Releasesby GlyphAI 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 .
Also shipped Nov 13, 2026Databricks in November 2026 →
Many-to-many relationships in semantic views (Preview)
Many-to-many relationships are now in preview for semantic views. You can model a many-to-many relationship between two logical tables in either of the following ways:
Metabase 65.4
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Metabase 64.6
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Metabase 58.40
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Metabase 58.39
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Metabase 64.5
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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 .
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 .
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 .