Data Governance Architecture Diagrams for Azure Synapse Analytics¶

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This section provides comprehensive diagrams illustrating data governance architectures and frameworks for Azure Synapse Analytics.

Integrated Data Governance Architecture¶

This diagram illustrates how data governance components integrate with Azure Synapse Analytics.

Data Governance Maturity Model¶

This diagram illustrates the maturity model for data governance in Azure Synapse Analytics implementations.

End-to-End Data Governance Architecture¶

This diagram illustrates an end-to-end data governance architecture for Azure Synapse Analytics.

Data Classification Framework¶

This diagram illustrates a comprehensive data classification framework for Azure Synapse Analytics.

Microsoft Purview Integration Architecture¶

This diagram illustrates how Microsoft Purview integrates with Azure Synapse Analytics for comprehensive data governance.

Data Quality Framework¶

This diagram illustrates a comprehensive data quality framework for Azure Synapse Analytics.

Data Governance Roles and Responsibilities¶

This diagram illustrates the roles and responsibilities within a data governance framework for Azure Synapse Analytics.

Best Practices for Data Governance¶

When implementing data governance for Azure Synapse Analytics, follow these best practices:

1. Establish Clear Ownership
2. Designate data owners for all data domains
3. Define clear roles and responsibilities

4. Create accountability for data quality and security

5. Implement Comprehensive Classification

6. Use Microsoft Purview for automated classification
7. Apply sensitivity labels consistently

8. Implement protection controls based on classification

9. Automate Governance Processes

10. Set up automated scanning and discovery
11. Implement automated policy enforcement

12. Configure automated lineage tracking

13. Monitor Compliance Continuously

14. Create dashboards for governance metrics
15. Set up alerts for policy violations

16. Perform regular compliance audits

17. Establish Data Quality Framework

18. Define quality dimensions and metrics
19. Implement quality validation in pipelines
20. Create remediation workflows for quality issues