databricks-aibi-dashboards

You can validate SQL queries before dashboard deployment by running them through execute_sql to verify correctness. This skill enforces a strict workflow that requires query validation before constructing dashboard JSON and deploying via create_or_update_dashboard.

Building Databricks AI/BI dashboards with query validation requires a strict workflow: inspect schemas, write and test SQL via execute_sql, construct dashboard JSON, and deploy using create_or_update_dashboard. This ensures datasets, queries, and widgets are tested before production.

Yes, this skill supports deploying dashboards across multiple Databricks workspaces by integrating MCP tooling. It streamlines creation, testing, and publishing while ensuring governance and reproducible deployment across environments.

Databricks AI/BI dashboards break in production without query validation because untested SQL can cause brittle failures. This skill prevents this by enforcing a mandatory process that validates all SQL queries with execute_sql before production deployment.

To automate Databricks dashboard creation and deployment, you need to provide a validated dataset. The automation then produces and deploys the dashboard after SQL testing, handling schema inspection, query validation, JSON construction, and production deployment.

Yes, this dashboard deployment workflow meets strict requirements for dataset naming, query validation via execute_sql, dashboard JSON construction, and deployment via create_or_update_dashboard, ensuring properly configured widgets and datasets.