Plan, implement, and manage a solution for data analytics
Start with capacity and workspace design
Fabric analytics solutions begin with platform decisions that constrain everything later: capacity, workspace, and governance. A capacity (F SKU or P SKU) supplies compute; a workspace is a security and collaboration boundary that must be assigned to a capacity before most items can run. Know the workspace roles (Admin, Member, Contributor, Viewer) and how they interact with item-level permissions and OneLake data access. Deployment pipelines promote items across Dev/Test/Prod stages; they require capacity and support only certain item types, with rules for parameterization and autobinding. Git integration provides version control per workspace, but coverage is not universal and schema conflicts must be resolved. Governance concepts include domains, sensitivity labels, endorsement (promoted vs. certified), and the Capacity Metrics app for monitoring smoothing and throttling.
Traps: assuming a workspace includes compute; confusing a deployment pipeline with a data pipeline; expecting Git to deploy every artifact; and forgetting that capacities can be paused, which stops background jobs and refreshes. Another common trap is treating workspace roles as equivalent to dataset or warehouse permissions.
Rule of thumb: one capacity, separate workspaces for Dev/Test/Prod, Git-connected workspaces for source control, and a deployment pipeline for controlled promotion. Do not use Git alone as a release mechanism.