update-dqs

Update data quality specifications by merging STM mapping changes into existing DQS documents while preserving unchanged validation rules. This process maintains full traceability to upstream requirements and regenerates Spark-Expectations rule files to align with the revised specification across bronze, silver, and gold medallion layers.

Maintain medallion layer traceability by ensuring every updated validation rule traces back to an upstream STM mapping or DRD requirement. This approach automatically bumps the DQS version, adds change log entries, and runs mandatory validation gate checks before handing off to engineering teams.

Add referential integrity rules to a DQS by applying approved STM updates that automatically generate corresponding validation checks. The process updates statistical baselines, bumps the DQS version, and adds change log entries without overwriting existing quality rules.

Adjust SLA thresholds and alert configurations in an existing data quality specification by merging approved DRD requirement changes. The revision updates freshness SLA thresholds and alert configurations while preserving all unchanged content and maintaining cross-layer coverage for bronze, silver, and gold layers.

Regenerate Spark-Expectations rules after DMS schema modifications by applying the schema changes to the DQS document. This action triggers automated rule file regeneration to align with the updated specification, followed by read-only database volume verification for baseline updates.

Unchanged validation rules are preserved when a data quality specification is updated. The revision process merges approved changes to STM rules, DMS schema, SLA requirements, and alert thresholds while keeping all existing quality rules intact across the bronze, silver, and gold medallion layers.