Starting November 18, 2026, Databricks will automatically add the variantShredding table feature to new tables with a VARIANT column when they are created on Databricks Runtime 17.3 or above. Variant shredding stores frequently accessed fields in a typed, columnar layout to improve query performance. Clusters on Databricks Runtime 15.4 cannot read from or write to those tables, so affected clusters should be upgraded to Databricks Runtime 16.4 or above before the change takes effect.
The Databricks platform, as it moves
News
From the official release notes and blog. Every item points at the concepts worth re-reading.
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Data Ingestion
Release
Variant shredding will be added by default to new VARIANT tables
Semi-structured data: JSON, nested data, VARIANT +1Read
Jobs & Pipelines
Release
JAR tasks on serverless compute are coming to compliance-profile workspaces
In early October 2026, Databricks will automatically enable JAR tasks on serverless compute for workspaces with the compliance security profile enabled. Users can package Java or Scala Spark code in a JAR and run it as a JAR task in a Lakeflow Job without provisioning or managing clusters.
Serverless compute for jobsRead
Compute
Release
Environment version 6 is the new serverless base
The base environment for serverless and standard classic compute moved to version 6. Pinning an older one keeps working, but the default a new workload gets has changed.
Serverless compute +1Read
Everything, day by day
Monday 5 October
Tuesday 1 September
- Maintenance updates are available for supported Databricks Runtime versionsDatabricks released maintenance updates for supported Databricks Runtime versions. The entry says the updates include bug fixes, security patches, and performance improvements for Databricks Runtime 18 LTS, 18.2, 18.1, 17.3 LTS, 16.4 LTS, 15.4 LTS, 14.3 LTS, and 13.3 LTS.
- Git folders can use one shared serverless compute resource in betaGit Folder Serverless entered beta in Databricks. It uses one serverless compute resource shared by every notebook and file in a Git folder, with the folder Python environment managed by a root pyproject.toml instead of per notebook.
Saturday 1 August
- Lakehouse Replay is in Public Preview for serverless workload checksLakehouse Replay entered Public Preview in Databricks. It automatically replays a sample of read-only serverless workloads against upcoming Databricks Runtime releases to catch regressions before they reach production.
- Private network gateway enters Private PreviewPrivate network gateway is in Private Preview for Databricks. It provides a managed route from Databricks serverless compute to resources in a customer VPC, and to destinations that VPC can reach, using one subnet, a cross-account IAM role, and existing network connectivity configuration.
Nothing of that kind yet.