Metric Views
Recent items mentioning Metric Views across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
What is Metric Views?
A metric view is a Unity Catalog object that holds business metric definitions: measures such as revenue or active users, plus the dimensions used to group and filter them. You define measures and dimensions in YAML, created through SQL DDL or the Catalog Explorer UI. Unlike a standard view, it doesn't bake one aggregation in at creation time; measures are computed at query time against whatever grouping a query asks for.
It exists to end duplicate metric logic. When every dashboard and notebook rewrites its own aggregation, numbers drift apart and nobody trusts them. A metric view keeps one governed definition in the catalog, covered by normal Unity Catalog permissions and lineage, and every consumer reads from it: SQL editors, notebooks, dashboards, Genie Agents, alerts, and external BI tools including Power BI, Tableau, and Sigma.
Metric views have been generally available since April 2, 2026, announced as part of Unity Catalog Business Semantics, and Databricks is open sourcing the core implementation in Apache Spark. You need a workspace enabled for Unity Catalog, and creating a metric view is supported on Databricks Runtime 16.4 and above, with some features requiring later versions.
Are metric views generally available?
Yes. Metric views reached GA on April 2, 2026 as part of Unity Catalog Business Semantics. Databricks is also open sourcing the core implementation in Apache Spark.
What do I need to use metric views?
A workspace enabled for Unity Catalog. Creating a metric view is supported on Databricks Runtime 16.4 and above, though some features require later versions; the official tutorial uses a SQL warehouse or compute running Runtime 17.3 or above.
How is a metric view different from a regular view?
A regular view locks its aggregations in when you create it. A metric view stores measures and dimensions separately and computes measures at query time, so one definition answers daily, regional, and per-customer questions without rewriting SQL.
Can external BI tools use metric views?
Yes. Metric views can be consumed from Power BI, Tableau, Sigma, and other external BI tools, alongside Databricks surfaces like SQL editors, notebooks, dashboards, Genie Agents, and alerts.
Sources: Unity Catalog metric views (Databricks docs) · Create a metric view (Databricks docs) · GA announcement: Unity Catalog Business Semantics (Databricks blog)
dbt-databricks v1.12.5 changed how metric views materialize, switching to a backup-and-create strategy instead of direct create-or-replace syntax 3, while UnityCatalog 0.6.0 formalized metric views (alongside SQL views) as governed catalog objects for semantic layers 8. Genie can now auto-convert PowerBI dashboards into Databricks metric views 9, even as practitioners report materialization failures 2 and ask how to route metric views back out to PowerBI 1.
Generated daily from the 9 most recent items mentioning Metric Views. Click any [N] to jump to the source.
Databricks metric views to PowerBI?
submitted by /u/FiftyShadesOfBlack [link] [comments]
Metric views materialization failure
Metric views now replace existing tables or views using a backup-and-create strategy rather than direct create-or-replace syntax. Lazily formatted Databricks SQL log records are also now properly interpolated when mirrored into dbt logs.
NewsDatabricks LakeFlow Designer: Visual Pipelines, Real Code
LakeFlow Designer is a visual no-code tool for building governed data pipelines that generates transparent SQL or Python code. Recent releases add AI-powered operator search, on-demand custom operators, direct table transformations, email notifications, and built-in visualizations.
Excel Add-In Roadmap
Has there been any public roadmap for the excel add-in? I was going to install and try it out this weekend, but I can already foresee some of its shortcomings. I'm accustomed to using Excel pivot tables for Microsoft OLAP (which are pretty hard to beat!) Questions: - Based on docs it didn't appear that this add-in would reap the benefits of lakebase (sub-ten-ms queries). Isn't that the point of using Excel, to interact with data instantly? Can we get an experience that is specifically tailored to lakebase? The CEO of databricks keeps acknowledging that "agents like fast data". But here is a newsflash; humans like fast data too! We've had fast data in Excel/SSAS pivot tables for decades. - I'm assuming this tech sends SQL queries back to the SaaS service for processing. Is that fundamentally better than the ODBC support already available to excel users? I'm guessing the catalog/usability/security is the main attraction (ie. making things "easier" and more secure). - If lakebase is as fast as the CEO claims, will it ever be possible to transpile MDX? Will those sorts of queries be able to run on lakebase? Some other open source tools do MDX, as we can see in Mondrian or Apache Kylin. These tools offer a robust, high-performance pivot table experience in Excel. Any information would be appreciated. I'm guessing it will be a very long time before Databricks wants to pursue MDX, or compete with the normal pivot tables available from Microsoft. They are more likely to follow down the current path with "metric views" for several years, rather than using pre-existing technology. From a customer perspective, I think it would be amazing if Databricks could offer an Excel experience that approaches the ones offered by Microsoft/Fabric. (One thing that is particularly compelling about the Databricks add-in is the write-back. This was something that Microsoft attempted long ago, but wasn't able to be successful with it. I'm interested to see if Databricks can do better. If nothing else, I think the culture of modern databricks user may be more receptive than the culture of the users doing write-back to OLAP cubes.) submitted by /u/SmallAd3697 [link] [comments]
What is the general guidance for data structure when using a text to SQL agent such as Databricks Genie?
I’ve heard use of metric views is highly recommended, but never sure if OBT approach or normalized approach works better. If I’m defining the joins in my Genie, shouldn’t it be able to pull things either way? submitted by /u/ceeej777 [link] [comments]
Databricks Metric Views- Stop Building Metrics Twice
UnityCatalog 0.6.0
This release adds Apache Spark 4.2 support and introduces metric views and SQL views as governed catalog objects for semantic layers and query management. UC access tokens now expire after 24 hours by default, requiring periodic re-exchange instead of the previous indefinite validity.
NewsDatabricks News: ZeroOps, DABs, Indexes, Genie, sandboxes, migration from PowerBI, secrets
Zero Ops automatically detects errors in jobs and data quality with lineage analysis and proposes code fixes, while DABs now default to direct mode instead of Terraform with automatic state migration. Full-text search indexes deliver 400x faster queries on billion-row tables, Genie automatically converts PowerBI dashboards to Databricks metric views, and Unity Catalog secrets support granular read and reference-only permissions.
Getting ready for Genie Ontology
Are you getting ready for Genie Ontology? You can leverage PAGES that sit in the Discover page and are organized by domain and subdomain. Each domain and subdomain has its own set of Pages and users with access to a domain can create and govern them. 🛑 But first, what do you mean by Pages? Pages are part of UC semantics; it's the business context that you define and govern explicitly, forming the human-modeled layer of the Genie Ontology. 🛑 Why is it useful? When Genie One answers a question about a concept you've defined in a Page, it prioritizes the Page's definition over context it infers automatically, and cites the Page so users can confirm the source. 🛑 Any tips to build pages? You can create Pages from those documents instead of writing each one by hand. Genie Code reads the documents you attach, extracts the terms it finds, and returns a set of proposed Pages. You review and edit the proposed Pages before any of them are created. 🛑 Is it a collaborative environment? You can Comment : Ask a follow-up question or flag context for the owner. You can Suggest edits : Suggest changes to a published Page's body. Each time you click Suggestion , edit the body, and click Save , your edits are grouped into a single batch. The owner or curator accepts or rejects the entire batch at once. Accepting a batch clears all other pending batches on the Page, including those from other users, and this can't be undone. You can React : Upvote or downvote a Page to signal whether it answered your question. The owner or curator can also edit a published Page's content directly, bypassing the suggestion workflow. 🛑What's next? Create domains, Subdomains, leverage UC metric views, and connect your external tools to Databricks submitted by /u/Youssef_Mrini [link] [comments]
Unity Catalog Metric Views to be accessible to Custom Apps outside of DBX environment
How to ground Genie Agents in both structured data and documents without losing governance
Ground Genie Agents across structured data and unstructured Unity Catalog Volumes so a single agent can answer questions across all of your data. By enforcing governance at the catalog layer rather than the model layer, agents run under the user's identity to filter every answer through Unity Catalog object privileges, row filters, and column masks.
[PARTNER BLOG] Evolving Metric Views: YAML, UI, Genie Code & Materialization
Databricks Metric Views: Why Gold Tables Are No Longer the End of the Pipeline
can i deploy a metric view using DABs
Introducing Apache Spark 4.2
Apache Spark 4.2 introduces governed business definitions via metric views, AI-native analytics features like vector retrieval, and simplified real-time data processing through Auto CDC and Real-Time Mode. This release also expands Spark's accessibility from external services and AI agents by leveraging Spark Connect, Arrow-first Python execution, and Python Data Sources.
Introduces catalogs.yml v2 support, a skip_optimize config for opting out of post-materialization OPTIMIZE, and Rust kernel backend for SQL warehouses. Fixes numerous incremental model bugs around constraints and tags, but now requires --full-refresh to apply changes to primary/foreign key expressions.
Genie space: Delta Tables or Metric View
Metric Views with Power BI and Tabular Editor (Part 3 of 3)
UnityCatalog 0.5.0
UC 0.5.0 introduces a dedicated UC Delta API for catalog-managed Delta table operations across Spark, Flink, Trino, DuckDB, and other engines, with standardized REST endpoints and server-side commit validation. The Spark connector now ships separate artifacts for Spark 4.0 and 4.1 and enables credential-scoped file systems by default, fixing out-of-memory issues in long-running sessions.
Querying Metric Views via Classic/Pro SQL Warehouses
Unlocking semantics for AI: How Mercedes-Benz Korea built trusted “Talk to Data” at scale
Mercedes-Benz Korea built a trusted "Talk to Data" solution at scale by making 500+ KPI definitions available in an AI-ready semantic layer on Unity Catalog metric views, accelerating the transition with an automated DAX-to-Metric-View transpiler. This governed semantic layer supports both existing BI and new "Talk to Data" experiences, with Genie and Agent Bricks providing consistent answers and shaping a playbook for persona-based AI agents across markets.
Metric Views in AI/BI Dashboards & Genie (Part 2 of 3)
From Tableau to Databricks: Migrating KPI Dashboards with Metric Views
BI Serving Pointers; Maximizing for Performance and TCO
Databricks now offers Unity Catalog Metric Views for a headless semantic layer, enabling governed business metrics across all BI tools and AI agents. Maximize performance and TCO by structuring your physical layer with star schemas, liquid clustering, and Predictive Optimization, and leverage aggregate-aware materialization for OLAP-style performance.
AI readiness in telecommunications
Telco AI initiatives stall at production scale due to data debt, not model quality; Databricks Unity Catalog provides the semantic layer and governance needed to bridge this gap. It unifies disparate systems via Lakehouse Federation, offering AI agents rich context and enabling end-to-end governance for regulatory compliance and accurate operational tasks.
TutorialsThe Future of Finance Operations Starts Here
The video demonstrates how Databricks' financial lakehouse solution addresses common finance data challenges like fragmentation and slow analysis. It showcases features like Unity Catalog for data governance, Lake Flow for pipeline management, and Genie Spaces for natural language querying of financial data.
Introduction to Metric Views (part 1 of 3)
Power BI/Tableau to Databricks the new way!
Have you noticed the new feature that allows for direct migration from Power BI/Tableau to Power BI using Genie Code? Currently it is a Beta feature. I took it for a spin and I am quite impressed to be honest. It took my semantic model and converted it to Metric Views 1:1. Except for a few DAX specific functions (which were flagged to me) everything was working the same way as in Power BI, including relations, measures etc. One thing I noticed is that Genie Code thought it should be able to convert the visual side as well, however it kept saying that there were no visuals in my exported .pbit file. \- I worked around it by including a screenshot, which it then rendered into the visuals correctly. [https://www.linkedin.com/feed/update/urn:li:activity:7464937157162541057/](https://www.linkedin.com/feed/update/urn:li:activity:7464937157162541057/) Anyone from Databricks that can give a heads up on that part - why does it not recognize the visuals from the file?
Reports migration made easy through genie Code!
Genie Code now supports migrating from Tableau and PowerBI. With the new /𝒊𝒎𝒑𝒐𝒓𝒕𝑩𝑰 command, you can: → Upload a .twb, .twbx, .tds, .tdsx, or .pbit file directly → Auto-generate an AI/BI dashboard that mirrors your existing visualizations → Convert measures & dimensions into reusable metric views → Promote them to Unity Catalog for governance, lineage, and access control The migration path that used to take weeks now starts with a single command.
Databricks now supports importing Tableau and Power BI files into Genie Code to automatically build AI/BI Dashboards with Metric Views
With **Genie Code**, you can now add a Tableau or Power BI file and have it build an **AI/BI dashboard** that replicates your existing visualizations - while connecting them to **metric views** that mirror the underlying business logic. [Import BI files using Genie Code - Azure Databricks | Microsoft Learn](https://learn.microsoft.com/en-us/azure/databricks/dashboards/manage/import-bi#requirements) Many organizations have years of BI logic embedded inside workbooks, reports, templates, and semantic layers. Rebuilding that logic manually in a new platform can be slow, error-prone, and difficult to govern. This new workflow helps accelerate that migration path: 1. Upload a Tableau or Power BI file directly into Genie Code Supported formats include `.twb`, `.twbx`, `.tds`, `.tdsx`, and `.pbit`. 2. Use the `/importBI` command in Agent mode Genie Code imports the BI asset and generates an AI/BI dashboard. 3. Review the generated dashboard and metric views Measures and dimensions from the original file are transformed into metric views. 4. Promote metric views to Unity Catalog This makes them reusable across dashboards, Genie Spaces, and notebooks, while adding governance, lineage, access controls, and discoverability. Currently, there is also a 100 MB limit for direct file uploads. For larger files, the recommended path is to store the file in a Unity Catalog volume and reference it directly, for example: `/importBI @/Volumes/my_catalog/my_schema/my_volume/sales_workbook.twb`
v1.12.0 adds metric views, row filters, and Python UDFs as new materializations, along with SCHEDULE EVERY and TRIGGER ON UPDATE refresh modes for materialized views and streaming tables. databricks_tags now merge additively across hierarchy levels instead of child replacing parent (breaking change), and the release includes fixes for metric view validation, pydantic v1 compatibility, and streaming table refresh scheduling.
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
The Databricks Excel add-in allows business users to connect directly to Unity Catalog to browse, filter, and import governed tables and metric views without writing SQL. Users can also run custom or shared queries, generate pivot tables, and refresh all workbook data with a single click.
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.
dbt-databricks v1.12.0a1 introduces row filter functionality for implementing row-level access controls and adds support for metric views. These features expand SQL modeling capabilities within the Databricks environment.
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.
NewsAI/BI Dashboards - July 2025 Updates
Databricks AI BI dashboards have added several new features, including custom dashboard themes, global filters, drill-through capabilities, and file uploads. The updates also introduce automated forecasting, chloropleth map visualizations, and scheduled parameterized reports.
Tutorials46 AIBI Dashboards & Visualizations | Consumer Access in Databricks | Forecasting Reports
AIBI dashboards in Databricks enable users to create interactive visualizations and reports from their data using a drag-and-drop interface, with AI-assisted features like automated visualization generation and forecasting. Business users can then access and consume these published dashboards through a simplified consumer access profile that restricts permissions to viewing and refreshing only.
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