How do you implement data quality checks in pipelines?

Data quality checks detect issues before they reach dashboards. Common checks: - Schema validation - Null/uniqueness constraints - Freshness and volume anomalies - Referential integrity Automate alerts and quarantines, and track incidents to reduce repeated failures. Great Expectations and dbt tests are common approaches.