Releasesby GlyphFlows are securable objects in Unity Catalog (Beta)
Pipeline flows are now securable objects in Unity Catalog. You can grant READ FLOW , REFRESH FLOW , WRITE FLOW , APPLY TAG , and MANAGE on individual flows, and CREATE FLOW on schemas. To participate in the Beta, contact your Databricks account team. See Flows in Unity Catalog .
Also shipped Nov 12, 2026Databricks in November 2026 →
Replit Agent can now buy for your business and help you reach customers
Replit updated Replit Agent with capabilities to purchase business supplies using Link and Visa and manage advertising campaigns on Google Ads.
Table-level data lineage for BigQuery pipelines in Knowledge Catalog is GA
Google Cloud made table-level data lineage in Knowledge Catalog generally available for BigQuery pipelines, enabling tracking of data flow across pipelines.
BigQuery generative AI functions support Gemini 4 Hydrogen
BigQuery generative AI functions now support the Gemini 4 Hydrogen model in general availability.
Amazon Aurora and RDS now support Graviton5-based R9g database instances
AWS announced the general availability of Graviton5-based R9g database instances for Amazon Aurora and Amazon RDS. The R9g instances offer up to 30% better performance and up to 22% improved price-performance over Graviton4 instances.
[In preview] Public Preview: SQL performance monitoring for Azure SQL Managed Instance
Microsoft launched a public preview of managed SQL performance monitoring for Azure SQL Managed Instance, providing native monitoring capabilities for business-critical database workloads.
Gemini Enterprise: Gemini 4 Hydrogen available in web app, mobile app, Workflow Builder, and AI developer tools
Gemini 4 Hydrogen is generally available across Gemini Enterprise web and mobile applications, Workflow Builder, and AI developer tools, with data residency support in the US and EU.
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AI Function task support in Declarative Automation Bundles (Beta)
Databricks introduced Beta support for AI Function tasks within Declarative Automation Bundles.
Standalone pipelines on serverless general compute are now generally available
Creating and refreshing standalone materialized views and streaming tables from a notebook on serverless general compute is now generally available. You can define them with SQL through spark.sql() or with the Python pipelines decorators. See Notebooks and Use Python with standalone pipelines .
Partial schema inference for Python Lakeflow pipelines
In an upcoming release, Python Lakeflow pipelines will support partial schema inference in Beta. You will be able to use the override_schema argument of table , materialized_view , or create_streaming_table to declare only the columns whose type, constraint, or comment you want to control. The pipeline infers the remaining columns from the query output.
New jobs compute will soon default to Auto access mode for workspaces with the compliance security profile enabled
New jobs compute in Databricks will default to Auto access mode. This mode uses Standard access mode unless specific requirements like GPU instances or older runtimes are met.
New jobs compute defaults to Auto access mode
Databricks updated the default access mode for new jobs compute in the UI to Auto. This change automatically selects Standard access mode unless specific runtime or hardware configurations are required.
Shift dates in Lakeflow Jobs parameters with date arithmetic (Beta)
Lakeflow Jobs now supports dynamic date arithmetic, allowing users to shift dates by a duration within job parameters.
Use cleaned AI answers in Lakeflow Designer (Public Preview)
Clean supported AI Function outputs in Lakeflow Designer for downstream transformations. Cleaned results include answer values, error messages, and raw responses in separate columns. See AI Function .
Distinguish Lakeflow Designer Python previews from full runs (Public Preview)
Python operators in Lakeflow Designer now expose config["is_preview"] so your code can skip side effects during previews. See Python preview mode .