Databricks releases, every cloud
- Confluence connector (GA)
Allows reading Confluence spaces and pages into Databricks.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Databricks SQL version 2026.10 is rolling out in Current
Replaces version 2026.09, requires no configuration changes.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Version 18.1
Supports DATETIMEOFFSET for Azure Synapse and adds schema evolution with INSERT statements.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - New cascade field for deleting Unity Catalog pipelines (Beta)
Deletes associated materialized views and tables by default, set cascade=false to retain them.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Databricks Documentation site table of contents tabs
Replaces static sidebar with tabbed navigation, includes 6 context tabs.
- Databricks SQL version 2026.10 is now available in Preview
Fixes observation metric errors, optimizes Unity Catalog writes, and uses session timezone for timestamp partitions.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
- Search in metric views fix
Now returns correct results across paginated results and supports filtering by display name.
- 5X-Large SQL warehouse size (Beta)
Available in all supported regions, controlled from Previews page.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Databricks SQL pipelines support notifications and performance mode (Beta)
Supports failure notifications and serverless performance mode for materialized views and streaming tables.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Workday HCM connector (Beta)
Ingests Workday Human Capital Management data into Databricks.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Serverless compute now available for IRAP and Canada Protected B workloads on Azure Databricks
Requires environment version 5 to be enabled.
- Version 18.0
Upgrades Redshift JDBC driver to 2.1.0.28, adds SQL window functions to metric views.
- Serverless environment version 5 is now available
Includes CPU and GPU versions.
- Change to use_case field in VPC endpoint API responses Coming soon
Changes from WORKSPACE_ACCESS to GENERAL_ACCESS, see API docs for details.
- Databricks SQL version 2025.40 is rolling out in Current
Replaces version 2025.39, requires no setup changes.
- TikTok Ads connector (Beta)
Ingests data from TikTok Ads into Databricks.
- HubSpot connector (Beta)
Ingests HubSpot Marketing Hub data into Databricks via Lakeflow Connect.
- Databricks SQL version 2025.40 is now available in Preview
Adds SQL scripting with procedural logic and expands parameter markers and IDENTIFIER clause support.
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- Serverless compute now available for compliance standards
Adds HITRUST, PCI-DSS, TISAX, and UK Cyber Essentials Plus support.
- Google Ads connector (Beta)
Ingests data from Google Ads into Databricks via Lakeflow Connect.
- New GCP regions support serverless compute
Available in me-central1 and southamerica-east1 regions.
- Zendesk Support connector (Beta)
Ingests ticket, help center, and community forum data from Zendesk Support.
- Trigger on update for pipelines (GA)
Refreshes pipeline when source table changes, usable in Databricks SQL pipeline schedules.
- View and restore your recent workspace tab sessions
Restores up to 10 recent sessions.
- Google Drive connector (Beta)
Supports read_files, spark.read, COPY INTO, and Auto Loader for ingesting files.
- You can now use custom base environments for Python, Python Wheels, and notebook tasks in serverless jobs
Defined with YAML files, they support Python, Python wheel, and notebook tasks.
- Ingest Salesforce formula fields incrementally (Beta)
Replaces snapshotting with incremental ingestion, improving performance and reducing costs.
- Row filtering for managed ingestion connectors (Beta)
Applies conditions like a SQL WHERE clause, available for Google Analytics, Salesforce, and ServiceNow connectors.
- Meta Ads connector (Beta)
Ingests data from Meta Ads, requires setup as a data source.
- Serverless compute is now available in Azure China North 3
Available for notebooks, jobs, pipelines, and SQL warehouses.
- Confluence connector (Beta)
Ingests Confluence spaces and pages into Databricks, requires OAuth U2M configuration.
- Serverless workspaces are now available in new GCP regions
Available in most GCP regions, see documentation for list.
- Change owner for materialized views or streaming tables defined in Databricks SQL
Done through Catalog Explorer, replacing previous methods.
- Customizable SharePoint connector (Beta)
Ingests structured, semi-structured, and unstructured files into Delta tables with customizable schema inference and parsing options.
- Discover files in Auto Loader efficiently using file events
Requires file events enabled on external locations. Replaces directory listing for efficiency.
- Jira connector (Beta)
Ingests Jira issues, comments, and attachments metadata into Databricks.
- Configure full refresh behavior for managed database connectors
Applies to Lakeflow Connect managed database connectors, allowing scheduled full refresh snapshots and automatic recovery from unsupported schema changes.
- Serverless compute is now available in new regions
Added to Australia Southeast, Canada Central, Mexico Central, Norway East, and South Africa North regions.
- Databricks SQL version 2025.35 is rolling out in Current
Includes new features, see documentation for details.
- Jobs can now be triggered on source view update
Supports views referencing supported table types, see documentation for specifics.
Headlines, dates and product areas are Databricks' own, and every item links to the note it came from. The one-line summaries are ours.