9.36 Release Notes: Nov 10, 2025-Nov 16, 2025¶

Attention

This release has completed. For differences between the in-advance and final versions of these release notes, see Release notes change log.

SQL updates¶

Enhanced SQL functionality¶

This update enhances your ability to modify properties on both functions and stored procedures:

Function category	Function	Description
Function	CREATE OR ALTER FUNCTION	Updated to support changing function definition. For example, RUNTIME_VERSION, ARTIFACT_REPOSITORY (Python), PACKAGES, IMPORTS, return type, and function body.
Procedure	CREATE OR ALTER PROCEDURE	Updated to support changing procedure definition. For example, RUNTIME_VERSION, IMPORTS, PACKAGES, return type, procedure body, and ARTIFACT_REPOSITORY for Python stored procedures.

Extensibility updates¶

Support for OAuth when authenticating with GitHub (General availability)¶

You can authenticate using OAuth when you’re integrating a repository on GitHub with Snowflake.

For more information, see Configure for authenticating with OAuth.

Run Apache Spark™ workloads on Snowflake (General availability)¶

You can connect your existing Spark workloads directly to Snowflake and run them on the Snowflake compute engine. As a result, you can run your PySpark dataframe code with all the benefits of the Snowflake engine.

For more information, see Apache Spark™ workloads on Snowflake with Snowpark Connect.

Support for connecting Scala applications to Snowpark Connect for Spark (Preview)¶

You can now connect your Scala applications to the Snowpark Connect for Spark server. After you configure a connection to authenticate with Snowflake and start the Snowpark Connect for Spark server, you can run Scala code to connect to Snowpark Connect for Spark.

For more information, see Getting Started with Snowpark Connect for Scala Applications.

Data governance updates¶

Anomaly detection for Data Quality Monitoring (Preview)¶

Set up anomaly detection for data quality monitoring so that Snowflake automatically detects unexpected changes in the following dimensions:

Volume of data in a table.
Frequency with which a table is being updated.

For more information, see Detecting anomalies in data quality.

Release notes change log¶

Announcement	Update	Date
Release notes	Initial publication (preview)	Nov 07, 2025