Lakehouse Studio Release Notes — July 8, 2026 (v2.3.0)

This release (Release 2026.07.08, v2.3) introduces a range of new features, enhancements, and fixes. Please note that these updates will be rolled out in stages to the regions listed below. The rollout will complete within one to two weeks of the release date, depending on your region.

• Mainland China Regions

  • Alibaba Cloud (Shanghai)
  • Tencent Cloud (Shanghai / Beijing / Guangzhou)
  • AWS (Beijing)
  • Alibaba Cloud (Hangzhou)

• International Regions

  • Alibaba Cloud (Singapore)
  • AWS (Singapore)

Breaking Changes

• None

New Features

Data Integration

• Real-time sync now supports GaussDB as a data source for ingestion into Lakehouse, with full coverage across data source onboarding, the Rocket engine, FlinkX components, and front-end configuration.
• Fixed an issue where the incremental state displayed incorrectly after a multi-table full real-time sync failed.
• Multi-table offline integration mirror mode now supports custom fields, allowing custom fields to receive parameters passed from upstream and improving flexible configuration in multi-table scenarios.
• Offline sync preview now supports input parameter format parsing, making it easier to debug and validate parameters.

Task Development

• Notebook now supports scheduling, including scheduling configuration, scheduled execution, and scheduled instance runs, with the full front-end configuration flow connected end-to-end.
• Notebook supports importing and exporting .ipynb format files, enabling seamless interoperability with external notebook toolchains such as Jupyter.
• Spark nodes now support visual code editing and submission, along with a quick migration path for legacy tasks, significantly improving the Spark task development experience.

Global Features

• The account registration page now shows real-time prompts and validation for username and password input rules, improving registration flow while enhancing account security.

Data Sources

• Improved the form hint interaction for the Databricks data source type, making configuration easier to use.

Bug Fixes

• Data Sources: Fixed an issue where a newly created data source passed connectivity checks, but selecting it in an offline task prompted a data source detection failure.
• Data Integration: Fixed an issue where syncing an Oracle data source to Lakehouse caused TIMESTAMP(6) WITH LOCAL TIME ZONE to map to timestamp_ltz with an error indicating the mapping could not be performed automatically.
• Data Integration: Fixed an issue in offline sync where writing from Lakehouse to a Redis data source with the list write type showed a successful run and data was readable, but nothing was actually written and no dirty data was recorded.
• Data Integration: Fixed a precision loss issue when writing Uint64 data from ClickHouse to Lakehouse.
• Data Integration: Fixed an issue where data continued to be written in real time after pausing a single table in a real-time sync job.
• Task Development: Fixed an issue with clicking Run on a Spark sub-task type inside a composite task.
• Task Development: Fixed the impact of external system exceptions on the scheduling system task execution pipeline.
• Task Development: Fixed an issue where opening and then exiting an SQL-type sub-task inside a composite task caused an error.
• Task Development: Fixed incorrect parameter display after switching composite nodes, where the same task group parameters were shown incorrectly.
• Data Quality: Fixed an issue where the scheduled time configured for a quality rule task did not take effect.
• Operations Center: Fixed an issue where downstream instance previews failed after an upstream failure.
• Global Features: Fixed an issue where the default schema configured under a workspace did not take effect.

Known Limitations

• None