Releasesby GlyphPartial 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.
Also shipped Nov 11, 2026Databricks in November 2026 →
Web search in apps
Retool added native web search capabilities to the app builder Chat tab and app functions, enabling agent applications to query live web data via a managed Tavily account.
Eval datasets, curated by Bayes from your agents' real conversations
Monte Carlo introduced automated evaluation dataset generation by Bayes, creating Golden and Improvement datasets from an agent's scored conversations across intent and issue clusters.
BigQuery project caps are generally available
Project caps, also known as scheduling policies, are now generally available for BigQuery Enterprise and Enterprise Plus editions. This feature allows users to limit maximum slots and concurrency per project within a reservation.
BigQuery reservation assignments support principal routing
BigQuery now allows specifying a principal property on reservation assignments to route queries to specific reservations based on the identity of the user or service account. This feature is generally available for Enterprise and Enterprise Plus editions.
BigQuery JSON_FLATTEN function is generally available
The JSON_FLATTEN function is now generally available. It extracts non-array values from JSON inputs, including those nested within consecutive arrays.
Ornith-1.0-9B and Ornith-1.0-35B models now available on Amazon SageMaker JumpStart
DeepReinforce AI's Ornith-1.0-9B and Ornith-1.0-35B models are now available on Amazon SageMaker JumpStart. These open-source models are designed for agentic coding workflows.
More from LakeflowEvery Lakeflow release →
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 .
AI Function task support in Declarative Automation Bundles (Beta)
Support for AI Function tasks in Declarative Automation Bundles is now in Beta . See AI Function task .
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 .
Calculate across rows in Lakeflow Designer (Public Preview)
Use the Multi-Row Formula operator in Lakeflow Designer for running totals, ranks, moving averages, and other calculations across ordered rows. See Multi-Row Formula .