Data Governance and Security Frameworks

Explore Azure Data Factory’s best practices and frameworks for ensuring governance and compliance of Azure data.

Data management is crucial in the digital age, and Azure Data Factory (ADF) stands as a key player in orchestrating data workflows. Here, we’ll explore the vital aspects of data governance, security, and compliance within the realm of ADF.

What is data governance?

Data governance is the foundation of effective data management. It involves defining policies, procedures, and standards to ensure data quality, security, and compliance. In the context of Azure Data Factory, data governance becomes the linchpin for seamless data integration and transformation.

Best practices for data governance in ADF

Here are some best practices for data governance in ADF:

  1. Define data policies: It is essential to define data policies that specify how data should be handled throughout its life cycle. Data policies should include data quality standards, data security and privacy policies, and data retention and archival policies.

  2. Create a data catalog: Creating a data catalog is essential for data governance in ADF. A data catalog is a centralized repository that stores metadata about the data sources, data pipelines, and datasets used in ADF. It provides a single source of truth for all data-related information and helps maintain data lineage.

  3. Ensure data quality: Data quality is critical for the success of any data integration project. In ADF, we can use data quality rules to ensure that data is accurate, complete, and consistent. Data quality rules can be defined at the source or target level and can be used to validate data during ingestion, transformation, and loading.

  4. Implement data security: Data security is a critical aspect of data governance in ADF. ADF provides various security features such as role-based access control, encryption, and secure transfer of data. We should implement these security features to ensure that data is secure at all times.

  5. Use version control: Version control is essential for managing changes to data pipelines and datasets. ADF integrates with popular version control systems such as GitHub and Azure DevOps, allowing us to manage changes to data pipelines and datasets easily.

How does Azure Purview ensure data governance?

Azure Purview is a unified data governance service that automates the discovery, classification, and management of data assets, ensuring effective governance across an organization’s data landscape. Let’s see how it works with ADF to ensure data governance:

  1. Data ingestion with ADF: ADF orchestrates ETL workflows, ingesting data from diverse sources into Azure environments.

  2. Metadata extraction: Purview automates metadata extraction, creating a comprehensive inventory of ingested datasets.

  3. Classification and labeling: Purview identifies sensitive data, applying proper classifications and labels based on policies.

  4. Establishing data policies: Purview enables policy definition and enforcement for data quality, access control, and compliance.

  5. Data lineage and mapping: Purview provides insights into data flow and relationships, enhancing transparency and traceability.

  6. Integration with Power BI and other downstream applications: Curated data from Purview seamlessly integrates with Power BI for reliable analytics and reporting.

The diagram below explains the functioning of Azure Purview with ADF:

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