You can now organize workbooks into folders to better structure your analytics content. Grant permissions at the folder level, and all workbooks inside will automatically inherit these permissions, making it easier to manage access across related content.
This feature improves content organization and discovery, and represents our first step toward building a robust analytics content discovery platform.
You can now ask the AI agent to create a dashboard directly from Analytics Chat conversations. Your entire conversation can be turned into an interactive dashboard, making it easy to convert your data discussions into shareable analytics content. This feature requires access to workbooks.
The Model Context Protocol (MCP) enables AI applications to securely access external data sources and tools. Now users can connect AI assistants like Claude to the Cube MCP server, providing a way to explore your data through natural language.
Cube MCP server is compatible with all modern AI clients that support MCP: Claude, ChatGPT, Cursor, Codex, and more. Users can configure what deployments and agents to use for MCP server.
Navigate to Admin → MCP Server to get started.
You can set up Cube dashboards to automatically update on a schedule. When a scheduled refresh is triggered, the widgets on the dashboard are queried in the background, refreshing Cube's in-memory cache. This warms up dashboards so they load instantly when users open them, eliminating wait times for data to load.
Access scheduled refreshes from the dashboard builder or published dashboard by clicking the calendar icon, which opens the scheduled refreshes sidebar. From this sidebar, you can create new schedules, modify existing ones, or remove schedules you no longer need.
Cube now supports bi-directional integration with Snowflake Semantic Views. This integration enables you to author views in Cube and use them in Snowflake, or work with Snowflake semantic views directly in Cube. This ensures consistency between your Cube definitions and Snowflake's semantic layer, allowing teams to work in their preferred environment.
From the IDE, users can pull semantic views from Snowflake and turn them into cubes and views in Cube. The pull integration generates code files with cube and view definitions in your Cube repository, making it easy to work with existing Snowflake semantic views. Alternatively, you can push Cube views into Snowflake as native semantic views. The push integration creates DDL from Cube's definitions and executes it in Snowflake, creating Snowflake Semantic Views that match your Cube schema.