3.6 dbt

Mage’s dbt integration brings software engineering best practices to your data transformation workflows. dbt blocks allow you to leverage dbt's powerful modeling capabilities while maintaining the orchestration and dependency management that makes Mage pipelines robust.

Why integrate dbt with Mage?

dbt excels at SQL-based transformations, testing, and documentation, while Mage provides orchestration, data loading, and multi-language support. Together, they create a comprehensive data engineering platform that spans the entire analytics lifecycle.

dbt strengths:

• SQL-based transformations with Jinja templating

• Built-in testing and data validation frameworks

• Comprehensive documentation generation

• Version control for analytics code

• Dependency management between models

Mage + dbt advantages:

• End-to-end pipeline orchestration

• Integration with non-SQL data sources

• Python and R transformation capabilities

• Real-time monitoring and alerting

• Unified deployment and scheduling

dbt block structure

When a pipeline runs, each dbt model block will run and then each test associated with that dbt model will be ran. This ensures your dbt models maintain data quality standards automatically as part of your pipeline execution.

Conclusion

dbt integration in Mage represents the best of both worlds, combining dbt's mature SQL modeling framework with Mage's comprehensive data engineering capabilities. This integration eliminates the common challenge of orchestrating dbt models within broader data workflows, while preserving the testing, documentation, and version control practices that make dbt valuable.