subject: OLAP servers simplifying data processing [print this page] OLAP servers simplifying data processing OLAP servers simplifying data processing
Online Analytical Processing or better known as OLAP is a system of giving the end users a rapid access to a huge amount of data. OLAP Server uses data which are of multidimensional data representations to help the access to data that is kept in data warehoused. The multi dimensional data representations also known as cubes arrange data in the dimension and table the fact in a data warehouse with the intentions to give high end query user the analysis features top client. These servers give the users real-time data analysis characteristics to client applications. The software which are used in these OLAP servers gives the users a real time data analysis chance for information stored in warehouses.
An OLAP server primarily takes its role as a different component and stores indexing tools and algorithms to assist quick processing of data mining tasks without affecting hampering database performance. It is very important to understand that OLAP is a very important part of any business as it helps in the analysis and in the process of decision making. The IT sector which are often having problems related to the systems very much depend on correct information on decision making.
OLAP server primarily helps in the process of data analysis from different business dimensions, giving support to high-end analysis needs for effective decision making and over that assisting complicated analysis.
OLAP Server is based on two kinds of structure. They are Relational OLAP and Multi -dimensional OLAP accesses data from the warehouses directly and MOLAP executes a multi dimensional database for providing analysis .ROLAP is not in any way affected by the huge amounts of data stored on the database and gives the opportunity to the system designer regarding performance while MOLAP requires pre-compilations of databases so as to increase batch performance. ROLAP is better for dynamic consolidation for decision support analysis and MOLAP suits consolidation of data in batch. Compared to MOLAP, ROLAP can be used for a business dimensions and it has better support for OLAP analysis for the huge amounts of atomic level data, at the same time MOLAP delivers performance when data is less. Considering the end user requirement the server is important for staging multi dimensional data.
