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, 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. 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, 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 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, 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.
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 costs.
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 typically schedule and monitor dbt runs with orchestration engines 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, 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 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, 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.
