pytest-testing

To write Pytest tests for Airflow DAGs and BigQuery queries, you use structured unit and integration test patterns with reusable fixtures to validate DAG correctness and query logic in CI pipelines.

Property-based testing with Hypothesis for data pipelines automatically generates diverse test cases to explore edge cases and validate invariants, ensuring your data processing logic handles unexpected inputs robustly.

You enforce data contracts in data pipelines using Pytest by validating data outputs against Pydantic models and JSON schemas, ensuring data quality and structural consistency across pipeline runs.

Yes, you need Python tooling to run Pytest tests for data pipelines. You must install dependencies like pytest, hypothesis, and pydantic, with integration tests requiring valid credentials to execute successfully.

Yes, you can run snapshot and performance tests for data outputs with Pytest by installing pytest-benchmark, allowing you to capture output states and measure execution speed to prevent regressions.