The legacy Data Quality Services workflow used a knowledge base to identify acceptable values, detect likely matches, and clean imported records. Modern workflows can preserve those goals without requiring DQS.
Concept and purpose
Domain rules identify permissible patterns, mappings, and reference values. The key challenge is deciding which transformations are safe to automate and which should be reviewed by a human.
Example or practical exercise
Suppose customer city values contain inconsistent capitalization and whitespace. A staging query can normalize case and trim spaces; a trusted CityReference table can identify approved names; uncertain spellings should be flagged rather than blindly replaced.
Good practices
Track original and corrected values, rule versions, approval identity, and validation outcomes. Test false-positive matches and retain a rollback or reprocessing path.
Version and compatibility notes
Data Quality Services is removed from SQL Server 2025. Historical DQS server/client installation tutorials apply to earlier supported releases, not new SQL Server 2025 deployments.
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