databricks-aibi-dashboards

To validate SQL queries before deploying a Databricks AI/BI dashboard, execute all queries using execute_sql to ensure they run correctly. This mandatory validation workflow prevents invalid widgets by verifying dataset columns and enforcing Spark SQL patterns prior to dashboard creation.

The best way to build reliable Databricks AI/BI dashboards across multiple datasets is to enforce strict query testing and widget field-name alignment. Test all SQL queries via execute_sql, then generate a ready-to-deploy dashboard JSON configuration from the verified datasets.

Dashboard widgets show invalid fields after deployment when widget fields are not aligned with dataset columns. The validation workflow prevents this by enforcing dataset architecture guidelines and mandatory SQL query testing with execute_sql before generating the dashboard configuration.

Yes, you can automate Databricks dashboard deployment by building dashboard JSON from verified datasets and deploying with create_or_update_dashboard. This process uses MCP tools to streamline creation, validation, and publication of the dashboard configuration.

To create and publish Databricks AI/BI dashboards, you need MCP tools like get_table_details, execute_sql, get_best_warehouse, create_or_update_dashboard, publish_dashboard, and get_dashboard to manage the end-to-end dashboard lifecycle from query testing to deployment.

Yes, testing every SQL query is required before creating a Databricks dashboard. The workflow mandates executing all SQL queries with execute_sql to enforce Spark SQL patterns and ensure widget field-name alignment, producing a ready-to-deploy dashboard configuration.