Databricks releases, every cloud
- New alert editing experience
Replaces old editor with a multi-tab editor for unified workflow.
- Unified runs list (Public Preview)
Combines job and pipeline runs into one list.
- Jobs can now be triggered on source table update
Triggers require Databricks Runtime 7.0 or later.
- Create backfill job runs
Triggers job runs to load past data, useful for repairing processing failures.
- The billable usage table now records the performance mode of serverless jobs and pipelines
Records performance mode in product_features.performance_target column with values PERFORMANCE_OPTIMIZED, STANDARD, or null.
- Visualizations fix
Fixes legend selection for charts with aliased series names in SQL editor and notebooks.
- Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
- Semantic metadata in metric views
Requires YAML 1.1 and Databricks Runtime 17.3 or higher.
- Lakeflow Pipelines Editor is now in public preview
Replaces multi-file editor, edits pipelines as files in asset browser, defaults to Python and SQL code.
- New requirement to create connections for Salesforce ingestion
Requires Databricks connected app installed. Applies to new connections only.
- Databricks SQL version 2025.30 is now available in Preview
Adds LIKE operator support for UTF8 collations and ST_ExteriorRing function.
- Migrate Spark Declarative Pipelines on Lakeflow pipelines from legacy publishing mode is generally available
Replaces legacy mode that only published to a single catalog and schema. Now supports multiple catalogs and schemas.
- Discover files in Auto Loader efficiently using file events without enrollment (Public Preview)
Requires file events enabled on external locations.
- Google Analytics Raw Data connector GA
Enables ingestion of raw Google Analytics data into Databricks, requires Google Analytics 4 and Google BigQuery setup.
- Python custom data sources can be used with Spark Declarative Pipelines on Lakeflow
Supports Python custom data sources and sinks in pipeline definitions.
- Spark Declarative Pipelines on Lakeflow now supports stream progress metrics in Public Preview
Supports querying event log for stream progress metrics.
- Migrate Spark Declarative Pipelines on Lakeflow from legacy publishing mode is rolled back to Public Preview
Reverts migration feature to Public Preview due to issues. Requires re-enabling default publishing mode in pipelines.
- SQL Server connector supports SCD type 2
Maintains a history of changes, rather than overwriting outdated records.
- OLTP Database tab renamed to Lakebase Postgres in Compute section
Used to create and manage Lakebase database instances, replacing OLTP Database tab.
- Get this table by email. One Monday mail covering the week, filtered to the clouds and products you run. Weekly digest
- Admins can now manage a workspace's serverless base environments (Public Preview)
Defines custom environment specs for serverless notebooks, allows setting a default for new notebooks.
- Default warehouse setting is now available in Beta
Applied across SQL editor, AI/BI dashboards, and other tools, with optional user override.
- Lakebase synced tables supports syncing Apache Iceberg and foreign tables in Snapshot mode
Syncs Iceberg and foreign tables in Snapshot mode, requiring a supported Databricks runtime.
- Migrate Spark Declarative Pipelines on Lakeflow from legacy publishing mode is now GA
Replaces legacy single-catalog publishing with multi-catalog support.
- Microsoft SQL Server connector GA
Replaces manual ingestion scripts, requires Lakeflow Connect setup.
- Budget policy support for Lakebase database instances and synced tables (Public Preview)
Tags database instances and synced tables for billing attribution, also supports custom tags for compute usage.
- Data type mapping update for Lakebase synced tables
Newly created synced tables map TIMESTAMP to TIMESTAMP WITH TIMEZONE. Existing tables still use TIMESTAMP WITHOUT TIMEZONE.
- Databricks SQL version 2025.25 is rolling out in Current
Rolls out from August 20th to August 28th, 2025.
- Declarative Automation Bundles support for Lakebase database resources
Defines Lakebase database catalogs, instances, and synced tables for one-command deployment. Starts instances immediately upon bundle deployment.
- Set the run-as user for Spark Declarative Pipelines on Lakeflow
Replaces user accounts with a service principal for automated workloads.
- Databricks SQL version 2025.25 is now available in Preview
Adds recursive common table expressions and spatial SQL expressions.
- Fixed timeout handling for materialized views and streaming tables
New views and tables apply warehouse timeout, existing ones require CREATE OR REFRESH. Default timeout is 2 days.
- New SQL editor is generally available
Provides a unified authoring environment with features like real-time collaboration and enhanced Databricks Assistant integration.
- Spark Declarative Pipelines on Lakeflow template in bundles in the workspace (Public Preview)
Creates ETL pipelines in workspace bundles using Lakeflow template. Requires New ETL pipeline setup.
- Jobs in continuous mode can now have task-level retries for failed tasks
Task-level retries replace job-level retries, require continuous mode.
- Lakebase Public Preview enabled by default
No admin enablement required, can be disabled by admins.
- ServiceNow connector GA
Replaces manual API configuration, requires Lakeflow Connect setup.
- Databricks Terraform provider supports Lakebase resources
Creates database catalogs, instances, and synced tables with infrastructure as code.
- Preset date ranges for parameters in the SQL editor
Includes options like This week, Last 30 days, and Last year for timestamp and date parameters.
- Synced tables are now metered and billed
Metering is automatic, usage is tracked in system.billing.usage.
- Jobs & Pipelines list now includes Databricks SQL pipelines
Includes materialized views and streaming tables created with Databricks SQL.
- Inline execution history in SQL editor
Shows past results without re-executing queries, links to past query profiles.
Headlines, dates and product areas are Databricks' own, and every item links to the note it came from. The one-line summaries are ours.