Forum Discussion
Data Quality
Hi binitafulpagare ,
Below are the few points that may cover your questions.
- Organizations typically use a centralized data quality framework with reusable validation rules instead of creating separate rules for each pipeline.
- Common checks like schema validation, null checks, data type validation, duplicate checks, and reconciliation are standardized and reused across projects.
- A metadata-driven approach is commonly used, where rules are stored in a central repository/configuration table and applied dynamically.
- Business-specific validations are managed by individual data domains, while common standards are governed centrally.
- Data quality checks are integrated into pipeline monitoring, automated testing, and CI/CD processes to reduce manual maintenance.
- A hybrid governance model helps balance consistency and flexibility: central teams provide standards/frameworks, and domain teams manage specific business rules.
Thanks,
Chaithanya.
Hi v-kathullac,
Thank you for sharing these comprehensive data quality best practices.
I appreciate the emphasis on data validation, automated quality checks, schema management, exception handling, and data governance. These practices are essential for building reliable and scalable data pipelines in Microsoft Fabric.
I also found the recommendation to route invalid records to a quarantine area instead of stopping the entire pipeline particularly valuable, as it helps maintain pipeline continuity while allowing data quality issues to be investigated separately.
Thank you again for providing these practical recommendations. They serve as an excellent reference for anyone looking to implement robust and production-ready data integration solutions in Microsoft Fabric.