Data Pipeline Testing — Strategies and Tools

Pipeline testing is key for reliability. Unit tests, integration tests and automated quality checks in CI/CD.

Why Test Pipelines¶

Untested pipelines lead to silent failures — bad data in reports.

Test Pyramid¶

• Unit tests — individual transformations
• Integration tests — entire pipeline
• Data quality tests — output validation
• Contract tests — compliance with contracts

def test_removes_test_orders():
    input_data = [
        {"id": 1, "status": "confirmed"},
        {"id": 2, "status": "test"},
    ]
    result = run_model("stg_orders", input_data)
    assert len(result) == 1
    assert all(r["status"] != "test" for r in result)

CI/CD¶

# .github/workflows/data-ci.yml
jobs:
  test:
    steps:
      - run: pip install dbt-duckdb
      - run: dbt test
      - run: soda scan -d test checks/

Summary¶

Pipeline testing prevents silent failures. Unit tests, quality checks and CI/CD are foundation of reliable data.