Validate data quality for ML pipelines using Great Expectations, Pandera, Pydantic, and Deequ. Covers schema validation, data profiling, data quality checks (completeness, uniqueness, consistency, accuracy, timeliness), data contracts, anomaly detection in datasets, automated data testing in CI/CD, handling schema evolution and breaking changes, data quality dashboards, alerting on validation failures, and integration with orchestrators like Airflow and Prefect. Use when building data quality gates, profiling datasets, enforcing data contracts, or debugging data issues.
