Python Analysis in Cube

Cube has introduced Python analysis capabilities, which enhance its existing semantic layer by enabling advanced modeling tasks that go beyond SQL's aggregation capabilities. Traditionally, complex analyses such as forecasting, regression, and clustering required exporting data to a notebook for processing, but with Python analysis, users can attach scripts directly to workbook tabs in Cube. This approach ensures that analyses are performed on consistent, governed data and can be saved, re-run, and shared easily. Users can initiate Python-based analyses through Analytics Chat by requesting specific tasks like forecasting, which are executed when they require statistical or machine-learning libraries. While SQL remains the tool for standard aggregations and time series, Python is employed for more complex analyses, with results rendered directly in the platform. Users can manage scripts within Cube, ensuring transparency and ease of modification, with popular Python libraries preinstalled, and the option to request demos for new users.

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