OLAP is an acronym for On-Line Analytical Processing, it is a powerful technology behind many Business Intelligence (BI) applications such as business process management, planning, financial reporting, complex analytical calculations, data mining, budgeting, forecasting, and more. It processes multidimensional data and allows users to view data from multiple perspectives. The essence of OLAP is that it stores data in multiple dimensions rather than tabular relations found in relational databases, which enables users to analyze data from any point of view. Data is collected from multiple data sources and organized in the form of cubes.

An OLAP cube is a structured data set containing multiple Dimensions and Measures. A dimension is a way to categorize facts. A multidimensional cube for reporting sales might be, for example, composed of 7 dimensions: salesperson, sales amount, region, product, region, month, year. Measures represent values that are counted, summarized, or aggregated, such as costs and the number of units sold, etc. The multidimensional structure of an OLAP cube makes it easy to visualize or conceptualize data along the various dimensions. The pre-calculated and pre-aggregated data in the cube makes analysis quicker.

Basic types of OLAP systems

• ROLAP stands for Relational OLAP, they work mainly for the data that resides in a relational database, where the base data and dimension tables are stored as relational tables. This type of storage is excellent for organizations that need larger data warehousing. 
  

• MOLAP stands for Multidimensional OLAP, broadly known as the classic OLAP type. MOLAP utilizes a multidimensional database for storage and analyzing information. This is the fastest option for data retrieval but also requires the most storage space. MOLAP is optimized for fast query performance and retrieval of summarized data.
  

• HOLAP stands for Hybrid OLAP. It's a blend of MOLAP and ROLAP. By utilizing both ROLAP and MOLAP information stores, Hybrid OLAP offers the qualities of both techniques. HOLAP stores data summaries in the binary files or in the pre-calculated cubes. It leaves the quantities of fact and dimension information in the relational database.