Databricks releases, every cloud

1. Scala and Java UDFs in Unity Catalog are now generally available

   Reuses existing JVM logic, offers better performance than Python UDFs.

   #Data Engineering
   AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP, read it on their docs Jul 16, 2026
2. Data Classification is now available by default for additional compliance standards

   Requires compliance security profile with C5, TISAX, or IRAP controls.

   #Data Classification#Unity Catalog
   AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP, read it on their docs Jul 15, 2026
3. 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.

   #Data Engineering
   AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP, read it on their docs Jun 29, 2026
4. Data Classification is now available by default for some workspaces with the compliance security profile enabled

   Requires compliance security profile and specific controls like HIPAA or HITRUST.

   #Data Classification#Unity Catalog
   AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP, read it on their docs Jun 23, 2026
5. 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.

   #Data Engineering
   AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP, read it on their docs Jun 12, 2026
6. Converting a partitioned table to liquid clustering is generally available

   Uses ALTER TABLE command, requires Databricks Runtime 18.1 or above.

   #Data Engineering
   AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP, read it on their docs Jun 12, 2026
7. Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs

Headlines, dates and product areas are Databricks' own, and every item links to the note it came from. The one-line summaries are ours.