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databricks Platform · Updated Oct 2, 2025

Semantic metadata in metric views

You can now define semantic metadata in a metric view.

Update Open docs ↗release note ↗
What it does

You can now define semantic metadata in a metric view. Semantic metadata helps AI tools such as Genie Spaces and AI/BI dashboards interpret and use your data more effectively. To use semantic metadata, your metric view must use YAML specification version 1.1 or higher and run on Databricks Runtime 17.3 or above.

Why it matters

Table like asset source Reference a table like asset using its three part name: YAML source: catalog.schema.source table SQL query source To use a SQL query, write the query text directly in the YAML: YAML source: SELECT FROM samples.tpch.orders o LEFT JOIN samples.tpch.customer c ON o.o custkey = c.c custkey note When using a SQL query as a source with a JOIN clause, set primary and foreign key constraints on underlying tables and use the RELY option for optimal query performance.

Key capabilities
name: discount datatype: double default
name: discount datatype: double default: 0
name: product expr: product
name: product expr: product
name: discountedSales expr: SUM((1 -
name: discountedSales expr: SUM((1 - discount) amount)
name: orders source: samples.tpch.orders on
name: orders source: samples.tpch.orders on: source.lorderkey = orders.oorderkey
Architecture
Databricks Platform → Platform
In short

You can now define semantic metadata in a metric view. Semantic metadata helps AI tools such as Genie Spaces and AI/BI dashboards interpret and use your data more effectively.

Read the full documentation ↗
Key takeaway
You can now define semantic metadata in a metric view.
2025-10-02-semantic-metadata-in-metric-views Generated by the Databricks Updates Agent · browse all updates