February 2026 platform release notes
These features and SAP Databricks platform improvements were released in February 2026. The cloud the release applies to is indicated for each release note.
Releases are staged. Your SAP Databricks account might not be updated until a week or more after the initial release date.
ISMAP compliance support
February 26, 2026 | Applies to: 
AWS Azure
You can now configure SAP Databricks workspaces to support ISMAP, the Japanese government certification initiative for cloud services.
Python unit testing in the workspace
February 26, 2026 | Applies to: AWS GCP Azure
SAP Databricks now provides integrated Python unit testing tools in the workspace. Use the tests sidebar, inline execution glyphs, and testing results bottom panel tab to discover, run, and debug pytest-based tests.
Serverless outbound IPs available through public JSON endpoint (Preview)
February 25, 2026 | Applies to: AWS
You can now retrieve serverless outbound IP addresses from a public JSON endpoint.
SAP Databricks ODBC Driver renamed from Simba Spark ODBC Driver
February 25, 2026 | Applies to: AWS GCP Azure
The SAP Databricks ODBC driver has been renamed from Simba Spark ODBC Driver to SAP Databricks ODBC Driver. Existing Simba driver versions remain supported for two years.
Personal access tokens preserved when CAN USE permission is revoked
February 23, 2026 | Applies to: AWS GCP Azure
When you revoke a user's CAN USE permission, their personal access tokens become unusable but are not deleted. If the permission is restored, the same tokens become active again.
ADBC driver is now the default driver for new Power BI connections
February 20, 2026 | Applies to: AWS GCP Azure
New connections created in Power BI Desktop or Power BI Service now automatically use the Arrow Database Connectivity (ADBC) driver by default. Existing connections continue to use ODBC unless you manually update them to ADBC. You can still switch to ODBC drivers for new connections.
Anthropic Claude Sonnet 4.6 now available as a SAP Databricks-hosted model
February 17, 2026 | Applies to: AWS GCP Azure
Mosaic AI Model Serving now supports Anthropic Claude Sonnet 4.6 as a SAP Databricks-hosted model. You can access this model using Foundation Model APIs pay-per-token.
Qwen3-Embedding-0.6B now available in Preview as a SAP Databricks-hosted model
February 17, 2026 | Applies to: AWS GCP Azure
Mosaic AI Model Serving now supports Qwen3-Embedding-0.6B in Preview as a SAP Databricks-hosted model. You can access this model using Foundation Model APIs pay-per-token.
Query tags for SQL warehouses (Preview)
February 6, 2026 | Applies to: AWS GCP Azure
You can now apply custom key-value tags to SQL workloads on SAP Databricks SQL warehouses for grouping, filtering, and cost attribution. Query tags appear in the system.query.history table and on the Query History page of the SAP Databricks UI, allowing you to attribute warehouse costs by business context and identify sources of long-running queries.
Anthropic Claude Opus 4.6 now available as a SAP Databricks-hosted model
February 5, 2026 | Applies to: AWS GCP Azure
Mosaic AI Model Serving now supports Anthropic Claude Opus 4.6 as a SAP Databricks-hosted model. To access this model, use Foundation Model APIs pay-per-token or batch inference workloads using AI Functions.
Decrypt query history system table fields (Preview)
February 5, 2026 | Applies to: AWS Azure
For workspaces enabled for customer-managed keys (CMK) for managed services, the system catalog's data encryption settings can be configured to decrypt the statement_text and error_message fields in the query history system table.
Select tables and create pivot tables in Google Sheets
February 3, 2026 | Applies to: AWS GCP Azure
You can now directly select SAP Databricks tables from the Catalog Explorer and import data as pivot tables in Google Sheets using the SAP Databricks Connector.
Data Quality Monitoring Anomaly Detection (Preview)
February 2, 2026 | Applies to: AWS GCP Azure
SAP Databricks Data Quality Monitoring Anomaly Detection is now in Preview. The feature is enabled at the schema level and learns from historical data patterns to detect data quality anomalies. The health of all monitored tables is consolidated into a single system table and a new UI.