adding-dbt-unit-test

You can create dbt unit tests by generating YAML entries that supply mocked inputs for upstream refs and sources alongside the expected output rows. This approach validates SQL transformations like window functions and multi-join logic using inline dict, csv, or sql fixture formats.

dbt unit tests support inline dict, csv, and sql fixture formats for mocking inputs. You can also reference external fixture files stored in your configured test-paths directory to organize and reuse test data across multiple unit test definitions.

Yes, you can test incremental and ephemeral dbt models using unit test YAML definitions. The framework provides specific guidance for handling incremental dependencies, ephemeral models, versioned models, and adapter-specific caveats for BigQuery, Snowflake, Redshift, Postgres, and Spark.

dbt unit tests allow you to override macros, project vars, and environment variables directly within the YAML definitions. This environment control enables you to create precise test scenarios and validate bug fixes before materializing your models.

Use dbt unit tests when you need to validate complex transformation logic like regex, window functions, or multi-join operations by mocking inputs and asserting exact expected outputs. Standard schema tests check data quality constraints, whereas unit tests enable test-driven development for specific SQL model behaviors.

To practice TDD with dbt, write unit test YAML definitions that specify mocked upstream inputs and expected outputs before or during SQL model development. This test-driven approach reduces regression risk by validating transformation logic against edge cases as you code.