databricks-aibi-dashboards

To build Databricks AI/BI dashboards, you must define datasets and validate all SQL queries using execute_sql before deployment. This process enforces data accuracy and uses JSON-based layouts to configure widgets and ensure visual consistency.

The best way to update a Lakeview dashboard layout is by using JSON-based deployment configurations that enforce strict widget sizing, positioning, and cardinality rules. This structured approach maintains readability and dashboard integrity during updates.

Yes, mandatory query validation is required before deploying Databricks AI/BI dashboards. You must test all SQL queries using execute_sql to prevent errors and ensure data accuracy prior to defining datasets and publishing visualizations.

Yes, you can automate Databricks dashboard publishing and management through defined MCP tools. This automation orchestrates the entire workflow from SQL query validation and dataset definition to JSON-based layout deployment and publishing.

Dashboard deployment may fail if widget configurations violate data modeling, sizing, or cardinality best practices. Enforcing strict adherence to layout guidelines and validating SQL queries beforehand prevents these structural errors and ensures performance.

Yes, the databricks-aibi-dashboards skill operates without external dependencies. It relies on internal references to orchestrate SQL validation, dataset definition, and JSON layout deployment for Databricks AI/BI dashboards directly.