adding-dbt-unit-test

dbt unit tests validate data transformations by mocking upstream model inputs and comparing them against expected outputs. You define these mocked inputs and expected results in a YAML configuration to verify model logic before production.

You mock upstream inputs by defining them within the given block of the unit_tests YAML. This structure simulates the data dependencies and validates the expected outputs against the mocked input rows.

Yes, you can write unit tests for ephemeral and versioned dbt models. The generated YAML supports specifying model versions and applying overrides to handle platform-specific considerations and dependencies.

The required fields in a dbt unit test YAML file are model, given, and expect. The structure must adhere to dbt's unit test spec, with optional fields for versions and overrides to handle model dependencies.

To use test-driven development with dbt, generate the unit test YAML defining expected outputs first, then build or refactor your dbt model transformations to make the mocked inputs pass the expected results.

Yes, dbt unit testing supports overrides. The generated YAML definitions can include overrides and versions to manage platform-specific considerations and ensure accurate data transformation validation across dependencies.