data-quality-checker

Data quality validation checks datasets against six core dimensions—accuracy, completeness, consistency, timeliness, validity, and uniqueness—to prevent corrupted data from reaching downstream dashboards and models. It enforces ingestion-time schema validation and freshness SLA monitoring.

Data contract enforcement validates formal producer-consumer agreements—covering schema, freshness SLA, volume bounds, and versioning—at ingestion time. This prevents silent schema breaks caused by upstream changes from reaching downstream surfaces.

Distribution drift tracking monitors statistical changes in data over time to detect when input data deviates from expected patterns. It is needed for machine learning workflows to prevent model degradation caused by shifting data distributions.

Yes, referential integrity checks identify orphan foreign key records and validate relationships across warehouse layers. This ensures consistency within your data warehouse and prevents broken joins in downstream analytics.

Production data observability provides scan methodology and tooling recommendations for monitoring volume anomalies and freshness SLA breaches across pipeline layers. It uses quarantine-based error handling to isolate corrupted data before it reaches customers.

Yes, shift-left validation applies schema checks at ingestion time to catch malformed or missing data early. This prevents invalid records from entering your warehouse and enforced data contracts stop silent schema breaks.