Databricks releases, every cloud
| Release | Date | ||||
|---|---|---|---|---|---|
| Native data profiling for results tables in the SQL editor Select column headers and click Open selection details to view statistics. | On AWS, read it on their docs | On Azure, read it on their docs | On GCP, read it on their docs | Not on SAP | May 1, 2026 |
| agg SQL function as a synonym for measure Replaces measure, used as agg(measure_column) in metric views. | On AWS, read it on their docs | On Azure, read it on their docs | On GCP, read it on their docs | Not on SAP | Apr 30, 2026 |
| Databricks Runtime maintenance updates (04/29) Includes bug fixes, security patches, and performance improvements for LTS versions 13.3 to 16.4. | On AWS, read it on their docs | On Azure, read it on their docs | On GCP, read it on their docs | Not on SAP | Apr 29, 2026 |
| 5X-Large SQL warehouse size (Public Preview) Provides 512 workers, available in all regions, controlled from Previews page. | On AWS, read it on their docs | On Azure, read it on their docs | On GCP, read it on their docs | Not on SAP | Apr 28, 2026 |
- Native data profiling for results tables in the SQL editor
Select column headers and click Open selection details to view statistics.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - agg SQL function as a synonym for measure
Replaces measure, used as agg(measure_column) in metric views.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - Databricks Runtime maintenance updates (04/29)
Includes bug fixes, security patches, and performance improvements for LTS versions 13.3 to 16.4.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud) - 5X-Large SQL warehouse size (Public Preview)
Provides 512 workers, available in all regions, controlled from Previews page.
AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
Headlines, dates and product areas are Databricks' own, and every item links to the note it came from. The one-line summaries are ours.