OLAP Server or ONLINE ANALYTICAL PROCESSING SERVER to define in a very simple language is an engine which is enabled with the capacity to manage and store data in a very systematic manner. This technique helps in quick processing of the queries through the format of analysis which is multi dimensional in nature. Data mining and relational reporting is also encompassed with OLAP server or ONLINE ANALYTICAL PROCESSING SERVER, which can further falls in the category of intelligent management of the business. OLAP server or ONLINE ANALYTICAL PROCESSING SERVER is therefore included in variety of business application like sales reporting in business, preparing management reports, economic report of the business, budgeting and forecasting the process management of the business. Further new zones are explored regarding its application in areas like weather, agro based products etc.
The OLAP server or ONLINE ANALYTICAL PROCESSING SERVER is developed from the traditional idea of OLTP, to specify Online Transaction Processing. The tasks which are assigned for the OLAP users follow the model of multi dimensional data storage which allows complex analysis and processing of all the queries in a lightening speed. For this they use hierarchical database and navigational database which have better speed than the relational database. The matrix format is used to display the results of the processed queries if the OLAP server or ONLINE ANALYTICAL PROCESSING SERVER, the columns and rows take the form of the dimensions of the matrix while the values are formed of the measures of the matrix.
The idea of the hypercube or multi dimensional cube is the catalyst in developing the idea of OLAP Server or ONLINE ANALYTICAL PROCESSING SERVER. Is basically consists of numeric facts better known as measures and better categorized as dimensions. The relational database has tables of snowflake schemas or star schemas which is behind the typical creation of the cube metadata. Fact tables help in deriving the measures while the dimension table helps in deriving the dimensions. An easy instance would be a store cube with measures as sales and dimension in the form of date and time.