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databricks Platform · Updated Dec 17, 2025

New resource types for Databricks Apps

You can now add MLflow experiments, vector search indexes, user defined functions (UDFs), and Unity Catalog connections as Databricks Apps resources.

Update Open docs ↗release note ↗
What it does

You can now add MLflow experiments, vector search indexes, user defined functions (UDFs), and Unity Catalog connections as Databricks Apps resources. Your Databricks apps can integrate with various Databricks platform features, such as Databricks SQL for querying data, Lakeflow Jobs for data ingestion and processing, Model Serving to access generative AI models, and Databricks secrets for managing sensitive information. In the context of apps, these platform features are referred to as resources.

Why it matters

Reduces operational overhead and unlocks new capabilities for data teams on Databricks.

Key capabilities
DATABRICKSCLIENTID
DATABRICKSCLIENTID
DATABRICKSCLIENTSECRET Don't share service principal
DATABRICKSCLIENTSECRET Don't share service principal credentials between apps or across users. Each app should use its own…
Grant CAN USE on a
Grant CAN USE on a SQL warehouse if the app only needs to run queries.
Grant CAN QUERY on a
Grant CAN QUERY on a serving endpoint if the app only sends inference requests.
Architecture
Databricks Platform → Platform
In short

You can now add MLflow experiments, vector search indexes, user defined functions (UDFs), and Unity Catalog connections as Databricks Apps resources. Your Databricks apps can integrate with various Databricks platform features, such as Databricks SQL for…

Read the full documentation ↗
Key takeaway
You can now add MLflow experiments, vector search indexes, user defined functions (UDFs), and Unity Catalog connections as Databricks Apps resources.
2025-12-17-new-resource-types-for-databricks-apps Generated by the Databricks Updates Agent · browse all updates