Releasesby GlyphNew agent_type column in QUERY_HISTORY views
The QUERY_HISTORY views and table functions now include a new agent_type column that identifies the type of agent that directly invoked a query. This column helps you audit and monitor agent-initiated queries across your account. Possible values for the agent_type column: The column is NULL if the query wasn’t invoked by an agent.
Also shipped Jul 27, 2026Snowflake in July 2026 →
You can discover commercial BigQuery sharing listings on Google Cloud Marketplace with the Marketplace filter
You can discover commercial BigQuery sharing listings on Google Cloud Marketplace with the Marketplace filter. For more information, see Subscribe to a Cloud Marketplace-integrated listing . This feature is generally available (GA).
BigQuery APPENDS and CHANGES change history functions are GA
The APPENDS and CHANGES functions return the rows appended to or changed in a table over a time range, useful for incremental processing and auditing.
Google-developed ODBC driver for BigQuery is generally available
Applications can connect to BigQuery using the Google-developed Open Database Connectivity (ODBC) driver, now generally available.
BigQuery dataset insights is generally available (GA)
BigQuery dataset insights is generally available (GA). BigQuery dataset insights helps you discover and visualize relationships between tables and automatically generate cross-table queries.
AlloyDB write endpoints are now available in Preview
AlloyDB write endpoints are now available in Preview . Write endpoints simplify database connection management by providing a stable domain name service (DNS) name for your applications, decoupling them from instance IP addresses.
AlloyDB now supports cross-region failover in Preview
AlloyDB now supports cross-region failover in Preview . You can optionally enable cross-region failover to automate the recreation of the original primary when a secondary cluster is promoted while maintaining your replication topology without requiring manual cluster deletion or re-creation.
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Custom incremental dynamic tables (General availability)
Custom incremental dynamic tables are now generally available. For more information, see Custom incrementalization .
AI code suggestions in Workspaces (General availability)
AI code suggestions in Cortex Code are now generally available. This release also includes updated keybindings and bug fixes. Notebook Python cells , no changes to keybindings.
Data quality monitoring dashboard (Public preview)
The data quality monitoring dashboard is now available in public preview for Enterprise Edition accounts. The dashboard provides an account-wide view of data quality health in Snowsight.
Multiple host names for private connectivity endpoints on Microsoft Azure
You can now register more than one host name on an existing private connectivity endpoint using the new SYSTEM$ADD_PRIVATELINK_ENDPOINT_HOSTNAME and SYSTEM$REMOVE_PRIVATELINK_ENDPOINT_HOSTNAME functions.
Snowflake Data Clean Rooms updates
Clean room UI users: New updates are applied to the UI automatically when a user starts a new session after a new update is deployed. To ensure new updates are applied, make sure to log out and log back into the UI. Clean room API users: API updates are applied either manually or automatically to an account. Learn how to manage updates for Data Clean Rooms.
Adaptive refresh mode for dynamic tables (General availability)
ADAPTIVE refresh mode for dynamic tables is now generally available. ADAPTIVE refresh uses incremental refresh by default but automatically reinitializes the dynamic table when internal heuristics detect that the volume of upstream changes makes an incremental refresh significantly more expensive than a full refresh.
Compute monitoring and run observability for Notebooks in Workspaces
With this release, Snowflake Notebooks in Workspaces gives you more visibility and control over the compute that runs your notebooks, both while you develop interactively and when you troubleshoot scheduled runs.
SQL environment variable and private Git package support for dbt Projects on Snowflake
Environment variable support for dbt Projects on Snowflake is now generally available. Manage per-developer schemas, dynamic runtime values, secrets, and multiple environments in a single, Git-versioned env.yml file that Snowflake resolves before each run.