Databricks Enhances Dashboards and Genie Code with AI-Powered Features
Databricks has released several updates to its dashboarding capabilities and Genie Code AI assistant. New features include automatically creating dashboard relationships from imported BI files, enhanced Genie Code customization for date pickers, and full undo/redo support within Genie Code. Additionally, the platform now performs semantic validation for AI-authored metrics, offers low-code filters on measures, and improves the UI for imported metric views, along with bug fixes.
- →Dashboard relationships from imported BI files via Genie Code
- →Customize date picker quick-select options with Genie Code
- →Full undo and redo support in Genie Code
- →Semantic validation for AI-authored metrics
- →Low-code filters on measures in local metric views
Features (5) ›
- Dashboard relationships from imported BI files via Genie Code
When importing Power BI or Tableau files with Genie Code, the migration tooling now automatically creates dashboard relationships where detected in the source file.
- Customize date picker quick-select options with Genie Code
Genie Code for dashboard authoring now allows users to customize quick-select options in date range picker filters, such as retaining only day-based ranges.
- Full undo and redo support in Genie Code
Genie Code for dashboard authoring now supports full undo and redo functionality, with each Genie Code response treated as a single undoable step, including restoring emptied dashboards.
- Semantic validation for AI-authored metrics
Genie Code for dashboard authoring automatically performs a semantic validation check after creating a widget, verifying business definitions, grain, attribution, and other metric properties to ensure accuracy.
- Low-code filters on measures in local metric views
Local metric views now offer a low-code option for adding filters directly to measures, simplifying the process of refining data displays.
Enhancements (3) ›
- Improved low-code UI for imported Unity Catalog metric views
Metric views imported from Unity Catalog now benefit from an enhanced low-code user interface, making extensions and modifications more intuitive.
- Metric view display names on dashboard widgets
Dashboard widgets now show the user-defined display name for each field within a local metric view, instead of the literal field name, for better readability.
- Cross-filtering includes facet fields
Cross-filtering functionality on faceted charts now incorporates the facet field as part of the filter, providing more comprehensive filtering results.
https://docs.databricks.com/aws/en/ai-bi/release-notes/2026#dashboard-enhancements-and-bug-fixes-3
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