Releasesby GlyphDatabricks Runtime 19: August 10, 2026
New features and improvements collect_union aggregate function : SQL and the Scala and Python DataFrame APIs now support collect_union , which returns the distinct union of the elements in arrays within each group.
Also shipped Aug 10, 2026Databricks in August 2026 →
BigQuery supports gemini-3.1-flash-lite and gemini-3.5-flash GA models
Both models work in all BigQuery generative AI functions and are available on the us, eu and global multi-regional endpoints.
AI_MULTI_EMBED support for large video files in semantic video search (General availability)
Snowflake Cortex AI_MULTI_EMBED is now generally available for semantic video search with support for large video files up to 6 GB using the TwelveLabs Marengo Embed 3.0 model, enabling organizations to index and search production-grade video content directly in Snowflake.
Amazon S3 Tables Iceberg REST catalog integration (General availability)
Snowflake can now connect directly to the Amazon S3 Tables Iceberg REST endpoint, so you don’t need to create an AWS Glue integration. This capability is now generally available.
Request Access in Workspaces (Preview)
With this preview release, you can resolve access-related SQL errors in Workspaces by submitting access requests directly from Snowsight. When a query fails because of missing privileges and the user can’t self-resolve the issue by switching roles, the user can submit a request with a reason.
Spanner dynamic channel pooling for Go and Java clients is GA
Dynamic channel pooling for gRPC channels prevents issues from under- or over-provisioned channels and reduces configuration overhead in the Go and Java client libraries. It is disabled by default.
Gemini Enterprise: Export user data to a CSV file
Gemini Enterprise administrators can export user data to a comma-separated values (CSV) file to sort, filter, and analyze records offline. This feature is generally available (GA). For more information, see Export user data .
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Expanded networking billing for OpenSharing SecureConnect
Databricks will be expanding networking billing for OpenSharing SecureConnect. Today, data providers are billed for data transfer (egress) when recipients access shared data through SecureConnect. Additional charge types and access scenarios, including charges billed to recipients, are planned.
OpenSharing SecureConnect will be available by default for all accounts
OpenSharing SecureConnect is generally available and available by default for accounts on the Premium plan. For accounts on the Enterprise plan, Databricks will enable it automatically in mid-September 2026, and by early 2027 for the rest.
Share data behind a firewall with SecureConnect is generally available
OpenSharing SecureConnect is now generally available . Providers can share data from cloud storage behind a firewall or private endpoint without adding each recipient's network to an allowlist, and recipients access the shares using their existing OpenSharing setup.
Identity attributes (Beta)
Identity attributes are in Beta . Databricks can store nine IdP-sourced attributes, such as title, department, and cost center, on account users, so they can be referenced in Unity Catalog ABAC column-masking policies.
Cross-workspace access for serverless (Beta)
You can now control serverless network traffic between Databricks workspaces. Account admins use cross-workspace access to set which source workspaces can reach a workspace (ingress) and which destination workspaces a workspace's serverless workloads can send traffic to (egress).
ai_search function (Beta)
The ai_search() SQL function retrieves information from one or more AI Search indexes configured as knowledge sources. Given a natural-language query, the function generates optimized search queries, retrieves and deduplicates results across sources, reranks them by relevance, and returns the most relevant documents.
JAR tasks on serverless compute are now generally available
Package Scala or Java code as a JAR and run it as a task in a Lakeflow Job on serverless compute. Your JAR must match the Scala, Java Development Kit (JDK), and Databricks Connect versions of your serverless environment version. See Create and run JARs on serverless compute .
Search and replace text across files
You can now search and replace text across files in a Databricks Git folder or Declarative Automation Bundles project from the Search pane. See Search and replace across files .