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An OLAP cube is a multi-dimensional array of data. [1] Online analytical processing (OLAP) [ 2 ] is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three.
The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging. The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database.
Cubes is a light-weight open source multidimensional modelling and OLAP toolkit for development reporting applications and browsing of aggregated data written in Python programming language released under the MIT License.
Feeding cubes – star schemas are used by all OLAP systems to build proprietary OLAP cubes efficiently; in fact, most major OLAP systems provide a ROLAP mode of operation which can use a star schema directly as a source without building a proprietary cube structure.
Multidimensional Expressions (MDX) is a query language for online analytical processing (OLAP) using a database management system. Much like SQL, it is a query language for OLAP cubes. [1] It is also a calculation language, with syntax similar to spreadsheet formulae.
In computer programming contexts, a data cube (or datacube) is a multi-dimensional ("n-D") array of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data.
Excel pivot tables include the feature to directly query an online analytical processing (OLAP) server for retrieving data instead of getting the data from an Excel spreadsheet. On this configuration, a pivot table is a simple client of an OLAP server.
A dimension table in an OLAP cube with a star schema. A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time. [1] [2] (Note: People and time sometimes are not modeled as dimensions.)