OLAP system

The core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures which are categorized by dimensions. The cube metadata is typically created from a star schema or snowflake schema of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.
Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure.
A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale.
Any number of dimensions can be added to the structure such as Store, Cashier, or Customer by adding a foreign key column to the fact table. This allows an analyst to view the measures along any combination of the dimensions

Multidimensional databases

Multidimensional structure is defined as “a variation of the relational model that uses multidimensional structures to organize
data and express the relationships between data”.The structure is broken into cubes and the cubes are able to store and access data
within the confines of each cube. Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions.
Even when data is manipulated it is still easy to access as well as be a compact type of database.
The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications (O’Brien & Marakas, 2009). Analytical databases use these databases because of their ability to deliver answers swiftly to complex business queries. Data can be seen from different ways, which gives a broader picture of a problem unlike other models