implementing-data-quality-checks

Add data quality checks to pipelines — freshness, volume/row-count anomalies, schema drift, null/uniqueness/referential integrity, and value distributions — using dbt tests, Great Expectations, or Soda, and deciding warn vs block. Use when adding data quality validation, catching bad data before it reaches consumers, setting up freshness/volume checks, or defining expectations.