Dedicated Access mode on Azure Databricks clusters is an upgraded feature that extends the capabilities of single-user access mode. This mode allows a compute resource to be assigned either to a single user or to a group. It offers secure sharing among group members by scoping users' permissions down to the assigned group, enabling access for group-specific workloads. Below is a comparison of Dedicated Access mode with Standard access mode and single-user Dedicated Access mode:
Comparison with Standard (formerly Shared) Access Mode - Use Cases: Standard access mode is recommended for most workloads and provides cost-effective compute options. However, it is not suitable for certain specialized scenarios such as running Databricks Runtime for ML, Spark Machine Learning Library (MLlib), RDD APIs, or R-based workloads, which are supported under Dedicated Access mode when assigned to groups. - Operational Efficiency: Standard mode isolates workloads among users but does not support language and framework-level features such as R. Dedicated group clusters provide the operational efficiency of Standard mode while securely supporting specialized workloads. - Security Model: Standard clusters enforce data isolation among multiple users, unlike Dedicated Access mode, which scopes down permissions strictly to the group.
Comparison with Single-user Dedicated Access Mode - Use Cases: Single-user compute is a simplified and secure setup used for operational workloads, such as running credentials passthrough with Databricks File System (DBFS) mounts. When expanded to group assignments in Dedicated Access, this feature shifts from serving isolated scenarios to enabling shared collaborative environments. - External Resource Access: - Single-user clusters allow unrestricted external resource interactions (e.g., JDBC connections), almost mimicking a No Isolation setup within Databricks. - Group clusters strictly enforce access controls tied to group permissions. - Permission Management: When assigned to a group, permissions are scoped to the group's entitlements, meaning objects created or accessed are restricted to that group's context. This is significant for managing workspace artifacts such as notebooks, tables, and experiments securely. - Limitations: Group clusters may have limitations, such as requiring specific permissions for accessing external resources like secret scopes and Unity Catalog credentials; they also cannot interact with certain configurations like ephemeral job clusters.
Summary of Group-specific Features in Dedicated Access Mode - Supports unique workloads like ML, RDD-based, and R-based computations that Standard mode does not. - Enables secure collaboration by scoping permissions to group identities. - Requires setup adjustments, including group-specific folder permissions and workload fine-tuning (e.g., MLFlow tracking folder setup).
Overall, Dedicated Access for groups effectively bridges the accessibility gap for shared clusters running specialized workloads while maintaining stringent security aligned with group-level permissions. This mode balances operational ease from Standard configurations with the flexibility of custom, dedicated compute environments.
Cheers, Lou.
