Metric Views
Recent items mentioning Metric Views across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Apache Spark 4.2 now bakes governed business definitions directly into the engine via metric views 7, and Genie can auto-convert entire PowerBI dashboards into Databricks metric views 1. Community tooling is racing to keep up — new YAML/UI/materialization workflows 4, open questions on deploying metric views via DABs 6, and a push to expose them to Custom Apps outside the Databricks environment 2 — all reinforcing the argument that gold tables are no longer the end of the pipeline 5.
Generated daily from the 7 most recent items mentioning Metric Views. Click any [N] to jump to the source.
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.
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
Genie Agents can be grounded across both structured data and unstructured Unity Catalog Volumes, allowing a single agent to answer questions across all your enterprise data. By anchoring governance at the catalog layer with features like Automatic Identity Management, Row Filters, and Column Masks, the agent runs as the user's identity so every answer
[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.
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.
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.
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.
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.
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.
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.
