Why testing data pipelines is different
Business logic bugs in data pipelines often do not cause crashes — they silently produce wrong numbers that flow into dashboards and decisions. Testing must verify correctness of output data, not just process completion.
Unit tests
Test transformation functions in isolation with fixture data. dbt supports unit testing with dbt test and the unit_tests block. For Python transformations, use pytest with small DataFrames as fixtures.
Integration tests
Run the full pipeline against a staging environment with a production-like data sample. Verify row counts, distributions, and joins produce expected results.
Data contract tests
Use the datacontract CLI or Great Expectations to assert schema and quality rules at pipeline boundaries. Run in CI on every pull request.
Regression testing
For critical pipelines: maintain a golden dataset — a known-good output for a fixed input. Run the pipeline against the fixed input and diff the output. Any unexplained change fails the test.