Databricks releases, every cloud
- Meta Ads connector (Beta)
Ingests data from Meta Ads, requires setup as a data source.
- MySQL connector in Lakeflow Connect (Public Preview)
Enables incremental data ingestion from various MySQL databases. Requires access request through account team.
- Updated Lakeflow Jobs UI is generally available
Simplifies job creation and editing, see Lakeflow Jobs for details.
- Confluence connector (Beta)
Ingests Confluence spaces and pages into Databricks, requires OAuth U2M configuration.
- PostgreSQL connector in Lakeflow Connect (Public Preview)
Enables incremental data ingestion from various PostgreSQL databases. Requires configuration.
- Change owner for materialized views or streaming tables defined in Databricks SQL
Done through Catalog Explorer, replacing previous methods.
- Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
- Customizable SharePoint connector (Beta)
Ingests structured, semi-structured, and unstructured files into Delta tables with customizable schema inference and parsing options.
- Discover files in Auto Loader efficiently using file events
Requires file events enabled on external locations. Replaces directory listing for efficiency.
- Jira connector (Beta)
Ingests Jira issues, comments, and attachments metadata into Databricks.
- Microsoft Dynamics 365 connector (Public Preview)
Ingests data from Dynamics 365 apps into Databricks. Requires Lakeflow Connect.
- NetSuite connector (Public Preview)
Ingests data from NetSuite2.com via API, CLI, or notebook.
- ForEachBatch for Spark Declarative Pipelines on Lakeflow is available (Public Preview)
Allows processing streams as micro-batches in Python. Requires Lakeflow setup.
- Spark Declarative Pipelines on Lakeflow stream progress metrics are generally available
Supports querying event log for stream progress metrics.
- Configure full refresh behavior for managed database connectors
Applies to Lakeflow Connect managed database connectors, allowing scheduled full refresh snapshots and automatic recovery from unsupported schema changes.
- Jobs can now be triggered on source view update
Supports views referencing supported table types, see documentation for specifics.
- Improved full refresh flow for managed database connectors
Reduces downtime by delaying table refresh until snapshot completion, minimizing PENDING_RESET errors.
- SFTP connector (Public Preview)
Extends Auto Loader to ingest files from SFTP servers.
- Lakeflow Declarative Pipelines has been renamed to Lakeflow Spark Declarative Pipelines
Replaces Lakeflow Declarative Pipelines, see Spark Declarative Pipelines for details.
- Get this table by email. One Monday mail covering the week, filtered to the clouds and products you run. Weekly digest
- List MCP servers in Azure Databricks Marketplace (Public Preview)
Allows distribution of AI tools and connection to external data sources, requires provider listing.
- List MCP servers in Databricks Marketplace (Public Preview)
Lets users install MCP servers to connect AI agents to external data sources.
- JAR tasks are now supported on serverless compute
Run JAR jobs without a cluster.
- Salesforce ingestion connector supports history tracking (SCD type 2)
Enables history tracking, overwriting default SCD type 1 behavior.
- Zerobus Ingest connector in Lakeflow Connect (Public Preview)
Enables record-by-record ingestion into Delta tables via gRPC API.
- 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.
- 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.
- 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.
- 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.
- Set the run-as user for Spark Declarative Pipelines on Lakeflow
Replaces user accounts with a service principal for automated workloads.
- 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.
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