Permission Management in Databricks

Permission Management in Databricks is the backbone of secure and efficient data workflows. It defines which users, groups, and service principals can view, edit, or run notebooks, jobs, clusters, and data assets. Without a clear access control strategy, projects slow down, sensitive data leaks, and compliance risks grow.

Databricks uses Access Control Lists (ACLs) to give fine-grained permissions to workspace objects. You can set permissions at the workspace, cluster, job, and notebook levels. Common actions include granting CAN READ, CAN RUN, or CAN MANAGE. Groups make it easier to scale permission changes. Role-based assignments prevent unnecessary manual edits and ensure consistency.

For compute resources, cluster-level permissions decide who can attach notebooks, restart clusters, or edit configurations. Limit admin-level rights to trusted users. Combine cluster ACLs with job permissions so scheduled workflows run only under approved conditions.

Data access is controlled through Unity Catalog. This centralizes governance for tables, views, and files across all workspaces. Assign data permissions directly to users or groups, define catalog-level rules, and apply schema-based security boundaries. Use Table ACLs to restrict read and write actions, and audit logs to track every change.

Strong permission management requires ongoing maintenance. Review access lists quarterly. Remove unused accounts. Use service principals instead of personal accounts for automation. Enable logging to confirm that rules work as intended.

Databricks Access Control is not optional. It is the framework that protects the integrity of your data platform and keeps projects compliant while staying fast.

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