Data quality is not something you check after the pipeline runs. It is something you design for.

This category explores validation, assertions, reconciliation, freshness checks, schema changes, anomaly detection, data contracts, testing, monitoring, and incident investigation.

The focus is on catching issues early, understanding failures quickly, and building data systems people can actually trust.