What is dbt?

Dbt (Data Build Tool) is an open-source command-line tool that helps data teams transform raw data into an analytics-ready form by writing SQL statements. It integrates with modern data warehouses like Snowflake, BigQuery, and Redshift, enabling version control, testing, and documentation of SQL-based data transformations.

In addition to the open-source version, dbt Cloud provides a fully managed service that includes a web-based IDE, job scheduling, CI/CD integration, and team collaboration features. Dbt Cloud is particularly useful for teams that need a streamlined, managed environment for the development and deployment of dbt models.

Key Features

  • Modular SQL Development: Write modular SQL that is easy to manage and maintain.
  • Data Lineage and Dependency Management: Automatically build and visualize the data lineage to understand dependencies.
  • Testing and Validation: Create custom tests to validate data quality and consistency.
  • Documentation: Generate and maintain up-to-date documentation for data models.

Practical Use Cases for Data Engineers

Transforming Raw Data into Analytics-Ready Data

Dbt enables you to transform raw data stored in your data warehouse into structured, analytics-ready datasets using SQL. These transformations, defined as models in dbt, allow you to create clean and well-documented datasets from raw data sources.

Example: A Simple Transformation Model in dbt

In the example above, customers_transformed.sql is a dbt model that transforms raw customer data into a more usable form for analytics.

Once you've defined your models, you can use the dbt run command to execute them. This command will transform the raw data in your warehouse according to the logic defined in your dbt models, creating new or updated tables in your analytics environment.

The dbt run command processes all models in your project or the ones you specify, applying the SQL transformations to produce analytics-ready datasets.

Testing Data Quality

Dbt enables you to add data quality checks by defining tests on your data models. This can include checks for uniqueness, non-null constraints, and specific data ranges.

Example: Defining a Data Test in dbt

Automating and Managing Data Workflows with dbt

While dbt is focused on data transformation, its power comes from how it integrates data workflow automation, model lineage, and testing into a cohesive framework. Here’s how dbt automates core elements of your data engineering processes:

  • Model Lineage: Dbt tracks dependencies between models, ensuring that when you update one model, dependent models automatically re-run, keeping your transformations up to date.
  • Version Control: Every change to a dbt project is tracked, allowing you to roll back changes or view the complete history of transformations and data model updates.

Automatically Generated Documentation

Dbt also shines in its ability to automatically generate documentation that describes the models, sources, and relationships within your data pipeline. The documentation can be viewed in a web-based UI, providing a visual map of your entire data transformation process.

Key Features of dbt’s Documentation:

  • Model Descriptions: Each model can include a description that details its purpose, source data, and output structure.
  • Dependency Graphs: dbt creates visual dependency graphs that show how different models are interconnected.
  • Column Descriptions: You can add descriptions to columns in your models, making it easier for downstream users to understand the data without needing to read through SQL code.

Example: Auto-Generated Documentation

By running these commands, dbt generates up-to-date documentation for all models in your project and serves it locally, allowing you to explore your data pipeline visually. This reduces the time spent on manual documentation efforts and ensures consistency across your team.

Leveraging Incremental Models for Large Datasets

For large datasets, dbt supports incremental models, which process only the new or updated records rather than the entire dataset. This drastically reduces computation time and optimizes resource usage.

Example: Defining an Incremental Model

Built-In CI/CD for Data Pipelines

For teams using dbt Cloud or integrating dbt into their CI/CD pipeline, dbt enables automated testing, documentation generation, and deployment to production. This ensures that every change is tested and validated before it reaches production.

  • Automated Testing: Each change triggers tests to verify data integrity, preventing erroneous data from entering production.
  • Continuous Deployment: Once changes are approved, dbt can automatically deploy them to the production environment, ensuring up-to-date data pipelines without manual intervention.

Testing in dbt

Testing is a core feature in dbt that helps ensure data quality by validating the data within your models. dbt supports several built-in tests (e.g., uniqueness, non-null, and referential integrity), as well as the ability to create custom tests.

Built-In Tests

Dbt provides simple ways to test for common data integrity issues. These tests are defined in the YAML configuration file associated with your models.

  1. Unique Test: Ensures that a column contains unique values.
  1. Not Null Test: Ensures that a column does not contain any null values.
  1. Accepted Values Test: Ensures that a column contains only specific predefined values.
  1. Relationships Test: Ensures referential integrity between two tables.

Singular (Custom) Tests

Dbt supports singular tests, which allow for more specific or complex data validations using raw SQL logic. These tests are typically written as standalone SQL queries and can be reused across multiple models.

Example: Singular Test for Valid Date Ranges

Create a SQL file tests/test_due_date_after_order_date.sql with the following content:

You can then run this test using:

This example demonstrates a singular test, which directly queries the data to validate that all due_date values are after order_date.

Advanced Testing with dbt-expectations

For more advanced testing, the dbt-expectations package provides additional tests inspired by the Great Expectations framework. It allows for more detailed validations and assertions in your dbt project.

Example: Testing for Data Distribution

Running Tests

To run all tests for your project, use:

The results of the tests are logged, and any failed tests will be highlighted. You can investigate failed tests to debug and resolve data quality issues.

Best Practices for Testing

  • Run Tests Frequently: Add tests to your CI/CD pipeline to ensure data quality with every change.
  • Use Relationship Tests to enforce referential integrity between models.
  • Leverage Custom Tests for more granular control over data validation.
  • Document Tests: Ensure every model and column has appropriate tests to maintain high data quality standards.

Materializations in dbt

Materializations in dbt define how the results of models are stored in the database. By default, dbt models are materialized as views, but dbt offers several materialization strategies that can be configured at the project, folder, or model level. These strategies allow teams to balance performance, data refresh frequency, and storage considerations.

Types of Materializations

  1. View (default)
  • Behavior: Models are materialized as SQL views in the database. Views do not store data physically but represent the result of a query that runs each time they are accessed.
  • Use Case: Best for models that need real-time data and are used for lightweight transformations.
  • Example:
  1. Table
  • Behavior: Models are materialized as physical tables in the database. Data is stored permanently, and the model is refreshed each time dbt run is executed.
  • Use Case: Suitable for complex transformations where query performance is important. Tables are faster to query than views, as they don’t re-run the query each time.
  • Example:
  1. Ephemeral
  • Behavior: Models are not materialized as tables or views, but instead, their logic is injected into downstream models using Common Table Expressions (CTEs).
  • Use Case: Great for intermediate transformations that don’t need to be stored but are used within larger queries.
  • Example:
  1. Incremental
  • Behavior: Incremental models only process new or updated records, instead of reprocessing the entire dataset every time.
  • Use Case: Ideal for large datasets where full reprocessing is inefficient. Useful for tables where the majority of data remains unchanged between runs.
  • Example:

Best Practices for Materializations

  • Use Views for models where data freshness is critical, and query complexity is minimal.
  • Use Tables for complex transformations that are frequently queried or have large volumes of data.
  • Use Ephemeral for temporary, reusable transformations to avoid unnecessary table creation.
  • Use Incremental to handle large datasets efficiently and minimize load on the database.

Integrating dbt with AirflowDbt excels at data transformation within modern ELT pipelines, while Apache Airflow is a powerful platform for orchestrating workflows. By integrating dbt with Airflow, you can automate and schedule your dbt runs as part of a broader data pipeline, ensuring that data transformations happen reliably and on time.Here’s how to combine the strengths of both tools to manage and monitor data workflows effectively:Benefits of Orchestrating dbt with Airflow

  • Centralized Orchestration: Airflow acts as a central hub to manage dbt transformations and other pipeline tasks, providing a unified platform for data workflows. So you can combine dbt with things that occur before and after dbt, such as data ingestion and reporting.
  • Error Handling: Airflow offers robust error handling and notifications, enabling you to monitor dbt runs and be alerted to failures.
  • Scheduling and Automation: Use Airflow’s flexible scheduling to trigger dbt models based on time intervals, external events, or dependencies within the pipeline.
  • Scaling and Parallelism: Run multiple dbt models in parallel using Airflow, which optimizes performance and speeds up data processing.

Example: Running dbt in an Airflow DAGA typical Airflow workflow for running dbt models might look like this:

In this example:

  • The BashOperator runs the dbt run command, which triggers the transformation defined in the dbt project.
  • The DAG is scheduled to run daily, ensuring regular data updates.

Handling Dependencies and TriggersYou can define dependencies between dbt models or even between dbt tasks and non-dbt tasks using Airflow’s task dependencies:

Here, once the run_dbt_model task completes, the notify_team task can trigger an alert to inform stakeholders of the run status.

Parallelizing dbt Runs

For large dbt projects, parallel execution can significantly reduce processing time. Airflow allows you to define parallel tasks so that multiple dbt models run concurrently.

However, dbt itself supports parallelization using the threads configuration, which allows dbt to process multiple models simultaneously without requiring external orchestration tools like Airflow.

Using Airflow for Parallel Execution

In Airflow, you can parallelize the execution of dbt models by defining independent tasks that run simultaneously:

Monitoring and Logging

Airflow’s UI provides real-time logs and status for all your dbt runs, giving you visibility into task progress, success, or failure. You can also configure Airflow to send notifications or alerts if a dbt task fails, allowing quick troubleshooting and resolution.

Advanced Features

  • Error Handling: Use Airflow’s built-in failure callbacks to automatically trigger actions (like sending notifications or retrying tasks) if a dbt run fails.
  • Custom Operators: If you want more control over how dbt commands are executed, you can create custom Airflow operators to invoke dbt programmatically rather than through Bash commands.

Integrating dbt with Airflow: Cosmos Integration

In addition to the traditional methods of integrating dbt with Airflow, the Cosmos package provides a more advanced solution. Cosmos offers deeper integration by automatically parsing dbt dependencies and creating Airflow DAGs based on your dbt project structure. This simplifies managing large, complex dbt projects within Airflow.

More details can be found here: https://astronomer.github.io/astronomer-cosmos/getting_started/open-source.html.

Best Practices

  • Group Models Logically: Organize dbt models into meaningful DAGs based on team ownership or data dependency.
  • Use Incremental Models: In dbt, incremental models allow you to process only the new or updated data, reducing the computational load.
  • Testing with Airflow: Integrate dbt tests within Airflow to ensure data quality. By creating a separate DAG for dbt tests, you can automate testing after every transformation.

Dbt in Data Engineering: Key Values

Here’s our introduction to dbt (Data Build Tool) and its use in data engineering workflows. It covers dbt’s core functionalities, including data transformation, testing, and automated documentation generation. dbt is designed to work with modern data warehouses and can transform raw data into analytics-ready datasets using SQL, while ensuring data quality through built-in and custom tests.

Key sections include practical examples of transforming raw data, setting up incremental models for large datasets, and using dbt’s CI/CD capabilities for continuous deployment and testing. The article also discusses the integration of dbt with Apache Airflow to orchestrate and automate data pipelines, highlighting the benefits of combining both tools to streamline and scale workflows. Additionally, advanced features like the Cosmos package for deeper integration with Airflow are briefly introduced, offering enhanced orchestration capabilities for larger dbt projects.