Data Quality at scale with Microsoft Purview Unified Catalog and AI

2026-04-25 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

The Microsoft Purview Unified Catalog, integrated with Microsoft Fabric, addresses data governance and quality challenges, particularly the operationalization of data quality rules at scale. While Purview offers capabilities like completeness, uniqueness, validity, and consistency checks, manually configuring these rules for numerous tables and columns is impractical. A community-developed browser extension automates the bulk creation of these rules, leveraging AI to generate SQL expressions based on column metadata. This solution significantly reduces the time required to establish a comprehensive data quality framework, moving from hours of manual configuration to minutes of review. It is especially critical in the age of AI, where inconsistent data can lead to subtle yet impactful errors in models and agents, necessitating robust data observability.

Key takeaway

For Data Engineers or MLOps Engineers struggling with manual data quality rule creation in Microsoft Purview, exploring the community-developed browser extension with AI-powered SQL generation can drastically improve efficiency. This approach enables broader data quality coverage faster, which is becoming a business requirement for reliable generative AI pipelines. You should prioritize critical columns and review AI-generated rules to ensure accuracy before deployment, and monitor quality scores continuously.

Key insights

AI-powered bulk rule generation in Purview automates data quality at scale, crucial for reliable AI systems.

Principles

• Data observability is critical for AI systems.
• AI-generated rules require human review.
• Prioritize critical columns for rule creation.

Method

Install a browser extension, select data assets in Purview, let AI generate SQL quality rules from metadata, review and adjust, then apply in bulk and configure evaluation frequency.

In practice

• Use Purview API to enrich column descriptions for better AI context.
• Export quality scores to Fabric for Power BI observability reports.
• Start with a specific data domain to test the bulk AI approach.

Topics

• Microsoft Purview Unified Catalog
• Data Quality Management
• AI-powered SQL Generation
• Microsoft Fabric Integration
• Data Observability

Best for: Data Engineer, MLOps Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.