@databricks

databricks-lakebase covers autoscaling postgres project lifecycle, branching with copy-on-write, compute scaling from 0.5-32 cu, high availability, connectivity patterns, data api access, and synced table management for unity catalog integration.

databricks-pipelines covers lakeflow spark declarative pipeline development with decision trees for dataset selection, comprehensive api reference tables, and common trap enumeration. includes scaffolding guidance and medallion architecture conventions.

databricks-core covers cli authentication, profile management, resource enumeration, and data exploration via aitools. handles common pitfalls around positional arguments and shell session isolation.

databricks-apps covers building and deploying apps on the databricks platform using appkit or other frameworks, with detailed scaffolding workflows, data access routing, and lakebase integration for stateful applications.

databricks-jobs covers scaffolding and deploying lakeflow job projects via bundle init, with partial guidance on configuration and project layout. lacks procedural depth for job execution, monitoring, and failure recovery.