Database Management System
OLAP (On-Line Analytical Processing)

The main reason of using OLAP to answer queries is speed. Relational databases store entities in discrete tables if they have been properly normalized. This structure is good for operational databases but for complex multi-table queries is comparatively slow. A better model for querying but worse for operational use is the dimensional database. OLAP take a snapshot of a relational database and restructures it into dimensional data. The queries can then be run against this. It has been claimed that for complex queries OLAP can produce a result in around 0.1% of the time for the same query on relational data.

For example a set of customers can be grouped by city, by district or by country; so with 50 cities, 8 districts and two countries there are three hierarchical levels with 60 members. These customers can be considered in relation to products; if there are 250 products with 20 categories, three families and three departments then there are 276 product members. With just these two dimensions there are 16,560 possible aggregations. As the data considered increases the number of aggregations can quickly total tens of millions or more.

The second terminology that I research was Data Warehouse and Data Mart. Data Warehouse and Data Mart is corporate memory. Let's take this other terminology and break it in two part; one Data Warehouse and the second one Data Mart. Starting by Data Warehouse, academics will say it is a subject oriented, point-in-time, inquiry only collection of operational data. And what this means is the typical relational databases are designed for On-Line Transactional Processing (OLTP) and do not meet the requirements for effective On-Line Analytical Processing (OLAP). As a result data warehouses are designed differently that traditional relational databases.

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