Data Governance with Databricks

Data governance in Databricks involves supervision to guarantee that data adds value and aligns with your business strategy.

Delta Sharing is an open protocol created by Databricks to facilitate secure data sharing with external organizations or different teams within your organization, irrespective of the computing platforms they utilize.

Data lineage provides end-to-end visibility into how data flows in the data lakehouse from the source to the desired destination.

Here are some best practices for data governance:

• Unify data management 

• Unify data security 

• Manage data quality

• Sharing data securely and in real time

The four pillars of data governance such as data quality, data stewardship, data protection and compliance, and data management. Ensuring those aspects can provide a solid understanding for establishing a robust data governance framework.

It is a single and unified permissions model employed across all data assets and clouds. It can be in the form of attribute-based access control (ABAC) for personally identifiable information (PII).

Data quality management in Databricks has built-in quality controls, testing, and monitoring to ensure accurate and reliable data used for BI, analytics, and machine learning workloads.

Unity Catalog offers centralized control over access, auditing, data lineage, and data discovery functions across Databricks workspaces. Notable characteristics of Unity Catalog encompass the ability to establish data access policies in one location, ensuring security measures are consistently applied throughout all workspaces.