AI/BI Dashboards
Recent items mentioning AI/BI Dashboards across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
AI/BI Dashboards are seeing a wave of practitioner troubleshooting and extension: users are asking how to strip out HTML formatting when exporting table values 4 and how to remove semantic model relationship graphs 1, while more advanced users are pushing customization further — building Pareto charts with Vega-Lite 3 and even developing plug-ins for the dashboard framework 5. A separate thread on exporting pivoted data to Excel 2 shows export fidelity remains a recurring pain point across formats.
Generated daily from the 5 most recent items mentioning AI/BI Dashboards. Click any [N] to jump to the source.
How to remove/delete a Relationship Graph (Semantic Model) from an AI/BI Dashboard?
AI/BI Dashboard pivot export: Excel
Custom Visualizations in AI/BI Dashboards: Build a Pareto Chart with Vega-Lite
How to export clean values from ai/bi dashboard table that uses html formatted columns
Building Plug-ins for AI/BI Dashboard
NewsGenie Code Skills: Maintaining Quality at Scale
Genie Code can automatically generate a Databricks AI/BI dashboard from a simple business prompt, performing data discovery and dashboard authoring. By adding a "skill" to Genie Code, users can enforce engineering standards like bronze, silver, and gold table creation, dimensional modeling, and automated refresh jobs, making the output production-ready and repeatable.
Geospatial Unbounded: Spatial SQL GA with AI/BI Maps, Delta Sharing, and Iceberg v3
Spatial SQL is now Generally Available on Databricks, bringing native geospatial data types, 90+ ST_* functions, and AI/BI Dashboards that render maps natively. This release also includes major performance improvements, open lakehouse support via Delta Sharing and Iceberg v3, and Apache Spark 4.2 compatibility for geo columns.
The SDK now supports more granular AI agent detection in User-Agent headers and passes unrecognized values as-is. Several API changes introduce new fields for dashboards, apps, ML materialized features, and synced table statuses, along with a `Revert` method for Lakeview dashboards.
This release introduces new methods for Lakeview and Postgres services, including `revert()` for Lakeview and `undeleteBranch()` for Postgres, along with new fields across various services like Jobs and IAM. Several breaking changes require `actionType` and `resourceId` for bundle operations, `cliVersion` for bundle versions, and alter the `tags` field for Marketplace listings and pagination for cluster events.
This release introduces the new bundle package and workspace-level service, alongside a revert method for Lakeview dashboards and an undelete branch method for Postgres services. Breaking changes include modified pagination for the cluster events API and a type change for the marketplace listings request tags field to a dedicated dataclass.
NewsApache Iceberg V3 on Databricks: From Ingestion to Analytics
The video demonstrates Apache Iceberg v3 on Databricks, showcasing how its new variant column type natively handles semi-structured data and how row-level concurrency enables simultaneous data ingestion and corrections. It also highlights cross-platform data accessibility from open-source Spark via the Iceberg REST catalog, ensuring no vendor lock-in.
The question your commercial data should already be able to answer
Databricks and Veeva now embed Genie AI agents and AI/BI dashboards directly into Veeva Vault CRM, enabling life sciences commercial teams to get real-time answers to their questions without leaving their workflow. This unified Databricks lakehouse with Unity Catalog delivers governed commercial data to every persona, from sales reps to MSLs, in the format and depth their role requires.
NewsStop Guessing Table Health — Let These Dashboards Tell You
Databricks offers two dashboards for monitoring table health and access: the Table Access Advisor and the Table Health Advisor. These dashboards provide insights into table ownership, read/write patterns, staleness, optimization status, and underlying file structures, helping users identify ghost tables and ensure best practices.
TutorialsFrom Excel to AI Agents: The Evolution of BI Explained
The video explains the evolution of Business Intelligence (BI) through four phases, from IT-centric to analyst-driven, then semantic layers, and finally to a future where AI agents are primary BI users. It demonstrates how Databricks' BI stack, including Dashboards, Genie (natural language interface), Metric Views (semantic layer), and Databricks One (serving layer), addresses these evolving needs by providing a unified, open, and AI-ready platform.
NewsNever Build a Dashboard by Hand Again
The Databricks assistant, now called Genie code, can automatically generate multi-page dashboards from a blank canvas using natural language prompts. Users define a metric view as the data source and then describe desired dashboard pages, visuals, and themes, with Genie code planning and executing the build.
TutorialsDatabricks AI Dev Kit Demo - Install, DataGen, SDP, Dashboard
The video demonstrates installing the Databricks AI Dev Kit on a Mac, then uses it to generate synthetic data, create serverless Spark declarative pipelines for a medallion architecture, and build a Databricks dashboard based on the generated data. It highlights how the AI Dev Kit leverages skills and an MCP server to automate these development tasks.
This release introduces new resources for managing Postgres databases, data classification catalog configurations, and knowledge assistant features. It also renames the `databricks_apps_space` resource to `databricks_app_space`.
TutorialsDatabricks End-To-End Project | Zero-To-Expert | Streaming, AI, Lakeflow, Unity Catalog, AI/BI
This video demonstrates building an end-to-end restaurant analytics platform on Databricks, covering streaming and batch data ingestion, AI-powered sentiment analysis, and dashboard creation. It teaches how to use Unity Catalog, Lake Flow Connect for CDC, Spark declarative pipelines for real-time data from Event Hub, and how to construct a medallion architecture with fact and dimension tables.
TutorialsUnity Catalog Metric Views - Why you should care about Databricks' new Semantic Models
Unity Catalog Metric Views are Databricks' new semantic models, allowing users to define business-friendly names, dimensions, and context-sensitive measures for data. These views centralize KPI definitions, enabling consistent use across dashboards, AI tools, and downstream BI platforms, and are created using YAML.
The assessment workflow now includes a `force-refresh` parameter to rerun assessments and obtain updated results, and a new experimental workflow converts Azure WASBS URLs to the more performant ABFSS format. Dashboard management is now more resilient, automatically recreating dashboards when permission issues occur or they're trashed, and service principals are now supported as members of account groups.
Tutorials46 AIBI Dashboards & Visualizations | Consumer Access in Databricks | Forecasting Reports
TutorialsHealthcare Interoperability: End-to-End Streaming FHIR Pipelines With Databricks & Redox
UCX v0.58.0 adds offline installation support for restricted-access environments and enables creation of account-level groups from nested workspace groups for improved governance. The release also enhances dashboard navigation with hyperlinks to table and cluster resources, and includes fixes for assessment exporters and improved error handling in the Workflow linter.
This release adds Databricks Runtime 16+ support, fixes cluster policies on UC-enabled workspaces, introduces HMS Federation Glue credential migration, and refactors pipeline migration from skip-pipeline-ids to include-pipeline-ids. The migration progress workflow now runs automatically daily at 5 AM UTC while improving dashboard migration tracking and Python error reporting.