Cannot install libraries to cluster

[JoseU]

Getting the following error when trying to install libraries to all purpose compute using the Library tab in Cluster details. We had vendor setup the cluster and they have since dropped off. I have switched the owner to an active AD user however still getting this error

'Library installation failed after PENDING for 0 minutes since cluster entered RUNNING state. Error Code: USER_ID_NOT_FOUND_FAILURE. Library installation attempted on the driver node of cluster {cluster-id} and failed due to an invalid user. Please reinstall the library.'

[Kaniz_Fatma]

Hi @JoseU, 

1. Ensure that the new owner (active AD user) has the necessary permissions to install libraries on the cluster. This includes being part of the appropriate groups and having the right roles assigned.
2. Double-check the cluster configuration to ensure that the user ID is correctly updated.
3. Try reassigning the cluster ownership again to the active AD user. 
4. Restart the cluster after making the changes. This can help in applying the new permissions and configurations.
5. Ensure that the user ID used in the cluster configuration matches the one in your Active Directory. Any discrepancies can lead to such errors.

For managing data access and permissions in Databricks, here are some best practices:

• Use Unity Catalog: Unity Catalog provides fine-grained access control and auditing capabilities. It allows you to manage permissions at the table, column, and row levels.
• Role-Based Access Control (RBAC): Implement RBAC to assign permissions based on roles rather than individual users. This simplifies management and enhances security.
• Service Principals: Use service principals for automated processes and applications. This ensures that permissions are managed centrally and securely.
• Audit Logs: Regularly review audit logs to monitor access and changes to your data. This helps in identifying and addressing any unauthorized access.

Regarding the differences between shared serverless and dedicated serverless compute in Databricks:

• Shared Serverless: Resources are shared among multiple users and workloads. This can lead to cost savings but might introduce variability in performance due to resource contention.
• Dedicated Serverless: Resources are dedicated to a single user or workload. This ensures consistent performance but can be more expensive compared to shared serverless.

Would you like more detailed information on any of these points?