OLAPcube

An OLAP cube is a data structure designed to support multidimensional analysis of business data. It is used in data warehouses and data marts to enable fast, interactive queries across multiple dimensions, such as time, geography, product, and customer, while summarizing numerical measures such as sales revenue and quantity sold.

Data in an OLAP cube is organized into dimensions and measures. Dimensions provide the axes of analysis and often include hierarchies (for example Year > Quarter > Month or Country > State > City). Measures are numeric facts that can be aggregated across dimensions. The cube stores pre-aggregated values at different levels of granularity to support efficient drill-down and roll-up.

Core operations include slicing, dicing, roll-up, drill-down, and pivot. Slicing filters the cube along one dimension, dicing selects a subcube with multiple dimension values, roll-up aggregates data to a higher level in a hierarchy, drill-down moves to a more detailed level, and pivot rotates the cube axes to view data from different perspectives.

Architectures and storage models vary. MOLAP stores data in a specialized multidimensional store; ROLAP relies on relational databases with on-the-fly aggregation; HOLAP combines both approaches. Cubes may be pre-aggregated for speed or computed dynamically to reflect the latest data. The MDX query language is commonly used to retrieve data from OLAP cubes, though SQL-based querying is also common.

OLAP cubes enable rapid, exploratory analysis for activities such as sales forecasting, budgeting, and performance management. They are most effective when the underlying data is stable and well modeled into facts and dimensions. Limitations include storage cost, maintenance effort, and potential data staleness; modern BI environments frequently complement or replace traditional cubes with in-memory, columnar, or federated analytics approaches.