OLAP stands for OnLine Analytical Processing and is a technology used to collect, manage and process multidimensional data and provide fast access to this data for analytic purposes.
OLAP is widely used in business reporting for marketing, sales, human resource management and various other business fields.
OLAP allows for rapid execution of complex database queries in real time. OLAP facilitates complex data views through data pivoting, complex data computations, and data modeling.
OLAP deals with dimensional data, which allows for much faster execution of complex database queries compared to relational database management systems. OLAP gets a snapshot of a relational database management system data and then re-composes this data into multidimensional data.
The data structure that OLAP create from the relational data is called OLAP cube. OLAP cubes can be thought of as multi dimensional array. A business might want to analyze its sales data by product, by product category, by sales manager, or something else. These different analyzing criterions are the OLAP cube dimensions.
The OLAP cube structure consists of one central table called facts table, surrounded by dimensional tables - one table for each dimension of the cube. The facts table holds facts and metrics about the business process. The facts table also links to each of the dimensional tables.
Each dimensional table consists of columns called dimensional attributes. If you use the example given above, the product dimensional table will have several columns (dimensional attributes) like retail price, wholesale price, weight, height, width, depth, etc.
The dimension tables store data associated with particular dimension, but donít store facts. All facts are stored in the OLAP facts tables.
Some of the most popular OLAP products are Microsoft Analysis Services (part of SQL Server), DB2 Cube Views from IBM, SAP BW, and Essbase.