October 30, 2025 — 1.3 Lakehouse Platform Product Update Release Notes

This release (Release 2025.10.30) introduces a series of new features, enhancements, and bug fixes. These updates will be rolled out gradually to the following regions, expected to complete within one to two weeks from the release date. The exact timing depends on your region.

Domestic Regions

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

International Regions

Alibaba Cloud (Singapore)
AWS (Singapore)

SQL-Related Updates:

Incremental Computing Capability Enhancement:

Dynamic Table now supports refreshing by any partition, improving the flexibility and efficiency of data processing. Reference Link
Added undefined Data Type Support:

Now fully supports the BITMAP data type. This enables significant performance improvements and storage optimization when performing precise deduplication on large-scale datasets (such as UV statistics) and user profiling analysis (such as audience segmentation). Reference Link

3. New Built-in Functions

trans_array: Used to efficiently process nested arrays, replacing the complex split + explode operations and simplifying data processing logic. Reference Link
regexp_instr: New regular expression matching function for finding the position of a pattern in a string, Spark-compatible. Reference Link
regexp_count: New regular expression counting function for counting the number of occurrences of a pattern in a string. Reference Link

4. Syntax and DDL/DML Enhancement

QUALIFY: Used to filter the results of window functions in a SELECT statement. Reference Link
Command Functionality Extension:
- DESC CATALOG output now includes the connection_name field for easier identification and management.
- DESC FUNCTION now supports displaying the function's comment, improving readability and usability.
Function Extension: The EXTRACT function now supports the QUARTER time part, making it easier to perform quarterly date and time analysis.
SHOW Command Enhancement: For PIPE objects, supports SHOW PIPES IN WORKSPACE <workspace_name> to list all PIPE objects in the current workspace.

AI Feature Updates:

[Invite-Only] Semantic View: Officially launched the "Semantic View" creation feature. Semantic View is designed to encapsulate complex underlying physical data models using business-user-friendly semantic concepts such as dimensions and metrics. This feature is currently in the invite-only phase.
Lakehouse MCP Server Enhancement:

To improve the development and debugging efficiency of AI-related features, we have expanded the number of local MCP Server tools to 63. This allows users to interact with and manage the Lakehouse more efficiently and comprehensively when connecting via an MCP client. Reference Link

Data Openness:

Comprehensive Enhancement of Iceberg Ecosystem Bidirectional Integration: This update comprehensively opens up connectivity with the external Iceberg ecosystem, greatly enhancing the platform's openness and data federation capabilities.

Exposing Data to External Systems, Providing Standard Iceberg REST Catalog Service: Lakehouse now provides standard Apache Iceberg Catalog REST API interfaces. This means external compute engines (such as Apache Spark, etc.) can directly access and query Iceberg tables stored in Lakehouse through this industry-standard protocol. Users can maintain unified data storage while flexibly choosing different compute engines for data analysis. Reference Link
Accessing External Data, Supporting Iceberg REST-Based External Catalog Access: Through the External Catalog integration feature, Lakehouse can now connect to and access Iceberg data tables from services such as Snowflake OpenCatalog. Users can seamlessly query and analyze data stored in the Snowflake ecosystem within Lakehouse, enabling cross-cloud, cross-platform data federation queries. Reference Link

Feature Improvements:

Data Import: Command Functionality Enhancement

The COPY OVERWRITE command now supports outputting query results directly into a single file. This simplifies the data export process and is especially suitable for scenarios where analysis results need to be packaged, archived, or delivered. Reference Link
User Self-Service Management and Workspace

BYOS Self-Service Configuration: For BYOS (Bring Your Own Storage) scenarios, a self-service whitelist configuration process has been launched, simplifying user onboarding and management costs. Reference Link