dc-cube-definition

Drizzle Cube definitions require a mandatory securityContext in the cube SQL to isolate tenant data. This approach applies consistent security filters alongside defined measures, dimensions, and joins to support multi-tenant analytics across departments.

The best way to enforce row-level security filters in a Drizzle Cube semantic layer is by applying a mandatory securityContext within the cube SQL. This isolates data per tenant and ensures consistent access controls across all defined measures and dimensions.

Yes, you can define cross-cube joins in Drizzle Cube. The definition structure supports various join patterns including star and schema configurations, enabling cross-cube analytics while maintaining secure per-tenant SQL filters across related cubes.

You structure measures and dimensions with proper typing when defining a cube by using the defineCube function. This guides you to create rich definitions that include accurate typing and primary keys alongside your security context and joins.

Yes, Drizzle Cube supports multi-tenant security contexts for isolating departmental data. It enforces a mandatory security context in the cube SQL, creating consistent security filters to isolate data across different departments and domains.

You verify access controls after integrating a secure cube into the semantic layer by executing a sample query within a restricted security context. This tests whether the mandatory per-tenant SQL filters correctly isolate data across domains.