Metric Views
Recent items mentioning Metric Views across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks Unity Catalog Metric Views are gaining traction as a headless semantic layer for governed business metrics across BI tools and AI agents 7. Recent activity highlights their use in building "Talk to Data" solutions with AI-ready semantic layers, as demonstrated by Mercedes-Benz Korea 4, and for migrating KPI dashboards from other platforms like Tableau 6. Community discussions also cover querying Metric Views via SQL Warehouses 3 and integrating them with Power BI and Tabular Editor 2.
Generated daily from the 10 most recent items mentioning Metric Views. Click any [N] to jump to the source.
Genie space: Delta Tables or Metric View
Metric Views with Power BI and Tabular Editor (Part 3 of 3)
UnityCatalog 0.5.0
This release introduces a new UC Delta API for managing Delta tables via REST, enabling various engines to use Unity Catalog as a Delta-native catalog. The UC Spark connector now has separate artifacts for Spark 4.0.x and 4.1.x compatibility, and its credential-scoped file system is enabled by default.
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.
This release adds support for metric views, row filters, Python UDFs, and key-only Databricks tags. It also includes a breaking change where Databricks tags now merge additively 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.
TutorialsStep-by-Step: Using the Databricks Excel Add-in to Analyze Governed Lakehouse Data
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 new 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.
