AI/BI Dashboards
Recent items mentioning AI/BI Dashboards across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks is integrating AI/BI Dashboards directly into partner platforms, as seen with Veeva Vault CRM enabling real-time answers for life sciences teams 2. Community discussions highlight the need for programmatic data extraction from Lakeview AI/BI dashboard widgets via API 1 and issues with dashboard updates not reflecting successful job executions 3.
Generated daily from the 4 most recent items mentioning AI/BI Dashboards. Click any [N] to jump to the source.
How to programatically extract rendered or computed data from AIBI Lakeview dashboard widget via API
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.
AI/BI Dashboard refresh via DABs + Jobs executes successfully but dashboard does not update without
Claude code + AI Dev kit vs Genie code
Hi everyone, i am testing the limits for agentic engineering for databricks. Has anyone tried using claude code with AI dev kit and comparing it against Genie code? Specifically around these tasks 1) Data engineering 2) ML prototyping 3) AI/BI Dashboard building So far, claude code + AI dev kit seems to work really well. But from a conversation with claude it seems genie code out of the box is better already. Anyone have any experience?
AI/BI dashboard filter having no effect?
As seen in screenshot the Product filter is set to allow two values. But ALL values are still displayed. Why does this filter not work? Note: https://preview.redd.it/4rm0z9gxmyxg1.png?width=2092&format=png&auto=webp&s=02cc5ff480269c2e29fa7fd9d55214bb41df5121
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.
You can now force a rerun of an assessment to get new results, even if it was previously run. Dashboard management is more robust, automatically creating a new dashboard if a PermissionDenied exception occurs or if the existing one is trashed.
Tutorials46 AIBI Dashboards & Visualizations | Consumer Access in Databricks | Forecasting Reports
TutorialsHealthcare Interoperability: End-to-End Streaming FHIR Pipelines With Databricks & Redox
You can now create account-level groups from nested workspace-local groups and install UCX in offline environments. The UI for tables and compute summaries now includes clickable hyperlinks for easier navigation.
UCX now supports Databricks Runtime 16+ for Hive Metastore table conversions and introduces a new `query_statement_disposition` option for SQL backend exports to handle large workspaces. Pipeline and dashboard migration workflows have been enhanced with new filtering options and improved progress tracking, including daily scheduled migration progress updates.