Comparison Guide
Microsoft Purview vs. DQC: What are the differences?
Your evaluation guide to navigate data quality
Key differences between DQC Platform and Purview
Automated, flexible data quality rules with no-code, conversational rule creation for business users, and SQL/Python checks for technical users
Agentic data improvement workflows beyond rule-based remediation
Best-of-breed data quality focus, cross-systems and with cloud-agnostic architecture
1. Automated, flexible data quality rules with no-code, conversational rule creation for business users, and SQL/Python checks for technical users
The DQC Platform provides domain-specific data quality logic tailored to the semantics of different data domains such as product, material, and business partner data. Instead of applying generic checks, DQC uses domain knowledge to generate more relevant validations. DQC offers over 40 pre-built no-code rules accessible to business users and provides a conversational approach where users can ask questions like "Suggest completeness checks for this data" or "What consistency rules should I have between orders and inventory?" to get tailored, domain-aware rule recommendations. This results in fewer false positives, higher signal quality, and faster time-to-value compared to a one-size-fits-all approach. Moreover, users can use standard SQL or Python syntax (supported by DQC coding assistants) to check data. Purview provides only 7 built-in no-code rule templates, with all other rules requiring special Purview syntax or expression language and offers AI-assisted rule suggestions based primarily on generic statistical profiling rather than conversational, domain-specific guidance.
2. Agentic data improvement workflows beyond rule-based remediation
The DQC Platform uses agentic improvement workflows that allow business and technical users to define context-aware fixes together. The DQC AI improvement agent has different tools at its disposal to complete a data improvement task. High confidence improvements can be applied automatically, while uncertain cases are reviewed by humans, enabling scalable enrichment, validation, and correction use cases. Purview primarily focuses on rule-based data quality assessment and monitoring, where remediation happens outside of Purview in tools like Azure Data Factory or Synapse, requiring manual intervention and separate tooling for actual data correction.
3. Best-of-breed data quality focus, cross-systems and with cloud-agnostic architecture
The DQC Platform specializes on automated data quality checks and improvements and deliberately integrates best-of-breed tools for adjacent capabilities instead of replacing them. Quality signals, rules, and improvement results are exposed directly into existing catalogs (including Purview), BI tools, and workflows where users already work, while DQC remains fully focused on high-quality execution across the entire data quality lifecycle (find, improve, prevent). This best-of-breed specialization combined with cloud-agnostic architecture enables DQC to provide consistent data quality capabilities across data warehouses, databases, systems or cloud platforms without vendor lock-in. Purview follows a suite-based approach that bundles data quality with catalog, glossary, lineage, and broader governance capabilities, naturally prioritizing Microsoft data sources like Fabric and OneLake with feature coverage varying across different sources.