What distinguishes dbt from other ETL tools?

The emphasis on the transformation layer is what truly distinguishes dbt from other ETL tools. While many ETL tools cover the entire process—extracting data from various sources, transforming it, and loading it into a target system—dbt specifically focuses on the transformation aspect of the data pipeline. This means that dbt allows data analysts and engineers to build, test, and document the transformation of data as it is prepared for analysis.

By leveraging SQL, dbt enables users to define their transformations within a version-controlled framework, making it easier to manage changes, collaborate, and maintain a clear lineage of transformations. This focus on transformation allows teams to create reusable models, improve data quality, and ensure consistency across analyses.

Other tools might emphasize extraction or loading features, but dbt's unique position in prioritizing transformation using SQL and its robust integration with modern data stack practices truly sets it apart in the world of data analytics and engineering.