validate-dqs

To validate a DQS document, run an automated validation process that checks all required sections, field-level rules, and traceability links against quality standards. This generates a severity-ranked report identifying missing sections, invalid rules, and traceability gaps for immediate remediation.

A DQS audit for medallion data pipelines systematically reviews field-level validation rules, referential integrity checks, and reconciliation rules across bronze, silver, and gold layers. It ensures all metadata, alert frameworks, and traceability links meet engineering standards before pipeline implementation.

You validate referential integrity and statistical distribution rules in a DQS file by running an automated specification check against quality standards. The validation process scans for defined foreign key checks and statistical tests, flagging any missing or invalid rules as critical issues or warnings.

Yes, automated DQS validation can apply auto-fix capabilities to resolve CRITICAL severity issues immediately. It also generates user prompts to address WARNING-level gaps and provides INFO-level suggestions, tracking corrections to improve future specification quality.

Yes, DQS validation thoroughly checks alert and escalation frameworks within data engineering workflows. It validates the completeness of alert protocols in the specification to ensure proper monitoring and escalation paths are defined for downstream pipeline failures.

Automated DQS rule audits are limited to evaluating structural completeness, rule validity, and traceability gaps within the specification document. They validate the design of field-level rules and alert frameworks but do not execute these rules against live medallion layer data.