Monitor warehouse data quality beyond pipeline health

Successful data pipeline runs do not guarantee data quality, as "silent" errors like schema drift or upstream changes can produce incorrect results without triggering traditional pipeline alerts. To prevent these issues, organizations must implement data observability by monitoring the actual content of tables through freshness, distribution, and schema checks. By combining anomaly detection with fixed thresholds, teams can identify subtle data inaccuracies that standard job-level monitors often miss.