Olap Server: Supporting Multi-dimensional Data Structures
OLAP is an abbreviation for the Online Analytical Processing
, with which users can access quickly to vast amounts of data. The data is stored in data warehouses and is accessed by the end uses through multidimensional data representations that are used by OLAP Servers. These multidimensional data representations are also known as cubes. The arrangement of data in a data warehouse is done in the form of the dimension and fact tables by these cubes. This is because to assist the client applications with high-end query and analysis features. These servers use the software that provides the users with real-time data analysis capabilities for information which is stored in warehouses.
An OLAP is a separate component that includes indexing tools and algorithms to provide the end-uses with fast processing of data mining tasks without affecting database performance. Whether it is a decision making process or analysis, OLAP works well for both bringing the business at the new height of success. These servers are primarily seen in IT organizations where problems related to systems are generally found. The accurate information on decision making regarding various domains and platforms is the main part these systems. These decision support systems are counted under OLAP systems through which this kind of information is delivered in order to help decision makers when they need help in executing the right course of action for improving the condition of the organization. These OLAP servers are helpful in these conditions like:
-If you are going to support complicated analysis using atomic level data sets.
-If there is a need of supporting high-end analysis in order to polish the effective decision making.
-If there are different business dimensions and you want to data analysis from them.
There are two kinds of architectures called Multidimensional OLAP (MOLAP) and
Relational OLAP (ROLAP) on which OLAP Servers / systems are based. MOLAP uses multidimensional database for giving analysis, while ROLAP has the ability to access data from the warehouses directly. MOLAP works wells for batch consolidation of data and ROLAP is considered to be a suitable option for dynamic consolidation for decision support analysis.