Databricks releases, every cloud
- Transactions are now generally available
Require catalog commits enabled on participating tables. Use BEGIN ATOMIC or BEGIN TRANSACTION syntax.
- Variant and Variant shredding are now generally available
Requires Databricks Runtime 17.3, recommended 18.1 for optimal performance.
- Scala and Java UDFs in Unity Catalog are now generally available
Reuses existing JVM logic, offers better performance than Python UDFs.
- Databricks SQL alerts are now available by default for workspaces with the compliance security profile enabled
Supports all compliance security profile standards, monitors data and KPIs via scheduled queries.
- Parquet v2 for Delta Lake tables is generally available
Requires Databricks Runtime 18.1 and above, set delta.parquet.format.version to 2.12.0.
- Auto time-to-live for automatic row deletion is generally available
Deletes rows from Delta Lake, Iceberg, and streaming tables based on a DATE or TIMESTAMP column.
- Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
- Converting a partitioned table to liquid clustering is generally available
Uses ALTER TABLE command, requires Databricks Runtime 18.1 or above.
- Fixed LOD expression syntax update for metric views
Uses SQL window functions, replacing two-step pre-computation approach.
- Databricks SQL alerts are now generally available
Monitors data and KPIs by running scheduled queries, notifying recipients when conditions are met.
- Catalog commits are now generally available
Enables multi-statement transactions on Unity Catalog managed Delta tables, supported by products like MLflow and Delta Sharing.
- Search in metric views fix
Now returns correct results across paginated results and supports filtering by display name.
Headlines, dates and product areas are Databricks' own, from the release notes published for each cloud, and every item links to the note it came from. The one-line summaries are ours. Not a Databricks product and not affiliated with Databricks, Inc.