6 Benefits of a Modern Data Pipeline & How to Build One
January 6, 2025
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. 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, retail, and eCommerce, 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 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 importing data from various sources into the pipeline. This step can occur in real-time or in batches, depending on the pipeline's requirements. A well-designed ingestion process adapts to data size and speed, ensuring efficient intake without overwhelming the system.
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, 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.
Learn more in our detailed guide to data pipeline frameworks (coming soon)
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 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, 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.