A multidimensional analytics structure that pre-aggregates measures across many dimensions — designed for fast slice-and-dice queries that would be slow against raw fact tables.
OLAP Cubes store summarized data across multiple dimensions (sales by product × region × month × channel) in a structure optimized for analytical queries. Pre-aggregation makes 'what were Q3 sales of category X in region Y?' queries return in milliseconds vs seconds-to-minutes against raw tables. The classic cube tools (SSAS, Essbase, Mondrian) have largely been supplanted by columnar warehouses + semantic layer tools (Cube.dev, Looker LookML, dbt Metrics) that achieve similar speed without explicit cube building.
Defining a semantic layer in Cube.dev that aggregates fact_orders across customer, product, time, and channel dimensions — sub-second BI queries.
Cube concepts (measures, dimensions, pre-aggregation) remain foundational even as implementation has shifted from dedicated cube engines to warehouse-native semantic layers.
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