               int64
date_of_birth    object
role             object
is_related         bool
dtype: object

Schema inferred by PyArrow:
Field name: id, Type: int64, Nullable: True, Metadata: None
Field name: name, Type: string, Nullable: True, Metadata: None
Field name: age, Type: int64, Nullable: True, Metadata: None
Field name: date_of_birth, Type: string, Nullable: True, Metadata: None
Field name: role, Type: string, Nullable: True, Metadata: None
Field name: is_related, Type: bool, Nullable: True, Metadata: None
```

---

## Schema Mapping

> Translate data from one schema or structure to another to facilitate data integration.

Source: https://dagster.io/glossary/schema-mapping

### Schema mapping definition:

Schema mapping is the process of mapping the schema of one dataset to the schema of another dataset in order to integrate or merge them. It involves mapping the fields or attributes of one dataset to the fields or attributes of another dataset, ensuring that the data types, formats, and other properties match between the two datasets. Schema mapping is a crucial step in data integration, as it enables datasets with different schemas to be combined and analyzed together.

### Schema mapping example using Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

In Python, one way to perform schema mapping is by using the PySpark library. Here's an example:

```python trailing-newline
# Importing the required libraries
from pyspark.sql import SparkSession
from pyspark.sql.functions import col

# Creating a SparkSession
spark = SparkSession.builder.appName("Schema Mapping Example").getOrCreate()

# Creating a sample dataframe with multiple columns
data = [("John", 25, "25000.50"), ("Smith", 30, "35000.75"), ("Jane", 28, 32000.25)]
df = spark.createDataFrame(data, ["name", "age", "salary"])

# Defining the mapping schema
mapping_schema = {
    "name": "string",
    "age": "integer",
    "salary": "double"
}

# Creating a new dataframe with the mapped schema
mapped_df = df.select([col(c).cast(mapping_schema[c]).alias(c) for c in mapping_schema.keys()])

# Displaying the original and mapped dataframes
print(f"Original dataframe: {df}")
print(f"Mapped dataframe: {mapped_df}")

# Applying some transformations on the mapped dataframe
filtered_df = mapped_df.filter(mapped_df.age > 25)
grouped_df = filtered_df.groupBy("name").agg({"salary": "sum"})

# Stopping the SparkSession
spark.stop()
```

This will print out the schema of each dataframe showing the new mapping:

```javascript
Original dataframe: DataFrame[name: string, age: bigint, salary: string]
Mapped dataframe: DataFrame[name: string, age: int, salary: double]
```

---

## Scrape

> Extract data from a website or another source.

Source: https://dagster.io/glossary/data-scraping

### Data Scraping definition:

**Data Scraping** refers to the process of **extracting data from a website or another source**, typically to transform and store it in a structured form. While the concept has existed for a long time, it's become increasingly relevant in the context of modern data engineering due to the vast amounts of data available on the web and the value that can be derived from analyzing that data.

 Want to skip right to the code? [Jump to the example ↓](#)

### Is data scraping legitimate, or just a workaround?

Many engineers would argue the term "scraping" infers a workaround when no formal method for making the data available exists (such as an API - or Application Programming Interface - or some form of export or download) . Whether data scraping is a workaround or a legitimate approach to data acquisition depends on the context, the specific use case, and, often, the legal or ethical considerations involved:

**Legitimate Uses:**

- **Research**: Academics and researchers might scrape data from the web to conduct research, analyze trends, or gather datasets that are not otherwise available.
- **Business Intelligence**: Companies might scrape data to analyze market trends, track competitors' prices, or gather information that is publicly available but not readily accessible in structured formats.
- **Intelligent Process automation**: Engineers may devise ways of scraping data from unformatted or legacy documents that were never designed for digital processing.

**Workaround Uses:**

- **API Limitations**: Sometimes, websites offer an API  to access their data, but there might be limitations regarding the rate of requests, the volume of data, or the type of data available. In these cases, scraping might be seen as a workaround to bypass such limitations.
- **No API Availability**: If a website doesn't provide an API or structured data access, scraping is the method to get the data programmatically. Again, check the website's terms and conditions to see if scraping is a violation.

### is data scraping only good for web content?

Data scraping is often thought to be synonymous with "web scraping" but it is not limited to just web content. While web scraping is one of the most talked-about forms of data scraping due to the vast amount of publicly accessible data on the internet, scraping as a concept is much broader and can be applied wherever there's data that needs to be extracted. Here are some other contexts where data scraping is applicable:

1. **File Systems**: Data can be scraped from a collection of documents, spreadsheets, and other files. For instance, you might scrape text from a collection of PDFs or pull specific data from Excel files.
2. **Databases**: You can "scrape" or extract data from databases, although the term more commonly used in this context is "query" or "extract."
3. **Emails**: Data can be scraped from email bodies, attachments, or headers for various purposes like surveillance, analytics, or automations.
4. **Images and Videos**: With the help of OCR (Optical Character Recognition) tools, text can be scraped from images. Similarly, specific data or patterns can be extracted from videos.
5. **Software Applications**: Data can be scraped from software applications that don't offer an export feature but display relevant data on their GUI. Tools that simulate mouse and keyboard actions (like RPA - Robotic Process Automation tools) can be used in such scenarios.
6. **Network Traffic**: In cybersecurity and network management, data packets can be "scraped" or captured to analyze the content of network traffic.

### Data scraping use cases:

You will find data scraping steps in the data pipelines used in many different scenarios:   - **Market Research**: Scraping product prices from various online retailers for competitive analysis, as mentioned earlier.   - **Social Media Analysis**: Extracting user reviews, comments, and ratings for sentiment analysis.   - **Job Boards**: Scraping job postings to analyze labor market trends.   - **Real Estate**: Gathering property listings and prices for market trend analysis.

### the challenges of data scraping as a technique:

- **Legal Concerns**: Not all websites allow data scraping. It's essential to review the website’s `robots.txt` file and the terms of service to ensure compliance.
- **Dynamic Content**: Websites using JavaScript to load content can be challenging to scrape using basic methods. Frameworks that can mimic browser behavior, like Selenium, are used in such cases.
- **Rate Limiting**: Websites might limit the frequency of access to prevent scraping. Designing respectful and non-intrusive scrapers is essential.
- **Data Quality**: Scraped data can be messy and might require considerable cleaning and preprocessing.

### Ethics & Best Practices in data scraping:

- **Respect `robots.txt`**: This is a standard used by websites to communicate which parts should not be scraped or crawled.
- **Avoid Overloading Servers**: Introduce delays between requests to avoid unintentionally performing a Denial-of-Service attack.
- **User-Agent Headers**: Be transparent about who you are and why you are scraping. Some websites block default user-agent strings used by scraping libraries.

In the broader context of data engineering, data scraping is just one component. Once data is acquired, it has to be cleaned, transformed, stored, and finally analyzed. As technology and the web evolve, data scraping techniques and tools will also adapt, and many technology companies are growing increasingly protective of their web content.

### Python tools for data scraping

Python is a tool of choice for building data scraping applications. It provides many off-the-shelf tools for this task:

- **Libraries**: Python libraries such as Beautiful Soup, Scrapy, and Selenium are popular choices for web scraping.
- **Cloud Services**: AWS Lambda, Google Cloud Functions can be used to execute scraping tasks at scale.
- **Scraping Platforms**: Tools like Octoparse, Import.io, and WebHarvy offer user-friendly GUIs for web scraping without coding.

### A practical example of data scraping in Python

By way of illustration, let's scrape the real-time weather data from the [wttr.in](https://wttr.in/) website, which provides weather information in a simple text format. It's also friendly for developer usage, making it a suitable candidate for this example.

**Note**: While the "wttr.in" service is developer-friendly, try not to make too many requests to any service, as it may lead to your IP being rate-limited or banned. Always read and follow the terms of service or usage policies of any external service you interact with programmatically.

To do the scraping we will use [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/), a Python library for pulling data out of HTML and XML files.

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```javascript
pip install beautifulsoup4 requests
```

Then run the following code:

```python
import requests
from bs4 import BeautifulSoup

BASE_URL = "https://wttr.in/"

def fetch_weather(city):
    # Construct the URL based on the desired city
    url = f"{BASE_URL}{city}?format=%C+%t"

    # Make the request
    response = requests.get(url)
    response.raise_for_status()  # Raise an exception for HTTP errors

    # The content will already be in a simple text format: "<condition> +<temperature>"
    # No need for BeautifulSoup parsing in this case
    condition, temperature = response.text.split('+')

    return {
        "city": city,
        "condition": condition.strip(),
        "temperature": temperature.strip()
    }

# Example Usage:
cities = ["Philadelphia","San Francisco","London","Paris"]
for city in cities:
    weather_info = fetch_weather(city)
    print(weather_info)
```

When you run the above code, you might get an output similar to:

```plaintext
{'city': 'Philadelphia', 'condition': 'Overcast', 'temperature': '90°F'}
{'city': 'San Francisco', 'condition': 'Partly cloudy', 'temperature': '69°F'}
{'city': 'London', 'condition': 'Overcast', 'temperature': '66°F'}
{'city': 'Paris', 'condition': 'Clear', 'temperature': '70°F'}
```

Yes, it is very hot right now in Philadelphia!

---

## Secondary Index

> Improve the efficiency of data retrieval in a database or storage system.

Source: https://dagster.io/glossary/secondary-index

### Secondary index definition:

In data engineering, a **secondary index** is an alternative way to access and search for data in addition to the primary index, which is typically based on the primary key of a table.

A secondary index can be a valuable data engineering technique that enhances the performance and flexibility of database queries. However, it should be used judiciously and selectively, considering the trade-offs involved in terms of maintenance and storage overhead.

### Secondary index explained:

Here's a breakdown of the concept of a secondary index:

The **primary index** in a database is usually built on the primary key of a table. It ensures that rows in the table are physically stored in a way that allows for efficient retrieval based on the primary key values. Retrieving data using the primary key is typically fast and optimized, as the database engine knows exactly where to find the corresponding data.

A **secondary index**, on the other hand, is created on one or more columns other than the primary key columns of a table. Its purpose is to enable efficient searching and retrieval of data based on these secondary columns. In other words, it provides an additional path to access the data without having to perform a full table scan or a time-consuming query.

A table can have multiple "secondary" indexes. In fact, having multiple secondary indexes is a common practice in relational database systems to improve query performance.

One alternative—\_composite indexes\_ (indexes on multiple columns)—can be more efficient for some queries, but they are not a one-size-fits-all solution. Creating composite indexes requires careful consideration of the query patterns to ensure they are beneficial. Adding more columns to a composite index does not always improve performance.

### Benefits of Secondary Indexes:

- **Improved Query Performance**: Secondary indexes speed up queries that involve filtering, sorting, or searching based on columns that are not part of the primary key.
- **Reduced I/O Operations**: Secondary indexes reduce the need for scanning the entire table, which can significantly reduce I/O operations and improve query response times.
- **Flexibility**: They allow for a more versatile querying capability, as you can search for data using various criteria that may not be part of the primary key.
- **Supporting JOIN Operations**: Secondary indexes can also be helpful when performing JOIN operations between tables, where the join condition involves columns other than the primary key.

### The downsides of secondary indexing:

Secondary indexes require additional storage and maintenance, as they need to be updated whenever data in the indexed columns is inserted, updated, or deleted. This can lead to overhead in terms of storage and write operations. Consider carefully the columns to index as creating too many secondary indexes can lead to increased storage and maintenance costs.

### Estimating the impact

The additional storage overhead introduced by secondary indexing in a database depends on various factors, including the database management system (DBMS) you are using, the number and size of indexed columns, and the number of rows in the table, etc.

This said, a common rule of thumb is that **an index may require around 20-40% of the storage space of the data it indexes**.

To get a more accurate estimate for your specific use case, you can use the following approach:

1. Create a table with the data and columns you plan to index.
2. Populate the table with representative data.
3. Create the secondary index.
4. Check the size of the index in your specific DBMS using built-in functions or tools provided by the DBMS itself.

So, when should we consider a secondary index?  Let's look at some scenarios:

### When to use a secondary index

Secondary indexes are most efficient in circumstances where they can significantly improve query performance and provide a valuable advantage in data retrieval. Here are some scenarios in which using a secondary index is highly beneficial:

- **Frequent search queries** on columns other than the primary key, such as filtering, sorting, or searching based on non-primary key attributes.
- **Complex queries** involving JOIN operations, WHERE clauses, or GROUP BY clauses on columns that are not part of the primary key.
- **To improve read performance** by avoiding full table scans.
- **Ranges and range queries** such as date ranges, numerical ranges, or alphabetical ranges.
- **High selectivity** i.e. the values in the column are relatively unique. High selectivity helps reduce the number of rows that need to be scanned for a specific value, making queries faster.

### An example of secondary indexing in Python

![](https://dagster.io/images/glossary/681399f654933b29e12fb9e7_Postgresql_elephant.svg)

 This example requires a Postgres database. [See the setup steps here](https://database-setup).

To illustrate Secondary Indexing using Python, here is a simple example using PostgreSQL as our database, and using [psycopg2](https://pypi.org/project/psycopg2/), the most popular PostgreSQL database adapter for the Python programming language.

In this example, we'll create a table with a primary index on the "id" column and a secondary index on the "username" column for efficient username-based queries.

We achieve this with the PostgreSQL command [`CREATE INDEX <index_name> ON <table_name>`](https://www.postgresql.org/docs/current/sql-createindex.html).

First, you'll need to install the psycopg2 library if you haven't already. You can install it using pip:

```javascript
pip install psycopg2
```

Now, let's create a sample Python script to demonstrate the concept. Make sure to replace `'your_database_name'`, `'your_username'`, `'your_password'`, `'your_host'`, and `'your_port'` with the actual connection details for your PostgreSQL database.

If you have followed this guide, the example would use:\* 'dbname': 'dagsterdb',\* 'host': 'localhost',\* 'port': '5432',\* 'user': will be the one you retrieved with the command `whoami`\* you can skip `'your_password'` if you have not set one.

```python trailing-newline
import psycopg2

# Establish a connection to the PostgreSQL database
conn = psycopg2.connect(
    host="host",
    port="port",
    database="dbname",
    user="your_username",
    password="your_password"
)

# Create a cursor object to interact with the database
cursor = conn.cursor()

# Create a table with a primary key and a secondary index
create_table_query = '''
    CREATE TABLE band_members (
        name VARCHAR(255),
        role VARCHAR(50),
        created_date DATE
    );
'''

# Execute the create table query
cursor.execute(create_table_query)

# Create a secondary index on the 'role' column
create_index_query = '''
    CREATE INDEX role_index ON band_members(role);
'''

# Execute the create index query
cursor.execute(create_index_query)

# Insert some sample data into the table
insert_data_query = '''
    INSERT INTO band_members (name, role, created_date) VALUES
    ('Thom Yorke', 'vocals', '1968-10-07'),
    ('Jonny Greenwood', 'guitar', '1971-10-05'),
    ('Ed OBrien', 'guitar', '1968-04-15'),
    ('Philip Selway', 'drums', '1967-05-23'),
    ('Colin Greenwood', 'bass', '1969-06-26');
'''

cursor.execute(insert_data_query)

# Query data using the secondary index
search_role = 'guitar'
select_query = '''
    SELECT * FROM band_members WHERE role = %s
'''

cursor.execute(select_query, (search_role,))
users = cursor.fetchall()

if users:
    print(f"Radiohead band members with a role of '{search_role}' found:")
    for user in users:
        print(f"Name: {user[0]}, Role: {user[1]}, Date of birth: {user[2]}")
else:
    print(f"No users with username '{search_role}' found")

# Close the cursor and the database connection
cursor.close()
conn.close()
```

This example will return:

```plaintext
Band members with a role of 'guitar' found:
Name: Jonny Greenwood, Role: guitar, Date of birth: 1971-10-05
Name: Ed OBrien, Role: guitar, Date of birth: 1968-04-15
```

---

## Secure

> Protect data from unauthorized access, modification, or destruction.

Source: https://dagster.io/glossary/securing-data

### Securing data - a definition:

Securing data in modern data pipelines is an essential aspect to ensure data privacy and prevent data breaches. Some techniques used for securing data in data pipelines include encryption, access control, and data anonymization.

Dagster Cloud provides\_ role-based access control \_(RBAC) to give you granular control over who can execute or modify which part of your data platform, which narrows down the access to the data the platform manages.

Encryption involves converting plaintext data into ciphertext using encryption algorithms to ensure confidentiality.

### Example of securing data in Python:

In Python, the cryptography library can be used for encryption and decryption of data. Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
from cryptography.fernet import Fernet

# generate a secret key
key = Fernet.generate_key()

# create an instance of the Fernet class using the key
cipher_suite = Fernet(key)

# encrypt the data
message = b"Hello, world!"
encrypted_message = cipher_suite.encrypt(message)

print(encrypted_message)

# decrypt the data
decrypted_message = cipher_suite.decrypt(encrypted_message)

print(decrypted_message)
```

Access control involves setting up appropriate permissions and roles to ensure that only authorized personnel can access the data. This can be achieved through techniques like role-based access control (RBAC) or attribute-based access control (ABAC). In Python, access control can be implemented using libraries like `Flask-Security` or `Django-Defender`.

Data anonymization involves removing or obfuscating sensitive data from the dataset to ensure privacy while still maintaining the usefulness of the data. Techniques used for data anonymization include masking, generalization, and perturbation. In Python, data anonymization can be achieved using libraries like `OpenMondrian` or `ARX` .

Overall, securing data in modern data pipelines requires a combination of technical solutions and best practices to ensure the safety and privacy of sensitive data.

---

## Serialize

> Convert data into a linear format for efficient storage and processing.

Source: https://dagster.io/glossary/data-serialization

### Data serialization definition:

Data serialization is the process of converting complex data structures, such as objects or dictionaries, into a format that can be stored or transmitted, such as a byte stream or JSON string. This is useful in modern data pipelines for tasks such as saving data to disk, transmitting data across a network, or storing data in a database.

### What are the benefits of serializing data?

The main goals of data serialization are:

1. **Interoperability**: To enable data exchange between different systems or components. Serialized data can be transmitted between systems built with different programming languages or architectures.
2. **Persistence**: To allow the state of an object or data structure to be saved to storage media (like a hard drive) and then later retrieved and reconstructed.
3. **Transmission**: Facilitate the sending of data across a network. Serialized data can be sent over protocols like HTTP, or through sockets, and then reconstructed at the receiving end.
4. **Consistency**: Serialized data often adheres to specific schemas or formats, ensuring that the data exchanged or stored maintains a predictable structure.

### What about compression?

While some serialization formats or methods might result in a smaller representation of the data (and thus have a secondary effect of compression), true [compression](https://dagster.io/glossary/data-compression) is a separate process. In many scenarios, especially where bandwidth or storage is at a premium, serialized data might be further compressed using specific compression algorithms or techniques.  But remember, each step (serializing/deserializing, compressing/decompressing) consumes time and resources.

For some applications, especially in advanced data engineering or analytics use cases, certain serialization formats (like Avro, Parquet, or Protocol Buffers) might be preferred because they can offer both efficient serialization/deserialization and a compact data representation - scroll down for an example of using Protocol Buffers in Python. Still, compression is a beneficial side effect, not the primary goal of serialization.

### A simple Data serialization example using Python:

Let's look at a simple serialization/deserialization example.  You will find a more advanced example further down in the glossary entry.

Python provides several built-in serialization formats, including pickle, JSON, and YAML. Here's an example of using the pickle module to serialize a Python object:

```python
import pickle

# Define an object to serialize
data = {
    'name': 'Dagster',
    'age': 4,
    'email': 'dagster@dagsterlabs.com'
}

# Serialize the object to a byte stream
serialized_data = pickle.dumps(data)

# Write the byte stream to a file
with open('data.pickle', 'wb') as f:
    f.write(serialized_data)
```

This code defines a dictionary data and then serializes it using the pickle module's `dumps()` method. The resulting byte stream is then written to a file named data.pickle. If you open the file you will see the data written out as:

```javascript trailing-newline
��=}�(�name��Dagster��age�K�email��dagster@dagsterlabs.com�u.
```

To deserialize the data later, you can use the `loads()` method:

```python
import pickle

# Read the byte stream from the file
with open('data.pickle', 'rb') as f:
    serialized_data = f.read()

# Deserialize the byte stream into a Python object
data = pickle.loads(serialized_data)

# Print the deserialized data
print(data)
```

This code reads the serialized data from the file, deserializes it using pickle's `loads()` method, and then prints the resulting Python object.

```python
import pickle

# Define an object to serialize
data = {
    'name': 'Dagster',
    'age': 4,
    'email': 'dagster@dagsterlabs.com'
}

# Serialize the object to a byte stream
serialized_data = pickle.dumps(data)

# Write the byte stream to a file
with open('data.pickle', 'wb') as f:
    f.write(serialized_data)
```

### A data engineering example of data serialization

For more advanced data engineering use cases, especially when dealing with distributed systems and big data platforms, data serialization often involves formats like Apache Avro, Parquet, or Protocol Buffers (Protobuf). These formats provide efficient serialization and deserialization, often in both size and speed, compared to simple JSON or XML formats.

Let's look at an example using [Protocol Buffers](https://github.com/protocolbuffers/protobuf) (or "Protobuf"), a method developed by Google for serializing structured data.

First, let's make sure we have the right packages involved.

1. **Install Java**: You will need to be running java on your machine.  If you don't already have it installed you will find the [java installation instructions here](https://www.java.com/en/download/help/download_options.html).
2. **Install Protobuf**:  On a Mac the command you enter in the Terminal is `brew install protobuf`. On Linux it's `apt install -y protobuf-compiler`. On Windows you will find instructions [here](https://www.geeksforgeeks.org/how-to-install-protocol-buffers-on-windows/).  (More general instal instructions [here](https://grpc.io/docs/protoc-installation/)).
3. **Define the Data Structure** (.proto file): We'll define the structure of the data you want to serialize in a .proto file. Let's consider a simple message format for a `user`.  Create a file in a sandbox directory on your computer called `user.proto` as follows:

```python
syntax = "proto3";

message User {
    int32 id = 1;
    string name = 2;
    string email = 3;
}
```

**4. Have Protobuf create the Python serializer**. You need the `protoc` compiler to generate Python code from the `user.proto` file:

```javascript
protoc --python_out=. user.proto
```

This command simply instructs protoc to compile a new Python file (`python_out`) in the same directory (`.`) based on the information found in the file `user.proto`.

After running this command you should have a new Python file called `user_pb2.py` in your directory.

**5.Run the example**: Create a new file called `example.py` with the following code.

```python
import sys
import user_pb2

# Create a new User instance
user = user_pb2.User()
user.id = 123
user.name = "Alice"
user.email = "alice@example.com"

# Serialize the user data
serialized_data = user.SerializeToString()

print("Serialized data:", serialized_data)
print("Serialized data is type: ", type(serialized_data), " of size ", sys.getsizeof(serialized_data), "bytes")

# Deserialize the data
deserialized_user = user_pb2.User()
deserialized_user.ParseFromString(serialized_data)

print("Deserialized data:")
print(type(deserialized_user), " of size ", sys.getsizeof(deserialized_user), "bytes")
print("ID:", deserialized_user.id)
print("Name:", deserialized_user.name)
print("Email:", deserialized_user.email)
```

When you execute this example with `python3 example.py`, your output will look like this:

```plaintext
Serialized data: b'\x08{\x12\x05Alice\x1a\x11alice@example.com'
Serialized data is type:  <class 'bytes'>  of size  61 bytes
Deserialized data:
<class 'user_pb2.User'>  of size  80 bytes
ID: 123
Name: Alice
Email: alice@example.com
```

Congratulations - you serialized, then deserialized data using a more advanced data engineering technique.

---

## Serverless Computing

> A cloud-computing execution model where the cloud provider runs the server and dynamically manages the allocation of machine resources, allowing developers to focus on individual functions.

Source: https://dagster.io/glossary/serverless-computing

---

## Shard

> Partitioning a database into smaller, more manageable pieces.

Source: https://dagster.io/glossary/data-sharding

### Definition of Sharding in database administration

Sharding is the practice of **partitioning a database into smaller, more manageable pieces called shards**. Each shard is essentially a standalone database that stores a subset of the data.

Sharding improves performance, scalability, and manageability in databasing. In data engineering, sharding is a crucial concept when dealing with massive datasets that can't be effectively handled by a single machine or database instance. However, it's essential to approach sharding with a clear understanding of both its benefits and challenges.

Sharding is a technique used at firms like [Dropbox.com](https://dropbox.tech/infrastructure/cross-shard-transactions-at-10-million-requests-per-second) to handle 10 million requests per second across petabytes of metadata.

### When to Use Sharding:

Not all databases or storage situations benefit from sharding. It's essential to assess whether the complexity introduced by sharding will be offset by the performance and scalability gains.

Databases with very high write loads, massive datasets, or those anticipated to grow significantly in the future are good candidates.

Before jumping into sharding, consider other scalability techniques like [replication](https://dagster.io/glossary/data-replication), caching, or using specialized database systems optimized for certain types of workloads.

**Benefits and Challenges of Sharding:**

The key benefits of sharing are:- **Scalability**: As datasets grow, the demands on a database's CPU, RAM, and storage can overwhelm a single server. By sharding, you distribute the data across multiple servers, thereby distributing the load as well.- **Performance**: Large datasets can lead to slow query performance, especially in distributed multi-tenant systems. Breaking the data into shards can lead to faster query responses since each query can be executed on a smaller subset of the data.- **High Availability**: With data distributed across multiple servers, the failure of one server doesn't bring the entire system down.

But sharding also presents challenges:- **Complexity**: Sharding introduces operational and architectural complexity. Querying across shards, for example, can be more challenging than querying a single, unsharded database.- **Rebalancing**: Over time, as data grows or access patterns change, there might be a need to redistribute the data. This rebalancing can be a non-trivial operation.- **Join Operations**: Performing joins between tables that are on different shards can be complex and inefficient.- **Backups and Maintenance**: Each shard essentially being a separate database means backups, updates, and other maintenance tasks need to be managed for multiple databases.

### Database sharding concepts

**Shard Key**: - Central to the sharding strategy is the choice of a shard key. This key determines how data is distributed across the shards. A good shard key ensures that data is distributed evenly and access patterns are efficient.- The choice of a shard key is crucial. A poorly chosen shard key can lead to uneven data distribution (some shards have way more data than others) or inefficient query patterns.

**Types of  Sharding**:- **Horizontal Sharding**: Data rows are partitioned across shards. For example, if you have a table with user profiles, you might shard based on user ID, where IDs 1-1000 are on shard A, 1001-2000 on shard B, and so on.- **Vertical Sharding**: Different tables or columns of a table are stored in different shards. For example, a user profile table might be split such that names and addresses are on one shard, while user preferences are on another.

### Two examples of database sharding in Python

#### Example 1: Sharding in SQLite

Let look at an example where we need to shard user data based on the user's ID across multiple databases using Python with the sqlite3 library. While SQLite might not be used for sharding in production, it's useful for illustrating the concept. In a real-world scenario, you'd probably be working with distributed databases like Cassandra, MongoDB (illustrated in the next example below), or even sharded PostgreSQL or MySQL setups.

Steps:

1. Create multiple SQLite databases (representing 10 shards).

   - We're simulating the insertion of 1000 users.
   - The random\_name function generates random user names.
2. Write functions to determine which shard to write to or read from based on a user's ID.
3. Demonstrate writing and reading data by randomly retrieving and printing details of 5 users to demonstrate retrieval from the large dataset.

We'll generate a large volume of data using random functions to simulate many users and we'll insert them into the sharded databases. Feel free to change the value of `NUM_USERS` to make the example run faster.

In a real-world scenario, you'd also need to think about optimizing batch inserts, handling potential errors, and so forth. But this example is meant to illustrate the basic principle of sharding with a larger dataset.

This is a simplified example, and real-world sharding introduces many complexities, such as:

- Handling joins across shards.
- Ensuring balanced data distribution.
- Managing failures, backups, and replication.
- Rebalancing shards.

However, this SQLite example illustrates the basic principles of determining where data should reside based on a sharding key (user ID, in this case) and how to interact with the appropriate shard when reading or writing data.

```python
import sqlite3
import os
import random
import string

# Number of shards
NUM_SHARDS = 10

# Shard database names
SHARD_NAMES = [f"shard_{i}.db" for i in range(NUM_SHARDS)]

# Number of users to simulate (change this to a lower number if you want the example to run faster)
NUM_USERS = 1000

# Cleanup: Delete shard files if they exist to start fresh
for shard_name in SHARD_NAMES:
    if os.path.exists(shard_name):
        os.remove(shard_name)

def setup_shards():
    """Initialize the databases."""
    for shard_name in SHARD_NAMES:
        conn = sqlite3.connect(shard_name)
        cursor = conn.cursor()
        cursor.execute('''CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT)''')
        conn.commit()
        conn.close()

def get_shard(user_id):
    """Determine which shard a user ID should go to."""
    shard_num = user_id % NUM_SHARDS
    return SHARD_NAMES[shard_num]

def insert_user(user_id, name):
    shard_name = get_shard(user_id)
    conn = sqlite3.connect(shard_name)
    cursor = conn.cursor()
    cursor.execute('INSERT INTO users (id, name) VALUES (?, ?)', (user_id, name))
    conn.commit()
    conn.close()

def get_user(user_id):
    shard_name = get_shard(user_id)
    conn = sqlite3.connect(shard_name)
    cursor = conn.cursor()
    cursor.execute('SELECT name FROM users WHERE id=?', (user_id,))
    result = cursor.fetchone()
    conn.close()
    return None if result is None else result[0]

# Generate random user names
def random_name(length=5):
    letters = string.ascii_lowercase
    return ''.join(random.choice(letters) for i in range(length))

# Setup shards
setup_shards()

# Insert many users
for user_id in range(1, NUM_USERS + 1):
    insert_user(user_id, random_name())

# Retrieve and print some user names by random IDs
for _ in range(5):
    random_id = random.randint(1, NUM_USERS)
    print(f"User ID: {random_id}, Name: {get_user(random_id)}")
```

Because the data is randomly generated, the exact output will vary but will look something like this:

```javascript trailing-newline
User ID: 97057, Name: zkrlu
User ID: 59872, Name: tnyvr
User ID: 1760, Name: rtzpp
User ID: 58910, Name: azkkm
User ID: 48120, Name: iwmlc
```

#### Example 2: Sharding with MongoDB

Assuming you have MongoDB running locally, we can also issutrate sharding on this NoSQL solution.

MongoDB is a popular database that has built-in support for sharding. Here's a simple example using Python and the `pymongo` library to illustrate how sharding could be done:

To follow this example, [you need to have pymongo and MongoDB running](https://dagster.io/glossary).

This example will:\* Start MongoDB (multiple instances for shards, config servers, etc.).\* Set up sharding using MongoDB's commands.\* Use `pymongo` in Python to insert and retrieve data.

```python
from pymongo import MongoClient, ASCENDING
from pymongo.errors import OperationFailure

# Connect to the Mongo client
client = MongoClient("localhost", 27017)

# Access the database
db = client.shard_example_db

# Drop previous collection if it exists (for this example to be repeatable)
db.users.drop()

# Here we assume the sharding environment is set up.
# Normally, this step requires MongoDB configuration servers, adding shards, etc.
# Let's enable sharding on our database and collection:
try:
    client.admin.command("enableSharding", "shard_example_db")
except OperationFailure:
    # The database is already sharded
    pass

# Create an index, which will be used as the shard key
db.users.create_index([("user_id", ASCENDING)])

# Shard the collection
try:
    client.admin.command("shardCollection", "shard_example_db.users", key={"user_id": "hashed"})
except OperationFailure:
    # The collection is already sharded
    pass

# Insert data into the collection
for user_id in range(1, 10001):  # Insert 10,000 documents
    db.users.insert_one({"user_id": user_id, "name": f"User_{user_id}"})

# Retrieve and print some user data
print(db.users.find_one({"user_id": 5000}))

client.close()
```

The final `print(db.users.find_one({"user_id": 5000}))` command will output something like this (note, the `ObjectId` will vary):

```plaintext
{'_id': ObjectId('650b561b2e46072998072507'), 'user_id': 5000, 'name': 'User_5000'}
```

---

## Shred

> Break down large datasets into smaller, more manageable pieces for easier processing and analysis.

Source: https://dagster.io/glossary/data-shredding

### Data shredding definition:

Data shredding, also known as [data partitioning](https://dagster.io/glossary/data-partitioning) or [data fragmentation](https://dagster.io/glossary/data-fragmentation), is the process of breaking down large datasets into smaller, more manageable pieces for easier processing and analysis. Shredding data can improve performance by allowing distributed processing of the smaller pieces, which can be performed in parallel, reducing the amount of time required to process the entire dataset. This approach is particularly useful for large datasets that cannot be processed on a single machine or within a single process.

As a side note, *data shredding* may, in a different context, refer to the process of securely erasing or destroying sensitive data that is no longer needed.

### Data shredding example using Python:

In Python, data shredding can be achieved using various libraries, such as PySpark or Dask, which allow for distributed computing across multiple nodes or machines. For example, PySpark's repartition() function can be used to repartition a DataFrame into a specified number of partitions, enabling more efficient processing of large datasets. Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Here's an example:

```python
from pyspark.sql import SparkSession

# create a SparkSession object
spark = SparkSession.builder.appName("DataShreddingExample").getOrCreate()

# read in a large dataset
df = spark.read.format("csv").option("header", "true").load("large_dataset.csv")

# repartition the data into 10 partitions
df = df.repartition(10)

# perform some analysis on the data
result = df.groupBy("Col A","Col B").count().show()

# stop the SparkSession
spark.stop()
```

In this example, we start by creating a SparkSession object, then reading in a large dataset. We then repartition the data into 10 partitions using the `repartition()` function, which creates 10 smaller, more manageable pieces of the data. We can then perform some analysis on the data, such as grouping by a column and counting the occurrences, before stopping the SparkSession.

For example, if the input file `large_dataset.csv` contained two columns of 1000 random integers between 0 and 10, your results might look like this:

```plaintext
|Col A|Col B|count|
+-----+-----+-----+
|    9|    4|   11|
|   10|    5|    2|
|    9|    5|    8|
|    8|    2|    8|
|    7|    1|   11|
|    4|    1|   11|
|    4|    2|   12|
|    0|    6|    6|
|    5|    4|    8|
|    1|    4|   14|
|    0|    4|    5|
|    3|    8|   11|
|    4|    6|    8|
|    5|    1|   12|
|    5|    5|    8|
|    2|    8|   13|
|    1|    1|   13|
|    0|    1|    6|
|   10|    0|    2|
|    0|    5|    4|
+-----+-----+-----+
only showing top 20 rows
```

---

## Shuffle

> Randomize the order of data records to improve analysis and prevent bias.

Source: https://dagster.io/glossary/data-shuffling

### Data shuffling definition:

Data shuffling is a process in modern data pipelines where the data is randomly redistributed across different partitions to enable parallel processing and better performance. Shuffling is generally done after some processing has been completed on the data, such as sorting or grouping, and before additional processing is performed. Shuffling can be an expensive operation in terms of time and resources, especially for large datasets.

### Data shuffling example using Python:

In Python, data shuffling can be performed using various libraries such as Apache Spark or Dask. Here is an example of shuffling data using Apache Spark\:Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
# Import necessary libraries
from pyspark.sql.functions import rand
from pyspark.sql.functions import col
from pyspark.sql import SparkSession

# create a SparkSession object
spark = SparkSession.builder.appName("DataShreddingExample").getOrCreate()
# Load data into Spark DataFrame
df = spark.read.csv("data.csv", header=True, inferSchema=True)

# Shuffle the DataFrame by randomizing the order of rows, shen print it out
df_shuffled = df.orderBy(rand()).show()
```

In this example, we first load the data into a Spark DataFrame. We then use the `rand()` function to shuffle the rows in the DataFrame randomly. Note that the output will change on each execution since it is randomized.

So for a simple input file of

```plaintext
Col A,Col B
Item 1,A
Item 2,B
Item 3,C
Item 4,D
```

The output of the code might look like this:

```plaintext
+------+-----+
| Col A|Col B|
+------+-----+
|Item 4|    D|
|Item 3|    C|
|Item 1|    A|
|Item 2|    B|
+------+-----+
```

Note that shuffling can be an expensive operation, so it's important to use it judiciously and only when necessary. It's also important to carefully manage the resources required for shuffling, such as memory and network bandwidth, to avoid overloading the system.

---

## Single Source of Truth (SSOT)

> A practice of structuring information models and associated schema such that every data element is mastered in only one place.

Source: https://dagster.io/glossary/single-source-of-truth-ssot

---

## Skew

> An imbalance in the distribution or representation of data.

Source: https://dagster.io/glossary/data-skew

Unlike most of the entries in our glossary, skew is not something engineers do to data, but a characteristic of the data itself.

Note: There is a second use of the term \_skew\_ in data engineering, namely "Imbalanced load across partitions, such that some partitions have lots of requests or data, and others have much less. Also known as \_hot spots\_." See "Skewed Workloads and Relieving Hot Spots" in the O'Reilly book \_Designing Data-Intensive Applications\_ by Martin Kleppmann

### Data skew definition:

Data skew, also known as skewness, refers to an imbalance in the distribution or representation of data. In the context of distributed computing, this imbalance often leads to inefficient processing because some nodes in the system end up processing significantly more data than others.

This can occur in several ways. For example, one common type of skew is **key-value skew**, where some keys in your data map to significantly more values than others. If your processing tasks involve these keys, they can take much longer to process than tasks involving other keys. This imbalance can lead to an overall slowdown in your pipeline because other nodes may be sitting idle while a few nodes are still processing.

Another type of skew is **time skew**, where certain time periods have significantly more data than others. This can also lead to imbalances and inefficiencies in your pipeline if not accounted for.

Strategies for dealing with data skew can involve redistributing the data, adjusting the granularity of tasks, using more robust statistics or models that can handle skew, or [pre-processing the data](https://dagster.io/glossary/data-preprocessing) to balance out the skew.

### Illustrating skewness using Python

We can illustrate the concept of data skewness using two distributions, one normal (symmetrical) and another skewed.

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Here's how you can do it using `matplotlib` and `scipy.stats`:

```python
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

# Create a normal distribution
mu, sigma = 0, 0.1 # mean and standard deviation
normal_dist = np.random.normal(mu, sigma, 1000)

# Create a skewed distribution
skewness = 5  # positive value means right skew
skewed_dist = stats.skewnorm.rvs(a = skewness, size=1000)

# Create subplots
fig, axs = plt.subplots(2, sharex=True)

# Plot normal distribution
axs[0].hist(normal_dist, bins=30, density=True, color='b', alpha=0.7)
axs[0].set_title('Normal Distribution')

# Plot skewed distribution
axs[1].hist(skewed_dist, bins=30, density=True, color='r', alpha=0.7)
axs[1].set_title('Skewed Distribution')

plt.show()
```

In this example, we first create a normally distributed set of data, with mean 0 and standard deviation 0.1. We then create a skewed distribution using `scipy.stats.skewnorm`. The `a` parameter controls the skewness of the distribution, with positive values indicating right-skew.

Then we plot histograms of these two distributions. The top blue 'Normal Distribution' histogram should show a symmetric bell curve, while the bottom red 'Skewed Distribution' histogram will be skewed to the right.

![](https://dagster.io/images/glossary/681399f654933b29e12fba6b_glossary-14.png)

---

## Snapshot

> A set point in time copy of data that can be used as a backup for recovery purposes.

Source: https://dagster.io/glossary/snapshot

---

## Software-defined Asset

> A declarative design pattern that represents a data asset through code.

Source: https://dagster.io/glossary/software-defined-assets

### Definition of Software-defined Asset:

A software-defined asset (SDA) is a declarative design pattern that represents a data asset through code. An asset is an object in persistent storage that captures some understanding of the world, and it can be any type of object, such as a database table or view, a file in local storage or cloud storage like Amazon S3, or a machine learning model.

[Software-defined assets](https://docs.dagster.io/guides/build/assets/defining-assets) are defined by writing code that describes the asset you want to exist, its upstream dependencies, and a function that can be run to compute the contents of the asset. This approach allows you to focus on the assets themselves—the end products of your data engineering efforts—rather than the execution of tasks.

Here are some key points about Software-defined Assets:

1. **Declarative Nature**: SDAs allow developers to declare what the end state of an asset should be, and the orchestrator takes care of the execution logic required to achieve that state. This shifts the focus from task execution to asset production.
2. **Observability and Scheduling**: SDAs provide enhanced observability into your data assets and allow for advanced scheduling. This makes it easier to understand the state of your assets and when they should be updated.
3. **Environment Agnosticism**: SDAs are designed to be environment-agnostic, meaning that the same asset definitions can be used across different environments, such as development and production, without changes to the asset code.
4. **Data Lineage**: SDAs have clear data lineage, which makes it easier to understand how data flows through your system and to debug issues when they arise.
5. **Integration with External Tools**: SDAs can be integrated with external tools like dbt, allowing the orchestrator to track the lineage of every individual table created by these tools.
6. **Rich Metadata and Grouping**: SDAs support rich metadata and grouping tools, which are useful for organizing and searching assets within large and complex organizations.
7. **Partitioning and Backfills**: SDAs support time partitioning and backfills out of the box, which is useful for managing historical data and ensuring data consistency.

In summary, Software-defined Assets represent a novel declarative, code-based approach to defining and managing data assets, providing clear lineage, observability, and the ability to work seamlessly across different environments.

[      Learn more about Software-defined Assets in the Dagster Documentation.    ](https://docs.dagster.io/guides/build/assets/defining-assets)

### An example of a Software-defined Asset in Python

Using the Dagster framework for data orchestration, here is an example of defining an asset in code:

This asset is generated by reading data from a CSV file, processing it, and then writing the processed data to a new CSV file (the Asset).

In this example, we will create a simple pipeline with three assets:

1. `raw_data`: Reads data from an input CSV file named `input_data.csv` into a Pandas DataFrame.
2. `processed_data`: Takes the raw data DataFrame as input, performs some processing (in this case, doubling the values of an existing column), and returns the processed DataFrame.
3. `write_processed_data`: Takes the processed DataFrame and writes it to a new CSV file named `processed_data.csv`.

Each asset is defined with the `@asset` decorator, and the dependencies between assets are implicitly defined by the function arguments. For example, `processed_data` depends on `raw_data` because it takes `raw_data` as an argument.

The assets are rendered automatically in the Dagster UI along with upstream and downstream dependencies.

![](https://dagster.io/images/glossary/681399f654933b29e12fbb25_software-defined-asset-simple-pipeline.png)

Note that all three assets will be persisted in memory, and will be observable, meaning we will capture metadata on each materialization of the asset.

```python
import pandas as pd
from dagster import asset

# Asset to read data from a CSV file
@asset
def raw_data():
    df = pd.read_csv('input_data.csv')
    return df

# Asset to process the raw data
@asset
def processed_data(raw_data):
    # Imagine some processing logic here, for example:
    # - Cleaning the data
    # - Filtering rows
    # - Transforming columns
    # For simplicity, we'll just add a new column with transformed data
    processed_df = raw_data.copy()
    processed_df['new_column'] = raw_data['existing_column'] * 2
    return processed_df

# Asset to write the processed data to a new CSV file
@asset
def write_processed_data(processed_data):
    processed_data.to_csv('processed_data.csv', index=False)
```

To make these assets discoverable by Dagster, you would make sure they are imported in your repository `defs`:

```python
# __init__.py
from dagster import Definitions, load_assets_from_modules

from . import assets

all_assets = load_assets_from_modules([assets])

defs = Definitions(
    assets=all_assets,
)
```

This repository now contains a sequence of Software-defined Assets that can be materialized to perform a data processing workflow. When you run this in Dagster, the system will automatically resolve the dependencies and execute the assets in the correct order, in an optimized fashion.

---

## Spill

> Temporarily transfer data that exceeds available memory to disk.

Source: https://dagster.io/glossary/spill-to-disk

### A definition of spilling to disk in data processing

"Spilling to disk" in the context of data engineering refers to a process where **data that cannot fit entirely in a computer's memory (RAM) is temporarily written or stored onto the computer's hard disk or other storage media**. This is typically done to prevent memory exhaustion and maintain the overall stability of a data processing system.

When working with large datasets or performing complex data transformations, there might be instances where the amount of data being processed exceeds the available memory. In such cases, the system needs a way to handle this excess data without causing errors or crashes. This is where spilling to disk comes into play.

Here's a simplified explanation of how spilling to disk works:

1. **Memory Usage:** When a data processing task begins, data is loaded into memory for faster access and processing. However, if the dataset is too large to fit entirely in memory, the system needs a strategy to handle this situation.
2. **Spill to Disk:** When the available memory is nearing its limit, the system will start moving excess data from memory to the hard disk. This data is written to temporary files on the disk. This frees up memory for new data to be processed without causing out-of-memory errors.
3. **Processing:** The data processing continues using the available memory. As new data is processed, it might again fill up the memory, leading to further spilling of excess data to disk as needed.
4. **Disk I/O:** Reading and writing data to and from the disk is generally slower compared to memory access. Therefore, while spilling to disk prevents memory exhaustion, it can also lead to a performance bottleneck due to the increased disk input/output (I/O) operations.
5. **Disk Cleanup:** Once the data processing task is complete, or when memory becomes more available, the system can clean up the temporary files on the disk. These temporary files are no longer needed, as the original data might already have been transformed or analyzed.

Spilling to disk is a mechanism that helps manage large datasets efficiently within the constraints of available memory. It's commonly used in data processing frameworks, database management systems, and other software tools where working with large volumes of data is a routine requirement. The goal is to strike a balance between utilizing memory for faster processing and leveraging disk storage to handle data that cannot fit in memory at once.

### An example of spilling to disk using Python

Like many self-contained examples, this one is a bit contrived, but we hope it helps illustrate the concept of "spilling to disk"".

The following Python code uses a hypothetical scenario of processing a large dataset in chunks. In this example, we'll generate random numbers as our dataset and simulate the spilling process by writing chunks of data to temporary files on the disk.

```python
import os
import tempfile

# Parameters
total_data_points = 1000
memory_capacity = 100  # Simulated memory capacity
chunk_size = 50  # Size of each chunk to process

# Generate random data points
data = [i for i in range(total_data_points)]

# Simulated memory
memory = []

# Simulated spilling process
def process_chunk(chunk):
    # Simulated data processing
    processed_chunk = [x * 2 for x in chunk]
    return processed_chunk

# Process the data in chunks
for i in range(0, total_data_points, chunk_size):
    chunk = data[i:i+chunk_size]

    # Check if chunk fits in memory
    if len(memory) + len(chunk) > memory_capacity:
        # Spill the excess data to a temporary file on disk
        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
            temp_file.write(bytes(str(process_chunk(memory)), 'utf-8'))
            spilled_filename = temp_file.name
            print(f"Spilled {len(memory)} data points to disk: {spilled_filename}")

        # Reset memory for the next chunk
        memory = []

    # Process the chunk in memory
    processed_chunk = process_chunk(chunk)
    memory.extend(processed_chunk)

# Process any remaining data in memory
if memory:
    processed_remaining_data = process_chunk(memory)
    print("Processed remaining data:", processed_remaining_data)

# Clean up spilled files (in a real scenario, you would handle this more robustly)
if spilled_filename:
    os.remove(spilled_filename)

print("Processing complete!")
```

       The output you generate form the above code will look something like:

```plaintext
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmpi195o90z
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmpippjkr0p
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmpq9xdjpdm
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmptnoway00
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmp13_tivh3
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmpsuizyjzx
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmpus6utws7
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmpyliozzaz
Spilled 100 data points to disk: /var/folders/yz/6a7b9cdef74h3ij9kl1mnopq40000rs/T/tmpn0y68wqy
Processed remaining data: [3600, 3604, 3608, 3612, 3616, 3620, 3624, 3628, 3632, 3636, 3640, 3644, 3648, 3652, 3656, 3660, 3664, 3668, 3672, 3676, 3680, 3684, 3688, 3692, 3696, 3700, 3704, 3708, 3712, 3716, 3720, 3724, 3728, 3732, 3736, 3740, 3744, 3748, 3752, 3756, 3760, 3764, 3768, 3772, 3776, 3780, 3784, 3788, 3792, 3796, 3800, 3804, 3808, 3812, 3816, 3820, 3824, 3828, 3832, 3836, 3840, 3844, 3848, 3852, 3856, 3860, 3864, 3868, 3872, 3876, 3880, 3884, 3888, 3892, 3896, 3900, 3904, 3908, 3912, 3916, 3920, 3924, 3928, 3932, 3936, 3940, 3944, 3948, 3952, 3956, 3960, 3964, 3968, 3972, 3976, 3980, 3984, 3988, 3992, 3996]
Processing complete!
```

Please note that this code is a simplified example for illustration purposes and is not recommended for actual production scenarios. In a real-world setting, you would likely use libraries like `pandas` or other data processing frameworks to handle large datasets more efficiently.

---

## Split

> Divide a dataset into training, validation, and testing sets for machine learning model training.

Source: https://dagster.io/glossary/dataset-splitting

### Data splitting definition:

**Data Splitting** is the process of dividing a dataset into training, validation, and testing sets in preparation for the training and testing of a machine learning model.

### Example of data splitting for machine learning, using Python

Here is a simple example of data splitting using the `train_test_split` function from the `sklearn` library. We will use the iris dataset from the `sklearn.datasets` module for simplicity.  Please note that you may need to [add the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

```python
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

print(f"Training data size (X_train): {X_train.shape}")
print(f"Test data size (X_test): {X_test.shape}")
print(f"Training labels size (y_train): {y_train.shape}")
print(f"Test labels size (y_test): {y_test.shape}")
```

\### Aside: Uppercase matrix, lowercase vectorThe convention of using an uppercase \`X\` and a lowercase \`y\`, as shown in our code above, is typical in machine learning and statistics. \`X\` usually represents the matrix of feature vectors, and since it's a matrix (which can be multi-dimensional), it's represented as an uppercase \`X\`. Each row in \`X\` represents a different instance (or sample), and each column represents a different feature.On the other hand, \`y\` typically represents the target variable (or labels) we are trying to predict, and it's usually a one-dimensional vector, hence the lowercase. Each element in \`y\` corresponds to the label or target value of the corresponding row in \`X\`.While it's not a strict rule, it's a common convention in machine learning and statistics to use \`X\` for the matrix of input data and \`y\` for the vector of target values.

In the above code:

- We first load the iris dataset which is a simple, pre-cleaned classification dataset. `X` contains the **feature values** and `y` contains the corresponding **labels**.
- We then use the `train_test_split` function from `sklearn.model_selection` to split our data.
- The `test_size` argument determines what fraction of the original data is used for the test set. In this case, we're using 20% of the data for testing and the remaining 80% for training.

The `train_test_split` function shuffles the dataset and then splits it. Shuffling is The `random_state` argument is used for initializing the internal random number generator, which will decide the splitting of data into train and test indices (provided shuffle=True, which is the default).  This is important because it ensures that the **split you generate is reproducible**. In other words, every time you run your code, you will get the exact same split of data, which can be important for debugging and comparison purposes.

The value you provide to `random_state` can be any integer (We use `42` but that is arbitrary.) The specific value is not inherently important; what matters is that using the same random\_state value across different runs will ensure that the same random splits are generated, given the other parameters to `train_test_split` are the same.

If you do not specify the `random_state` parameter or if you set it to `None`, then each time you run the code, a different split will be generated based on the numpy's random number state at the time `train_test_split` is called.

The final four lines in our program print out the following:

```plaintext trailing-newline
Training data size (X_train): (120, 4)
Test data size (X_test): (30, 4)
Training labels size (y_train): (120,)
Test labels size (y_test): (30,)
```

You can now use `X_train` and `y_train` to train your model, and `X_test` and `y_test` to evaluate its performance.

In the above Python script, `y_test` contains the labels for the test set. These are the true values that a machine learning model will aim to predict accurately.

The expression `y_test.shape` gives you the dimensions of this array. Since `y_test` is a one-dimensional array, the shape will be expressed as `(n,)`, where `n` is the number of instances (or rows) in the test set.

When we performed the train/test split with `test_size=0.2`, we reserved 20% of the data for the test set. The iris dataset contains 150 instances, so `y_test` contains 20% of 150, or 30 instances.

These labels will be used to evaluate the performance of a machine learning model. Once the model has made predictions for the `X_test` data, these predictions can be compared to the true labels in `y_test` to calculate metrics such as accuracy, precision, recall, etc.

---

## Standardize

> Transform data to a common unit or format to facilitate comparison and analysis.

Source: https://dagster.io/glossary/data-standardization

### Data standardization definition:

Data standardization is the process of transforming data into a common format that allows for easy comparison and analysis. This process is useful when working with data that is collected from different sources or has different units of measurement. Data standardization involves scaling the data to a common range, usually with a mean of 0 and standard deviation of 1.

### Data standardization example using Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

In Python, we can use the `StandardScaler` class from the sklearn.preprocessing module to perform data standardization. Here's an example:

```python
import numpy as np
from sklearn.preprocessing import StandardScaler

# create a sample dataset
data = np.array([[1, 2], [3, 4], [5, 6]])

# create a StandardScaler object
scaler = StandardScaler()

# fit and transform the data
scaled_data = scaler.fit_transform(data)

print(scaled_data)
```

This code will generate the following output:

```plaintext
[[-1.22474487 -1.22474487]
 [ 0.          0.        ]
 [ 1.22474487  1.22474487]]
```

In this example, we create a sample dataset with 3 rows and 2 columns. We then create a `StandardScaler` object and fit it to the data. Finally, we transform the data using the `fit_transform` method and print the scaled data. The output shows that each column has been scaled to have a mean of 0 and a standard deviation of 1.

---

## Stateless Protocol

> A communications protocol that treats each request as an independent transaction, without requiring the server to retain session information or status about each communicating partner for the duration of multiple requests.

Source: https://dagster.io/glossary/stateless-protocol

---

## Stored Procedure

> Precompiled and stored SQL statements and procedural logic for easy database operations and complex data manipulations.

Source: https://dagster.io/glossary/stored-procedure

### Stored procedure: a definition

A "stored procedure" is a database programming concept that refers to a set of precompiled and stored SQL (Structured Query Language) statements and procedural logic within a database management system (DBMS). These stored procedures can be executed by invoking a single command or function call, making it easier to perform common database operations and complex data manipulations.

Stored procedures are a valuable tool in data engineering for streamlining database operations, improving performance, enhancing security, and promoting code reusability. They are particularly useful for managing and processing large volumes of data efficiently and consistently within a database environment.

### Key aspects of stored procedures

Here are some key aspects of stored procedures in data engineering:

**Precompiled Logic**: Stored procedures contain one or more SQL statements and procedural code written in a database-specific language (e.g., PL/SQL for Oracle, T-SQL for SQL Server, or PL/pgSQL for PostgreSQL). The code is precompiled and stored in the database for improved performance compared to executing individual SQL queries.

**Encapsulation**: Stored procedures encapsulate database operations into a single unit. This means that complex data operations, such as [data transformations](https://data-transformation), [data validation](https://data-validation), or [data aggregation](https://data-aggregation), can be encapsulated within a stored procedure, simplifying our interactions with the database.

**Reusability**: Stored procedures can be reused across applications or parts of the same application. This promotes code modularity and reduces redundancy, as developers don't need to rewrite the same SQL logic in multiple places.

**Security and Access Control**: DBMSs often provide access control mechanisms for stored procedures. Developers can grant specific permissions to execute stored procedures while restricting direct access to tables or views. This helps enhance security by limiting the potential for unauthorized data access or modification.

**Performance Optimization**: Since stored procedures are precompiled and stored, they can lead to improved query optimization and execution plans. This can result in faster query execution, especially for complex operations.

**Transaction Management**: Stored procedures can be used to manage transactions within the database. Developers can define custom transaction boundaries, ensuring data consistency and integrity.

**Version Control**: Stored procedures can be versioned, allowing for easier management and tracking of changes over time. This can be valuable when maintaining complex database systems.

**Maintenance and Troubleshooting**: Centralizing database logic within stored procedures can make it easier to maintain and troubleshoot database-related issues. Developers can focus on optimizing and debugging the stored procedures themselves rather than hunting down scattered SQL queries in application code.

### An example of Stored Procedure in Python and PostgreSQL

![](https://dagster.io/images/glossary/681399f654933b29e12fb9e7_Postgresql_elephant.svg)

 This example requires a Postgres database. [See the setup steps here](https://database-setup).

To illustrate Stored Procedures using Python, here is a simple example using PostgreSQL as our database, and using [psycopg2](https://pypi.org/project/psycopg2/), the most popular PostgreSQL database adapter for the Python programming language.

In the [PostgreSQL setup steps](https://database-setup), we created a `band_members` table with the five members of the band Radiohead. In this example, we'll create a stored procedure that calculates the average age of Radiohead band members based on their birthdates and returns the result.

First, let's create the stored procedure directly in PostgreSQL:

As a reminder, you need to log in to your Postgres instance as follows (See [database instructions](https://database-setup) for details.)

```javascript trailing-newline
psql -U your_username -d dagsterdb
```

Once connected to the database, execute the following command to create a stored procedure:

```python
CREATE OR REPLACE FUNCTION calculate_average_age()
RETURNS FLOAT AS $$
DECLARE
    total_age INT := 0;
    member_count INT := 0;
    average_age FLOAT := 0.0;
    member_record RECORD;
BEGIN
    -- Create a cursor to fetch rows from the band_members table
    FOR member_record IN (SELECT EXTRACT(YEAR FROM age(current_date, created_date)) AS age_diff FROM band_members)
    LOOP
        total_age := total_age + member_record.age_diff;
        member_count := member_count + 1;
    END LOOP;

    -- Calculate then return the average age
    IF member_count > 0 THEN
        average_age := total_age / member_count;
    END IF;

    RETURN average_age;
END;
$$ LANGUAGE PLPGSQL;
```

Now, let's write a Python script to call this stored procedure and retrieve the average age:

```python
import psycopg2

# Database connection parameters
db_params = {
    'dbname': 'your_database_name',
    'user': 'your_username',
    'password': 'your_password',
    'host': 'your_host',
    'port': 'your_port',
}

try:
    # Connect to the PostgreSQL database
    connection = psycopg2.connect(**db_params)
    cursor = connection.cursor()

    # Call the stored procedure and retrieve the result
    cursor.callproc('calculate_average_age')
    result = cursor.fetchone()[0]

    print(f'The average age of band members is: {result:.2f} years')

except psycopg2.DatabaseError as e:
    print(f'Error: {e}')
finally:
    if connection:
        cursor.close()
        connection.close()
```

Make sure to replace `'your_database_name'`, `'your_username'`, `'your_password'`, `'your_host'`, and `'your_port'` with the actual connection details for your PostgreSQL database.

If you have followed this guide, the example would use:\* 'dbname': 'dagsterdb',\* 'host': 'localhost',\* 'port': '5432',\* 'user': will be the one you retrieved with the command `whoami`\* you can skip `'your_password'` if you have not set one.

This example demonstrates how to create a stored procedure in PostgreSQL to perform a specific data analysis task and then use a Python script to call that stored procedure and retrieve the result. We defined the logic in the database, and our application just had to call the Postgresql function with `cursor.callproc('calculate_average_age')`. Data engineers can use such stored procedures to encapsulate complex data operations in the database and leverage them for various analytical tasks.

And so, what is the average age of the band members?  Here is the output:

```javascript trailing-newline
The average age of band members is: 54.00 years
```

You can list all stored procedures in your Postgres database with the following SQL command:

```python
SELECT proname AS procedure_name,
       pg_get_function_identity_arguments(oid) AS arguments
FROM pg_proc
WHERE pronamespace = (SELECT oid FROM pg_namespace WHERE nspname = 'public');
```

### About Dagster and stored procedures:

Since Dagster is a Python framework, you can use Python code to call stored procedures as we have done above. That being said, depending on your use case, it is worth considering running your SQL queries directly from within Dagster and storing the queries as files/text. This will provide version control for your stored procedures and keep the logic close to the scheduling rather than needing to search through multiple places.

---

## Streaming Data

> Data that is generated continuously by thousands of data sources, sending data records simultaneously and in small sizes.

Source: https://dagster.io/glossary/streaming-data

---

## Structured Query Language (SQL)

> A standard programming language specifically for managing and querying data in relational databases.

Source: https://dagster.io/glossary/structured-query-language-sql

---

## Synchronize

> Ensure that data in different systems or databases are in sync and up-to-date.

Source: https://dagster.io/glossary/data-synchronizing

### Data synchronization definition:

In the context of data engineering and data pipelines, data synchronization refers to the process of ensuring that data is consistent and up to date across multiple systems or databases. This is particularly important in situations where data is being transferred or shared between different systems, such as in a data warehousing or ETL (extract, transform, load) pipeline.

Some common best practices for data synchronization include:

- Establishing clear rules for data ownership and access permissions.
- Ensuring that data is properly normalized and structured to facilitate synchronization.
- Using appropriate tools and technologies to automate the synchronization process and minimize the risk of errors or inconsistencies.
- Monitoring the synchronization process closely to ensure that any issues or discrepancies are quickly identified and resolved.

Python offers a variety of libraries and tools that can be used for data synchronization, depending on the specific use case and data sources involved. For example, tools like Apache Kafka and Apache Spark can be used for real-time data streaming and synchronization. Other tools that can be used for data synchronization in Python include SQLAlchemy, Dask, and AWS Glue.

---

## Thread

> Enable concurrent execution in Python by decoupling tasks which are not sequentially dependent.

Source: https://dagster.io/glossary/data-threading

### Definition of threading in a Python environment

Threading refers to the use of multiple threads of execution within a single process. Each thread runs independently while sharing the same memory space, allowing for parallel execution of tasks.

Threading can enhance program efficiency, particularly in I/O-bound tasks or tasks involving concurrent execution, though it's important to note that due to Python's Global Interpreter Lock (GIL), true parallelism isn't achieved for CPU-bound tasks.

Python's threading module is built on top of the lower-level `_thread` module, which in turn is built on top of an even lower-level module that communicates directly with the operating system.

Threading is a technique for decoupling tasks which are not sequentially dependent. For instance, threading is good for I/O-bound tasks, such as making multiple requests to an API or performing read/write operations on a database.

### Example of threading in Python

Here's a very simple example to illustrate the use of threading in Python:

```python
import threading
import time

def print_numbers():
    for i in range(10):
        print(i, flush=True, end="")
        time.sleep(0.1)

def print_letters():
    for letter in 'abcdefghij':
        print(letter, flush=True, end="")
        time.sleep(0.1)

t1 = threading.Thread(target=print_numbers)
t2 = threading.Thread(target=print_letters)

t1.start()
t2.start()

t1.join()
t2.join()
```

In this code, two threads are created: one that prints the numbers from 0 to 9, and another that prints the letters 'a' to 'j'. These threads are then started with the `start` method, and the program waits for both threads to finish with the `join` method. The result is that numbers and letters are printed out concurrently.

Your output will look something like this:

```javascript trailing-newline
0a1b2c3d4e5f6g7h8i9j%
```

The `flush=True` argument to the `print()` function ensures that the output is printed immediately, and the `time.sleep(0.1)` introduces a small delay to help observe the overlapping execution of threads. The output from this code should be a mix of numbers and letters printed out concurrently, but we caused this artificially as the order of execution for threads is not guaranteed. The threads scheduler determines the order, and it can vary every time you run the program.  Feel free to remove the `time.sleep(0.1)` commands and experiment.

When considering the use of threading in your pipeline design, you should also research multithreading, and understand the tradeoffs between these approaches. [Python's Global Interpreter Lock (GIL)](https://wiki.python.org/moin/GlobalInterpreterLock) allows only one thread to execute at a time in a single process, which can be a limiting factor for CPU-bound tasks. This is one of the reasons why Python's threading module isn't suitable for tasks that require heavy CPU computation. For these tasks, multiprocessing is generally a better option.

[      Jump to the entry for 'Multiprocess'.    ](https://dagster.io/glossary/multiprocessing)

---

## Tokenize

> Convert data into tokens or smaller units to simplify analysis or processing.

Source: https://dagster.io/glossary/data-tokenizing

### Tokenization definition:

Tokenization is the process of breaking down a piece of text into individual words or tokens. This is a common technique used in data engineering to prepare text data for analysis.

### Tokenizing example using Python:

Here are some practical examples of tokenization in data engineering using Python-specific functions. Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code:

Using the `split()` function: The split() function can be used to split a string into a list of words based on a delimiter, such as a space or a comma.

For example:

```python
text = "This is a sample sentence."
tokens = text.split()
print(tokens)
```

This would output:

```plaintext
['This', 'is', 'a', 'sample', 'sentence.']
```

Using the word\_tokenize() function from the NLTK library: The Natural Language Toolkit (NLTK) is a popular Python library for natural language processing. The word\_tokenize() function from the NLTK library can be used to tokenize text data into individual words. For example:

```python
import nltk
nltk.download('punkt')

from nltk.tokenize import word_tokenize

text = "This is a sample sentence."
tokens = word_tokenize(text)
print(tokens)
```

Will output:

```plaintext
['This', 'is', 'a', 'sample', 'sentence', '.']
```

Using regular expressions: [Regular expressions](https://docs.python.org/3/library/re.html) can be used to define patterns for tokenizing text data. For example, the following code uses regular expressions to split a string into words based on whitespace and punctuation:

```python
import re

text = "This is a sample sentence."
tokens = re.findall(r'\b\w+\b', text)
print(tokens)
```

This code would produce the following output in the terminal:

```plaintext
['This', 'is', 'a', 'sample', 'sentence']
```

Using the `split()` function with a regular expression pattern: The `split()` function can also be used with regular expression patterns to tokenize text data. For example:

```python
import re

text = "This is a sample sentence."
tokens = re.split('\W+', text)
print(tokens)
```

This will yield:

```plaintext
['This', 'is', 'a', 'sample', 'sentence', '']
```

These are just a few examples of how *tokenization* can be used to prepare data for analysis and extract insights from text.

---

## Transform

> Convert data from one format or structure to another.

Source: https://dagster.io/glossary/data-transformation

### Data Transformation definition:

Data transformation in the context of data engineering refers to the process of **converting, reshaping, or manipulating raw data into a structured and usable format**.

Data transformation involves applying various operations such as filtering, aggregation, cleaning, and normalization to ensure that the data is consistent, accurate, and ready for analysis. Data transformation plays a crucial role in preparing data for storage, integration, and analysis, enabling organizations to derive valuable insights and make informed decisions from their data assets.

This said, data can be transformed at many different steps in a data pipeline.  A key to success is to be able to track and observe any data transformation steps introduced in the process, and build that into the [data lineage](https://dagster.io/glossary/data-lineage).

### Data transformation approaches:

Transforming data on the fly (i.e., during the extraction or loading stage) can be more efficient and faster, as it avoids the need for a separate transformation layer. However, it can also be more complex and harder to maintain, especially as the volume and complexity of the data increase.

Using a transformation layer like dbt can provide several benefits. For example, it can simplify and centralize the transformation logic, making it easier to maintain, test, and audit. It can also provide features like version control, documentation, and collaboration tools. Additionally, dbt can help enforce data quality and consistency by providing automated data validation and testing.

In general, using a transformation layer like dbt can be a best practice for data processing pipelines, especially in larger and more complex environments. However, it's important to carefully evaluate the specific needs and trade-offs of your project to determine the best approach.

There are many Python functions that can be used for data transformation in data engineering. Some of the most frequently used ones include:

1. `map()` and `filter()`: These functions can be used to transform and filter elements in a list or other iterable. For example, `map()` can be used to apply a function to each element in a list, while `filter()` can be used to remove elements from a list that do not meet a certain condition.
2. `apply()`: This function is part of the Pandas library and can be used to apply a function to each row or column of a DataFrame.
3. `join()` and `merge()`: These functions can be used to combine data from multiple sources. `join()` is used to combine data based on a common index, while `merge()` can be used to combine data based on common columns.
4. `groupby()`: This function is also part of the Pandas library and can be used to group data by one or more columns and perform operations on each group.
5. `agg()`: This function is used with `groupby()` and can be used to apply multiple aggregation functions to each group.
6. `pivot_table()`: This function is also part of the Pandas library and can be used to create a pivot table from a DataFrame.
7. `split()` and `join()`: These functions can be used to split a string into a list and join a list into a string, respectively.
8. `datetime()` and `timedelta()`: These functions can be used to work with dates and times in Python, such as converting a string to a datetime object or calculating the difference between two dates.
9. `json.loads()` and `json.dumps()`: These functions can be used to convert a JSON string to a Python object and vice versa.
10. `numpy.reshape()`: This function is part of the NumPy library and can be used to reshape an array into a new shape.

---

## Unstructured Data Analysis

> Analyze unstructured data, such as text or images, to extract insights and meaning.

Source: https://dagster.io/glossary/unstructured-data-analysis

### Unstructured Data Analysis definition

Unstructured Data Analysis (UDA) is the process of analyzing and extracting meaningful insights from data that does not have a pre-defined data model or structure. Unstructured data can take many forms, including text, images, audio, video, and social media posts. This type of data is often generated in large volumes and can be difficult to manage and analyze using traditional data analysis techniques.

Unstructured data is more challenging to analyze than structured data because, as its name infers, it lacks the organization and predictability of structured data. Structured data is typically stored in a database or spreadsheet, with a defined schema that outlines the data fields, data types, and relationships between tables. This makes it easier to query and analyze using standard SQL queries or data visualization tools.

In contrast, unstructured data is often stored in a raw format, such as text documents, images, or videos, with no predefined structure or schema. This means that there is no easy way to extract meaning from the data without first processing it using specialized tools and techniques. Furthermore, unstructured data is often incomplete, noisy, and heterogeneous, making it difficult to clean and prepare for analysis.

To effectively analyze unstructured data, data engineers need to use a variety of techniques such as natural language processing, computer vision, and machine learning. These techniques allow data engineers to extract meaning from unstructured data and make it useful for downstream analysis, such as text sentiment analysis or image classification.

### Unstructured Data Analysis techniques

Some common techniques used in data engineering for unstructured data analysis in Python include:

[**Text preprocessing**](https://dagster.io/glossary/data-preprocessing): This involves cleaning, normalizing, and tokenizing text data. Common preprocessing techniques include removing stop words, stemming, and lemmatization.

[**Sentiment analysis**](https://sentiment-analysis): This involves using natural language processing techniques to analyze the sentiment or emotion expressed in a piece of text. Python libraries like TextBlob and NLTK can be used for sentiment analysis.

[**Named entity recognition**](https://dagster.io/glossary/named-entity-recognition): This involves identifying and extracting named entities like people, organizations, and locations from text data. Python libraries like SpaCy and NLTK can be used for named entity recognition.

**Topic modeling**: This involves identifying the underlying topics or themes in a collection of documents. Techniques like Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) can be used for topic modeling in Python.

**Image processing**: This involves analyzing and manipulating images using Python libraries like Pillow and OpenCV. Common tasks include resizing, cropping, and applying filters to images.

**Audio processing**: This involves analyzing and manipulating audio files using Python libraries like LibROSA and Pydub. Common tasks include converting audio files to different formats, extracting features like tempo and pitch, and applying filters to audio signals.

**Data visualization**: This involves creating visual representations of unstructured data to aid in analysis and interpretation. Python libraries like Matplotlib and Seaborn can be used for data visualization.

---

## Upsert

> Update a record or insert a new record if it does not yet exist.

Source: https://dagster.io/glossary/data-upsert

### Data Upsert: A definition

'Upsert' is a operation that combines the actions of 'update' and 'insert' when working with any system of records. It refers to the process of either updating an existing record if it already exists or inserting a new record if it does not, based on specified criteria, typically using key fields to identify records uniquely.

[      Jump to the code example ↓    ](#)

### Data Upserting in Data Engineering

Record upserting is a key concept in data engineering. It ensures that the latest information is correctly stored and that duplicates are avoided.

In a typical database, you have two primary operations: "INSERT" to add new records and "UPDATE" to modify existing records. However, in many real-world scenarios, you may not always know whether a record already exists. This is where *upserting* comes into play.

- If a record with the specified *key* field already exists, an upsert operation will update the existing record with new data.
- If a record with the specified *key* fields doesn't exist, an upsert operation will insert a new record with the provided data.

Efficient upsert operations are critical in high-throughput systems. Data engineers need to optimize their database schemas, indexing strategies, and query execution plans to ensure fast upserts, especially in scenarios where a large volume of data is involved.

Depending on the specific use case and database system, different strategies for upserting data may be employed. These can include using database-specific syntax like "MERGE" or "UPSERT," or using conditional logic within code to perform upserts.  We provide database-specific examples below.

### Basic upserting considerations:

**Key Fields**: Upserting usually relies on one or more *key* fields that uniquely identify a record. These *key* fields are used to determine if a record already exists in the database.

**Upserting and Concurrency Control**: concurrency control is crucial. Multiple users or processes may attempt *upsert* operations simultaneously. To prevent conflicts and ensure data consistency, database systems often provide mechanisms like locks, transactions, or optimistic concurrency control.

### Dedicated upsert commands

Many modern database management systems (DBMS) offer specific upsert commands or functionalities to simplify the process of inserting or updating records. The specific syntax and implementation can vary between different database systems. Here are some examples of upsert commands in popular DBMS:

- **MySQL/MariaDB**, you can use the `INSERT INTO ... ON DUPLICATE KEY UPDATE` statement to perform an upsert operation. If a duplicate key violation occurs, it updates the existing record with new values. [See the docs](https://dev.mysql.com/doc/refman/8.0/en/insert-on-duplicate.html).
- **PostgreSQL** provides the `INSERT INTO ... ON CONFLICT DO UPDATE` syntax to perform an upsert. You specify the conflict resolution criteria and the update action. [See the docs](https://www.postgresql.org/docs/current/sql-insert.html#SQL-ON-CONFLICT).
- **SQL Server** offers the `MERGE` statement, which allows you to perform both insert and update actions based on a specified condition. It is a versatile way to implement upserts. [See the docs](https://learn.microsoft.com/en-us/sql/t-sql/statements/merge-transact-sql?view=sql-server-ver16).
- **Oracle Database** supports the `MERGE INTO` statement, similar to SQL Server's `MERGE`, which allows you to perform conditional insert and update operations in a single statement. [See the docs](https://docs.oracle.com/en/database/oracle/oracle-database/12.2/sqlrf/MERGE.html#GUID-5692CCB7-24D9-4C0E-81A7-A22436DC968F__GUID-3453B81A-A7FA-4D63-AD97-A0E86B9CF089).
- **SQLite**, you can use the `INSERT OR REPLACE INTO` statement to perform an upsert. It replaces the existing record if a conflict occurs. The specific `UPSERT` syntax was added to SQLite with version 3.24.0.  [See the docs](https://www.sqlite.org/lang_upsert.html)
- **Cassandra**, you can use the `INSERT INTO ... IF NOT EXISTS` statement to insert a record if it does not exist. This is a specific use case of upserting in a NoSQL context. [See the docs](https://docs.datastax.com/en/cql-oss/3.x/cql/cql_reference/cqlInsert.html#cqlInsert__ime-value).
- In **MongoDB**, you typically use the [`updateOne()`](https://www.mongodb.com/docs/manual/reference/method/db.collection.updateOne/#mongodb-method-db.collection.updateOne) or [`updateMany()`](https://www.mongodb.com/docs/manual/reference/method/db.collection.updateMany/#mongodb-method-db.collection.updateMany) methods to perform upserts. These methods allow you to specify update criteria and insert new documents if no matching documents are found.  The [`update`](https://www.mongodb.com/docs/manual/reference/command/update/#mongodb-dbcommand-dbcmd.update) command modifies documents in a collection.  [See the docs](https://www.mongodb.com/docs/manual/reference/method/db.collection.updateMany/).
- **AWS DynamoDB**, you can use the `PutItem` operation with conditional expressions to perform upserts. It allows you to specify conditions for inserting or updating an item. [See the docs](https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_PutItem.html).

The syntax and behavior of upsert commands may vary, and not all database systems provide dedicated upsert statements. In some cases, you may need to implement custom logic to achieve upsert functionality, combining conditional inserts and updates within application code or stored procedures.

**DuckDB** for instance does not (at time of writing - version 0.9.2) have a dedicated UPSERT command, but rather encourages the use of [INSERT](https://duckdb.org/docs/sql/statements/insert) and [UPDATE](https://duckdb.org/docs/sql/statements/update).

### Data Upsert: Files vs. databases

While the concept originates from database management systems, upserting can equally be applied to the large file formats associated with data engineering. When dealing with files, an "upsert" operation would involve reading the file, performing the insert or update operation in memory (like in a DataFrame), and then writing the modified DataFrame back to file.

However, it's important to note that a file-based upsert process is not as straightforward as in a database system. File formats do not natively support transactions or concurrent modifications like a database does. Each upsert operation would typically involve reading the entire file, making the necessary changes, and then writing it back, which can be resource-intensive for large datasets. Additionally, handling concurrent updates can be challenging and might require additional mechanisms like file locks or versioning.

For instance, as a columnar storage format, [Parquet](https://parquet.apache.org/docs/overview/) is not designed for in-place updates or insertions. In Parquet, data is typically written once and then read many times, and updates usually involve creating new versions of the entire Parquet file rather than modifying existing records.

When designing a process requiring upsert steps, you would be advised to adopt an extension of Parquet like Delta Lake that adds support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. It provides features like INSERT, UPDATE, DELETE, and MERGE operations on data stored in Parquet files.

### A basic example of record upserting in Python using SQLite

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

#### About SQLite

SQLite is a C-language library that implements a small, fast, self-contained SQL database engine. From this perspective, it's great for a simple tutorial, but not reflective of tools used in Data Engineering.

#### About our example

In this example we:\* Create a simple database called `languages.db` with a table `records`\* Define a function for the upsert operation. This function will insert a new record if the ID doesn't exist, or update the existing record if the ID is found.\* Use this function to perform upsert operations\*  retrieve and print all records from the table `records`.

Note that here we are not using SQLite's [`INSERT OR REPLACE INTO`](https://www.sqlitetutorial.net/sqlite-replace-statement/) or [`UPSERT`](https://www.sqlite.org/lang_upsert.html) in order to illustrate the basic logic of an upsert command, namely check if a record exists, update it if it does, or create it if it does not.

```python
import sqlite3

# Connect to SQLite database (it will be created if it doesn't exist)
conn = sqlite3.connect('languages.db')

# Create a cursor object using the cursor() method
cursor = conn.cursor()

# Create table
cursor.execute('''CREATE TABLE IF NOT EXISTS records
               (id INT PRIMARY KEY, name TEXT, release_year INT)''')

# Commit the changes then close the connection
conn.commit()
conn.close()


def upsert_record(conn, id, name, release_year):
    cursor = conn.cursor()

    # Check if the record exists
    cursor.execute('SELECT * FROM records WHERE id=?', (id,))
    data = cursor.fetchone()

    if data is None:
        # Insert a new record
        cursor.execute('INSERT INTO records (id, name, release_year) VALUES (?, ?, ?)', (id, name, release_year))
    else:
        # Update the existing record
        cursor.execute('UPDATE records SET name=?, release_year=? WHERE id=?', (name, release_year, id))

    # Commit the changes
    conn.commit()

def fetch_all_records(conn):
    cursor = conn.cursor()

    # Execute a query to fetch all records
    cursor.execute('SELECT * FROM records')

    # Fetch all rows from the database
    return cursor.fetchall()

# Reconnect to the database
conn = sqlite3.connect('example.db')

# Upsert some records
upsert_record(conn, 1, 'Plankalkul', 1940)
upsert_record(conn, 2, 'SQL', 1970)
upsert_record(conn, 3, 'Python', 1991)
upsert_record(conn, 4, 'Rust', 2015)

# Upsert an existing record
upsert_record(conn, 2, 'SQL', 1972)  # This will update the existing record

# Fetch and print all records
records = fetch_all_records(conn)
for record in records:
    print(record)

# Close the connection
conn.close()
```

The output from this script will be:

```plaintext
(1, 'Plankalkul', 1940)
(2, 'SQL', 1972)
(3, 'Python', 1991)
(4, 'Rust', 2015)
```

       As you will see the entry with `id` of `2` properly reflects the first release date of SQL in 1972.

### Advanced upserting considerations

While upserting is a basic and very common operation, there are some nuances to improving its performance and reliability.  If you look at the database specific commands, you will notice that many use a different logic than the one we spelt out.

The basic approach is:

1. Lookup a record with a given id
2. Update it if it exists
3. Create it if it does not exist.

While this logic is sound, it requires several calls, creating more overhead. It also opens up some potential issues of concurrency and interpretation.  Therefore, it is generally more efficient to use the following logic:

1. Update a record with a given id
2. If the update failed, create the record.

Aaron Bertrand explains the benefits of this approach [in this article](https://sqlperformance.com/2020/09/locking/upsert-anti-pattern).  You will notice that the [database specific approaches listed above](#) often use this pattern.

---

## Upstream

> In data processing, refers to the tasks, operations, or stages of processing occurring or located before a particular stage in a specified direction or flow.

Source: https://dagster.io/glossary/upstream

---

## Validate

> Check data for completeness, accuracy, and consistency.

Source: https://dagster.io/glossary/data-validation

### Data validation definition:

Data validation is a critical step in the data engineering process to have trusted, complete data. Data validation can be performed at various stages, including during data ingestion, transformation, and loading.

The following are some best practices for data validation in the context of modern data pipelines:

- **Define clear validation rules:** It is essential to define clear validation rules to ensure that the data is validated correctly. These rules should be based on the data requirements, such as data type, format, and constraints.
- **Validate data as early as possible**: Data validation should be performed as early as possible in the data pipeline to detect errors as soon as possible. This approach can save time and resources by preventing the propagation of errors.
- **Use automated validation**: Automated validation can help ensure that the data is validated consistently and accurately. Automation can include using tools, scripts, or frameworks that check the data against predefined validation rules.
- **Monitor data quality:** Monitoring data quality is an ongoing process that involves tracking the data quality over time. This approach can help detect data quality issues early and take corrective actions.

### Data validation example using Python:

Please note that you need to [have the necessary Python libraries installed in your Python environment](https://dagster.io/glossary) to run this code.

Python provides several libraries and functions for data validation. Let's look at three of them:

**Pydantic**: Pydantic is a data validation library that provides runtime type checking and validation of data structures. Dagster uses Pydantic internally to validate data for configuration or resources, for example. Here’s an example of Pydantic in action:

```python
from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str
    age: int

user_data = {"id": 1, "name": "John Doe", "email": "johndoe@example.com", "age": 30}
user = User(**user_data)
print(user)
```

This will output:

```plaintext trailing-newline
id=1 name='John Doe' email='johndoe@example.com' age=30
```

**Cerberus**: Cerberus is a lightweight and extensible data validation library that can validate complex data structures. Let’s look at an example of Cerberus in action:

```python
from cerberus import Validator

schema = {"name": {"type": "string"}, "age": {"type": "integer", "min": 18}}

data = {"name": "John Doe", "age": 25}

v = Validator(schema)
if v.validate(data):
    print("Data is valid")
else:
    print("Data is invalid")
    print(v.errors)
```

Output yielded by `v.errors`:

```javascript trailing-newline
Data is valid
```

**Great Expectations**: Great Expectations is a data validation library that can validate data quality and test data pipelines. It provides a range of features, including automated data profiling, data documentation, and testing data pipelines.

Here’s an example:

We can train Great Expecatations on a small data sample ( `small_dataset.csv` ), then apply the learnt rules to  our large dataset ( `large_dataset.csv` ).  The Small and Large datasets can be anything youlike, but should have a list of unique ids inthe `id` column:

You can use a sample as follows for the large dataset, and use just the first 10 rows in your small dataset.

```plaintext
id,Col A,Col B,Col C,Col D,Col E,Col F,Col G,Col H,Col I,Col J,Col K,Col L,Col M
1,8,5,8,10,7,0,0,5,2,8,9,4,9
2,5,8,4,3,10,6,8,6,2,2,2,4,7
3,9,8,5,10,6,3,1,2,5,5,7,3,5
[...]
996,8,8,5,4,3,8,5,9,9,7,7,5,0
997,8,6,6,7,6,5,5,4,7,7,0,5,8
998,1,9,8,1,4,7,10,3,6,5,4,1,4
999,1,5,1,1,4,0,1,5,5,5,1,2,1
1000,8,8,1,7,7,10,9,10,5,7,5,2,4
```

And apply this code to it:

```python
import great_expectations as ge

# Build up expectations on a representative sample of data and save them to disk
train = ge.read_csv("small_dataset.csv")
train.expect_column_values_to_not_be_null("id")
train.save_expectation_suite("my_expectations.json")

# Load in a new batch of data and test it against our expectations
test = ge.read_csv("large_dataset.csv")
validation_results = test.validate(expectation_suite="my_expectations.json")

# Take action based on the results
if validation_results["success"]:
    print ("The validation passed")
else:
    raise Exception("The validation failed")
```

This will output:

```javascript
The validation passed
```

So what is going on here?

1. We import the Great Expectations library
2. We use the \`ge.read\_csv()\`\` method to read in a CSV file containing a representative sample of data. In this case, the CSV file is named large\_dataset.csv.
3. We then use the \`expect\_column\_values\_to\_not\_be\_null()\`\` method to build an expectation that the "id" column in the data should not contain null values.
4. After building up our expectations, we use the `save_expectation_suite()` method to save them to disk in a JSON format. The resulting expectation suite is saved to a file named `my_expectations.json` .
5. Load in a new batch of data using the ge.read\_csv() method, again reading from the large\_dataset.csv file.
6. Use the `validate()` method to test the new data against our expectations. The `expectation_suite` parameter specifies the name of the JSON file containing our expectations.
7. The `validate()` method returns a dictionary of validation results. We check the "success" key of this dictionary to see if the validation passed or failed. If it passed, we print a message indicating that the validation was successful. If it failed, we raise an exception with a message indicating that the validation failed:

```python
Traceback (most recent call last):
  File "/Users/myname/validation-test.py", line 16, in <module>
    raise Exception("The validation failed")
Exception: The validation failed
```

Thanks to Ian Whitestone for [this example](https://ianwhitestone.work/hello-great-expectations/)

---

## Vectorize

> Executing a single operation on multiple data points simultaneously.

Source: https://dagster.io/glossary/data-vectorization

### Definition of vectorizing

**Vectorization** refers to improving the performance of operations on data, particularly with large data sets, by executing a single operation on multiple data points simultaneously — a significant characteristic of array programming.

Traditionally, many data management tasks are done in a loop, where each operation is performed sequentially on each data item. This is known as **scalar computing**. However, this approach can be quite slow when dealing with large amounts of data.

**Vectorization**, on the other hand, allows for the parallelization of computations. Instead of performing operations one data point at a time, vectorization allows these operations to be performed on whole arrays of data at once. This technique takes advantage of the architecture of modern CPUs and GPUs, which are designed to perform these types of parallel operations more efficiently. This is particularly prominent in libraries such as *NumPy* in Python, which is optimized for vectorized operations.

For example, let's say we have two large arrays of numbers, and we want to add the corresponding numbers in these arrays together. Without vectorization, we might use a for loop to go through each pair of numbers one by one and add them together. With vectorization, we can add the entire arrays together in one operation, which can result in a significant speedup.

### When vectorization should be used...

Here are some conditions where you should consider vectorizing your data:

1. **Handling Large Data Sets**: If you're working with large amounts of data, the speedup provided by vectorization can be substantial. The larger the dataset, the more noticeable the speedup usually is.
2. **Performing Mathematical and Statistical Operations**: Vectorization is very beneficial when performing mathematical or statistical operations on arrays of data. *NumPy*, for example, provides vectorized versions of many functions that operate on whole arrays of data at once.
3. **When Code Readability is Important**: Vectorized operations can often be written more concisely than their non-vectorized counterparts, leading to more readable code. For example, adding two arrays together with `a + b` is more readable than using a loop to add corresponding elements together.
4. **In Machine Learning Applications**: Many machine learning algorithms require operations on large matrices or arrays. Libraries like *NumPy* or *scikit-learn*, which are optimized for vectorized operations, are usually the best tools for these kinds of tasks.

### ...and when you shouldn't.

Vectorization is not always the best solution. There are situations where it might not be beneficial or even possible to use vectorized operations. This includes cases where the operations on the data cannot be performed in parallel because each operation depends on the result of the previous one. In such cases, traditional loop-based operations might be necessary.

### An example of data vectorization in Python using NumPy

Let's look at an example of data vectorization in Python using *NumPy*. In this example, we're going to generate two large arrays of random numbers and then perform some mathematical operations on them.

```python
import numpy as np
import time

# Create two large arrays of random numbers
array_size = 10**6
A = np.random.rand(array_size)
B = np.random.rand(array_size)

# Perform operations using loops (non-vectorized approach)
start_time = time.time()
C = np.empty(array_size, np.float64)
for i in range(array_size):
    C[i] = A[i] + 2*B[i] - A[i]*B[i]
end_time = time.time()

print("Time taken using loops: {:.6f} seconds".format(end_time - start_time))

# Perform the same operations using vectorized approach
start_time = time.time()
C_vectorized = A + 2*B - A*B
end_time = time.time()

print("Time taken using vectorized operations: {:.6f} seconds".format(end_time - start_time))

# Check if the results of the two methods are close enough
print("Are the results close? ", np.allclose(C, C_vectorized))
```

This script will create two large arrays of random numbers. It then perform some calculations on these arrays using a for loop (the non-vectorized approach), and measure the time it takes. Then, it performs the same calculations using vectorized operations, and again measure the time it takes. Finally, we check that the results of the two methods are close enough to be considered equal.

You'll see that the vectorized operations are significantly faster than the loop. This is a simple example, but it demonstrates the power of vectorization when working with large arrays of data.  Here are the results on my M1 Mac:

```javascript
Time taken using loops: 0.412291 seconds
Time taken using vectorized operations: 0.007932 seconds
Are the results close?  True
```

### An example of vectorization in Python using scikit-learn

Next, let's look as a slightly more advanced example of using vectorized operations in machine learning using *scikit-learn* and *pandas*.

In this example, we'll create a simple classifier for the Iris dataset, which is a built-in dataset in *scikit-learn*. The Iris dataset contains measurements of 150 iris flowers from three different species.

```python
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = datasets.load_iris()
X = iris.data  # we only take the first two features.
y = iris.target

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features (mean=0, variance=1) using a vectorized operation
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train a logistic regression classifier
clf = LogisticRegression(random_state=42)
clf.fit(X_train_scaled, y_train)

# Predict the test set results
y_pred = clf.predict(X_test_scaled)

# Output the accuracy of the classifier
print('Accuracy: {:.2f}'.format(accuracy_score(y_test, y_pred)))
```

In this script:

- We first load the Iris dataset, and split it into a training set and a test set.
- We then standardize the features to have zero mean and unit variance. This is done using a vectorized operation in the `StandardScaler` class. The `fit_transform` method computes the mean and standard deviation of the training set, subtracts the mean from each feature, and then divides by the standard deviation. The `transform` method applies this same transformation to the test set.
- We then train a logistic regression classifier on the training data. This involves several vectorized operations, including the computation of the gradient of the loss function.
- We use the trained classifier to predict the species of each flower in the test set. This again involves vectorized operations, as the prediction for each instance is computed as a weighted sum of its features.
- Finally, we compute the accuracy of the classifier, which is the proportion of test instances that were correctly classified. This again is a vectorized operation, as it involves comparing two arrays of predictions and true labels.

The final `print()` command will output the accuracy of the logistic regression model on the test dataset.

The accuracy is calculated as the proportion of correct predictions out of total predictions. It ranges from 0 to 1, with 1 indicating that every single prediction made by the model was correct, and 0 indicating that every prediction was incorrect. For a well-tuned logistic regression model and the Iris dataset, which is relatively simple and well-behaved, we might expect an accuracy in the high 90% range (0.90 to 1.00).

However, keep in mind that the randomness from the `train_test_split` function (defined by `random_state=42` in this example) means that your result might not be exactly the same each time you run the code.

Here's what the output might look like:

```javascript trailing-newline
Accuracy: 0.97
```

This means that the model correctly predicted the species of the iris flowers for 97% of the instances in the test set. This is a fairly high accuracy, indicating that our model is performing well on this particular dataset.  If you set `test_size` to 0.3 or less, you would expect an accuracy of 100%.

### vectorizing vs. vector databases

The term "vector" as used in vector databases is related to, but not exactly the same as, the concept of "vectorization" in programming and data science.

Vector databases (also known as "vector search engines", or more broadly a "vector store"), are a type of database (or component or module within a larger system) optimized for performing operations on vectors in high dimensional spaces. These vectors often represent complex, multidimensional data, such as images, sound, and text, in a form that machine learning models can understand. The databases are designed to efficiently perform nearest neighbor search in high dimensional spaces, which is a common operation in many machine learning tasks.

So in the context of vector databases, a "vector" is a representation of some piece of data in a high-dimensional space, as opposed to our definition above (performing operations on entire arrays of data at once).

So both concepts involve the manipulation of arrays or lists of numbers, they're used in different contexts and for different purposes.

---

## Version

> Maintain a history of changes to data for auditing and tracking purposes.

Source: https://dagster.io/glossary/data-versioning

### Data versioning definition:

Versioning of data is a process of tracking and managing changes made to data over time. It is important in modern data pipelines because it enables data engineers to keep track of changes made to the data and revert to a previous version if necessary.

### Data versioning example using Python:

Here are some practical examples of data versioning in data engineering using dvc. Please note that you need to [have the dvc library installed in your Python environment](https://dagster.io/glossary) to run this code:

One way to implement versioning of data in Python is by using Git, a version control system. Data can be stored in a Git repository, with each commit representing a new version of the data. Git provides tools for comparing different versions of the data and merging changes made to different versions.

Another way to implement versioning of data is by using a data version control system (DVCS) like DVC (Data Version Control). DVC allows data scientists and engineers to track and version datasets, models, and experiments. DVC stores data in remote repositories and provides features like tracking data lineage, enabling reproducibility, and sharing data across teams.

Here is an example of how to use DVC for versioning data in Python:

Install DVC using pip:

```javascript trailing-newline
$ pip install dvc
```

Initialize a DVC repository- for this example we wil not be using an SCM tool (e.g. Git).:

```javascript trailing-newline
$ dvc init --no-scm
```

Add data to the DVC repository:

```javascript trailing-newline
$ dvc add data.csv
```

Commit the data to the DVC repository:

```javascript
$ git add data.csv.dvc
$ git commit -m "Add data.csv"

[main (root-commit) 750be23] Add data.csv
 1 file changed, 4 insertions(+)
 create mode 100644 data.csv.dvc
```

Make changes to the data and commit them to the DVC repository:

```plaintext
dvc add data.csv
git add data.csv.dvc
git commit -m "Update data.csv"
```

Realpython provides a complete tutorial on DVC, that [you can find here](https://realpython.com/python-data-version-control/).

---

## Vertex

> In graph theory, a vertex is a point where two or more curves, lines, or edges meet, representing entities in graph-based storage and analysis systems.

Source: https://dagster.io/glossary/vertex

---

## Vulnerability Assessment

> The process of identifying, quantifying, and prioritizing the vulnerabilities in a system, involving the evaluation of system or software weaknesses and potential threats.

Source: https://dagster.io/glossary/vulnerability-assessment

---

## Web Crawling

> The automated process of browsing the web to collect information about websites and their pages, often used by search engines to index web content.

Source: https://dagster.io/glossary/web-crawling

---

## Web Scraping

> An automated method used to extract large amounts of data from websites quickly, used in data mining where you extract useful information or knowledge from data.

Source: https://dagster.io/glossary/web-scraping

---

## Wrangle

> Convert unstructured data into a structured format.

Source: https://dagster.io/glossary/data-wrangling

### Data wrangling definition:

**Data wrangling**, also known as data munging or [data preprocessing](https://dagster.io/glossary/data-preprocessing), in the context of modern data engineering, refers to the process of manually converting or mapping data from one raw form into another format that allows for more convenient consumption of the data with the help of semi-automated tools. This involves cleaning, normalizing, and transforming data to eliminate any corruption or inaccuracies, ensure consistency, and make it suitable for analysis or use in machine learning models. The ultimate goal of data munging is to improve the quality and reliability of data insights and decisions derived from the data.

Data wrangling is the process of cleaning, structuring, and enriching raw data into a desired format for better decision-making in less time. This process can involve a wide range of operations, including parsing text data, dealing with missing or inconsistent data, converting data types, or even more complex tasks such as integrating multiple data sources.

Additionally, the transformation of unstructured data (like text, images, videos) into a structured format (like a database or spreadsheet) is often a key part of an **ETL (Extract, Transform, Load)** process in data engineering.

In the context of specific types of unstructured data like text, this process may also be referred to as **text mining** or **text analytics**, which involves extracting valuable information from unstructured text data and transforming it into a structured format.

### An example of data wrangling using Python and Pandas

In this example, we'll demonstrate how to clean and transform a CSV file using the pandas library.

Given the following input file:

```plaintext
date,category,value
2023-01-01,A,10
2023-01-02,B,15
2023-01-03,A,8
2023-01-04,C,12
2023-01-05,B,20
2023-01-06,C,16
2023-01-07,A,11
2023-01-08,B,18
2023-01-09,C,14
2023-01-10,A,9
2023-01-11,B,16
2023-01-12,C,13
```

We can use the following code to clean and transform our data:

```python
import pandas as pd

# Load the CSV file into a DataFrame
data = pd.read_csv('data.csv')

# Display the initial state of the data
print('Initial data:')
print(data.head())

# Drop any rows with missing values
data = data.dropna()

# Convert a column to datetime format
data['date'] = pd.to_datetime(data['date'])

# Filter the data for a specific date range
start_date = pd.to_datetime('2023-01-01')
end_date = pd.to_datetime('2023-05-01')
filtered_data = data[(data['date'] >= start_date) & (data['date'] <= end_date)]

# Group the data by a categorical column and calculate the average of a numeric column
grouped_data = filtered_data.groupby('category')['value'].mean().reset_index()

# Sort the grouped data by the average value in descending order
sorted_data = grouped_data.sort_values(by='value', ascending=False)

# Reset the index of the sorted data
sorted_data.reset_index(drop=True, inplace=True)

# Display the final state of the data
print('Final data:')
print(sorted_data.head())
```

In this example, we assume that you have a CSV file named `data.csv` containing columns such as 'date', 'category', and 'value'. Here's a breakdown of the steps involved:

1. We start by importing the `pandas` library, which provides powerful data manipulation tools.
2. The CSV file is loaded into a DataFrame using the `read_csv` function from `pandas`.
3. We print the initial state of the data using the `head` function to display the first few rows.
4. The `dropna` method is used to remove any rows with missing values from the DataFrame.
5. The 'date' column is converted to datetime format using the `to_datetime` function.
6. We filter the data for a specific date range using boolean indexing.
7. The data is grouped by the 'category' column, and the mean value of the 'value' column is calculated using the `groupby` and `mean` functions.
8. The grouped data is sorted in descending order based on the average value using the `sort_values` method.
9. We reset the index of the sorted data using the `reset_index` function.
10. Finally, we print the final state of the data using the `head` function to display the sorted and transformed DataFrame.

This example demonstrates some common data wrangling operations such as dropping missing values, converting data types, filtering data based on conditions, grouping data, and sorting. These operations are commonly performed in modern data engineering pipelines to clean and prepare data for further analysis or processing.

Here is the output this example will produce:

```plaintext
Initial data:
         date category  value
0  2023-01-01        A     10
1  2023-01-02        B     15
2  2023-01-03        A      8
3  2023-01-04        C     12
4  2023-01-05        B     20

Final data:
  category  value
0        B  17.25
1        C  13.75
2        A   9.50
```

---

## XOR (Exclusive Or)

> A logical operator that outputs true only when inputs differ (one is true, the other is false).

Source: https://dagster.io/glossary/xor-exclusive-or

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## Zettabyte

> A unit of digital information storage used to denote the size of data. It is equivalent to one sextillion (10^21) bytes or 1000 exabytes.

Source: https://dagster.io/glossary/zettabyte