Metric Views
Recent items mentioning Metric Views across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks continues to expand support for Metric Views across its ecosystem, with the dbt-databricks adapter now supporting them 1 and the new open-source Databricks JDBC driver offering support and faster retrieval 3. Users can now browse and refresh governed data from Metric Views directly within Google Sheets via a new add-in 2. Metric Views enable advanced data definitions, including nested joins and complex calculations with windowing, and can be precomputed with incremental updates 8.
Generated daily from the 8 most recent items mentioning Metric Views. Click any [N] to jump to the source.
This release adds support for metric views, row filters, Python UDFs, and key-only Databricks tags. A breaking change alters how `databricks_tags` merge across hierarchy levels.
TutorialsConnect Google Sheets to Databricks
The Databricks Google Sheets add-in allows users to explore, import, and refresh governed data from the Databricks Lakehouse directly within Google Sheets. It demonstrates how to browse Unity Catalog, select tables or metric views, apply filters, schedule data refreshes, and use direct SQL queries with parameters.
Faster Queries and New Capabilities with the Open-Source Databricks JDBC Driver
The new open-source Databricks JDBC driver delivers up to 30% faster large result retrieval and adds support for multi-statement transactions, stored procedures, Arrow compatibility, and Unity Catalog metric views. This fully owned, open-source driver enables faster fixes, external contributions, and tighter platform integration.
NewsEnhancing your Skills with Databricks Genie Code
Databricks Genie Code is an agentic coding system that allows users to build custom "skills" using markdown files, enabling it to generate code and perform tasks according to specific in-house standards and conventions. These skills provide context-on-demand, ensuring repeatable and consistent output for various engineering tasks like schema documentation or metric view creation.
What's new in AIBI Dashboards - April 2026
* **Publish with service principal credentials**: Authors can publish dashboards using the data credentials of a service principal. 📖 [Documentation](https://docs.databricks.com/aws/en/dashboards/share/share#publish-dashboard) * **Service principal ownership**: Workspace admins can transfer dashboard ownership to a service principal in the UI. 📖 [Documentation](https://docs.databricks.com/aws/en/ai-bi/admin/#transfer-ownership) * **Choropleth map admin levels**: Choropleth maps support US admin levels 3 (regions, multi-state groupings) and 4 (states). 📖 [Documentation](https://docs.databricks.com/aws/en/dashboards/manage/visualizations/maps) * **SQL editor line numbers**: The SQL query editor displays line numbers to help with legibility and debugging. * **PDF subscription page selection**: Dashboard authors can select which pages to include in PDF email subscriptions. 📖 [Documentation](https://docs.databricks.com/aws/en/dashboards/share/schedule-subscribe) * **Parameter values in widget titles and descriptions**: Dashboard authors can reference parameter values in widget titles and descriptions, so the text updates dynamically as viewers change parameter selections. 📖 [Documentation](https://docs.databricks.com/aws/en/dashboards/manage/filters/parameters) * **Table cross-filtering and drill-through**: Tables support cross-filtering and drill-through. * **Counter prefix and suffix**: Numbers in counters support custom prefixes and suffixes. 📖 [Documentation](https://docs.databricks.com/aws/en/dashboards/manage/visualizations/types#counter) * **Schema browser default dataset type:** Adding a table to a dashboard from the schema browser creates a [local metric view](https://docs.databricks.com/aws/en/dashboards/manage/data-modeling/local-metric-views) by default instead of a SQL dataset. * **Warehouse overload message**: Dashboards show a message explaining when rendering is delayed due to the warehouse being overloaded. * **Tabular attachments in email subscriptions**: Dashboard email subscriptions include tabular attachments. * **Fullscreen scroll position**: Exiting fullscreen mode on a published dashboard returns you to your previous scroll position instead of jumping to the top of the page. * **Local metric views**: A new dataset type lets you create metric views directly in a dashboard using a low-code visual interface, without publishing to Unity Catalog first. 📖 [Documentation](https://docs.databricks.com/aws/en/dashboards/manage/data-modeling/local-metric-views) * **Edit hex color values inline**: Authors can click directly on a hex color value to edit it in place. * **View SQL for visualization widgets**: Authors can view the SQL behind specific visualization widgets while in draft mode. * **Waterfall chart totals**: Waterfall charts with categorical X-axis support a total bar. * **Scatter plot shape field**: Scatter plots support a shape field to differentiate data points by category. * **Clear applied filters individually**: Dashboard viewers can individually clear applied filters from the active selection bar. * **Text box vertical alignment**: Text box widgets support vertical alignment (top, center, and bottom). * **Choropleth map boundaries**: Choropleth maps support additional boundary types, including ZIP code and NUTS regions. * **“Explain this change” chart types**: The “Explain this change” feature is available for pivot table cells, horizontal bar charts, pie charts, and heatmaps, in addition to time series charts. 📖 [Documentation](https://docs.databricks.com/aws/en/dashboards/genie-spaces#explain-chart-changes)
Discovery layer in Databricks that feels like an internal marketplace
**How do business users at your company find the right asset for a specific domain?** Databricks has a feature for this: Discover + Domains. It’s currently in Beta, and it gives you a curated, business-friendly way to organize and browse assets. https://preview.redd.it/pii6h72fnhzg1.png?width=3452&format=png&auto=webp&s=ee64e06ee878ceb10c4bc6bf7219d62c096531cd What’s useful about it: * Organize by business concept * Domains let you group assets around concepts like Finance, Marketing, or Customer Support instead of making users navigate catalog/schema/table names. * Curated discovery * Curators can create and highlight custom sections on both the main Discover page and on each domain page, so you can feature things like Key Metrics, Quarterly Reports, or Getting Started. * Governed tags keep the taxonomy clean * Domains are built on governed tags, so you can standardize domain labels and control who can assign them. That’s much better than ending up with random variants like `finance`, `Finance`, and `fin` floating around. * It sits above the catalog hierarchy * In practice, that means you can bring together catalog assets like tables and metric views, plus assets like dashboards, notebooks, and Genie Spaces under one business concept. https://preview.redd.it/yub3p9pbnhzg1.png?width=1142&format=png&auto=webp&s=a6a84e080e399ab1f9288b2c6a36b743e15a7e9b Who should care: * **Data producers:** publish important assets into business-facing domains. * **Business users:** a better entry point than memorizing technical paths or asking around. https://preview.redd.it/1s1iv769fhzg1.png?width=2184&format=png&auto=webp&s=718e4fb5563c5d63168e5d78837f54446772603d https://preview.redd.it/6xrc0odwfhzg1.png?width=2150&format=png&auto=webp&s=d24597e453206ca609b67a469f899073aec33962 A lot of data platform friction is coming from the fact that people can’t find the right thing fast enough. And that's why I like this feature, as it’s one of those platform capabilities that can dramatically improve user experience.
TutorialsStep-by-Step: Using the Databricks Excel Add-in to Analyze Governed Lakehouse Data
ABAC Policies Not Working on Metric Views
Databricks One is now renamed as Genie
TLDR: * **Account-level Genie is now GA** – a single Genie experience shared across all workspaces in an account * **Unified Genie Chat** – ask once and get answers powered by full context across your data estate, including Genie Spaces, tables, metric views, dashboards, documents, and more * **Expanded connectors and sources** – native integration with platforms like SharePoint, Confluence, Google Drive, Glean, and others * **Genie Mobile** – native iOS and Android app, currently available in private preview * **Product unification** – Databricks One has been renamed to **Genie** as the unified product brand The next generation of Databricks Genie is here - check this blog out for more details: [https://www.databricks.com/blog/next-generation-databricks-genie](https://www.databricks.com/blog/next-generation-databricks-genie)
NewsMaking AI understand your data - part 2 #databricks #data #ai
Databricks metric views allow for advanced data definitions using joins, including nested joins with runtime 17.1+, and complex calculations with windowing for time-based analysis. Materialization can precompute popular metric views with incremental updates, and semantics can be added for non-technical users using runtime 17.2+.
NewsMaking AI understand your data - part 1 #ai #data #texttosql #code #vibecoding
Databricks' MetricView helps AI understand data by defining official sources and business logic, preventing inconsistent results from direct queries. The video demonstrates creating a MetricView in Unity Catalog, which can then be used with SQL or AI text-to-SQL tools for consistent data analysis.
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.
NewsSee Databricks Assistant Build a Metric View in 90 Seconds
The video demonstrates how Databricks Assistant can build a metric view in 90 seconds by generating YAML code for joins, dimensions, and measures from a natural language prompt. This metric view, a miniature semantic model, centralizes business logic and is queryable via SQL by various tools and agents.
NewsDatabricks News: Excel add-in, Metrics Views UI, and Quality Monitoring
Databricks announced Lake Watch for cybersecurity, new dynamic dropdown filters in SQL editor, and improved quality monitoring with null value scanning and automated alerts. The video also demonstrates a new UI for defining metric views, an Excel add-in for data preview and import, and the ability to publish dashboards as public web pages.
This release introduces row filter functionality and support for metric views.
NewsSynchronising Power BI to Metric Views with Tabular Editor's Semantic Bridge
Tabular Editor's new "Semantic Bridge" feature, launching in January, enables automatic synchronization of semantic models between Databricks Unity Catalog metric views and Power BI. This tool translates structural components and common SQL snippets into DAX, allowing users to maintain consistent business logic across different platforms.
NewsBringing the Semantics to Databricks Metric Views
Databricks Metric Views now include semantic metadata like display names, synonyms, and format specifications, which are auto-generated and enhance how business users interact with data. The video demonstrates creating and querying these metric views in SQL, highlighting their dynamic aggregation capabilities that differ from traditional database views.
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.
