Generally OLAP cube is a data structure. It engages in fast investigation of data. It is a powerful system for manipulating and analyzing the data from different perspectives.

There are some limitations of relational databases. This is why the data collection was done into cubes. Relational databases are not well equaled with the next data to continuous investigation and display the large amount of data. Whether OLTP (On-Line Transaction Processing) are better for creating reports from a series of data. There are some report making tools available in relational databases but they are not faster as OLTP.

OLAP cubes are similar to the extensions of the two-dimensional array of a table. Let us take one instance a company wants to study some finance record by product, by time-period, by town, by type of income and price, and by comparing finance record with a monetary budget. The additional methods of study the data are known as dimensions. There three or more dimensions can exist in OLAP system so the term hypercube is used.

The OLAP has some numeric data which is called measures. Measures are further classified by the dimensions. The cube organization can be created from the table organization in a relational database. Measures are created from the rows in the data table and we can get dimensions from the dimension tables.

An economics expert may view or he wants to build a pivot table in different traditions, such as displaying all the cities down the page and displaying all the goods across a page. This could be for a particular time, version and price. He may wants to view the data in a different way next time. The cube could fruitfully be changed to the reverse order i.e. time across the page and type of price down the page. Actually this system facilitates re-summarizing of huge facts within very short time. This change of the outlook of data has to be done by using OLAP in order to save the analyst's time.