A framework for enforcing data quality across all stages of a data pipeline — ingestion, transformation, and consumption. Covers the six dimensions of data quality (timeliness, completeness, accuracy, validity, uniqueness, consistency), practical implementation using Dagster asset checks, Great Expectations, Soda, and dbt tests, plus best practices like balancing strictness with practicality and making quality metrics visible. Includes working code examples for asset checks, freshness policies, and dbt model tests.
