Summary: Online Analytical Processing is a methodology used to provide end users with access to large amounts of data in a rapid manner to assist with deductions based on investigative reasoning. OLAP uses multidimensional data representations, known as cubes to provide rapid access to data stored in data warehouses. In a data warehouse, cubes model data in the dimension and fact tables in order to provide sophisticated query and analysis capabilities to client applications. The software used in OLAP offers real-time analysis of data stored in a data warehouse. Generally, the OLAP server is a separate component that contains specialized algorithms and indexing tools that enable the processing of data mining tasks with minimal impact on database performance.
Online analytical processing is an integral part of businesses. It helps in the analysis and decision-making of an organization. For example, IT organizations often face the challenge of delivering systems that allow knowledge workers to make strategic and tactical decisions based on corporate information. These decision support systems are the OLAP systems that allow knowledge workers to intuitively, quickly and flexibly manipulate operational issues to provide analytical insight. Usually, OLAP systems are designed to:
- Support the complex analysis requirements of decision-makers.
- Analyze the data from a number of different perspectives (business dimensions).
- Support complex analysis against large input (atomic-level) data sets.
OLAP systems are generally designed based on two architectures- multidimensional OLAP (MOLAP) and relational OLAP (ROLAP). The MOLAP architecture utilizes a multidimensional database to provide analysis, while the ROLAP architecture access data directly from data warehouses. According to MOLAP architects OLAP is best implemented by storing data multi-dimensionally, whereas ROLAP architects like to believe that OLAP capabilities are best provided directly against the relational database. If we compare these two architectures of OLAP, we would come clear with that:
- Since ROLAP architecture is neutral to the amount of aggregation on the database, it leaves the design trade-off between query response time and batch processing requirements to the system designer. But MOLAP usually requires the databases to be pre-compiled in order to provide acceptable query performance in order to increase the batch processing requirements.
- ROLAP is suitable for dynamic consolidation of data for decision support analysis, while MOLAP is often favored for batch consolidation of data.
- ROLAP can scale to a large number of business analysis perspectives or dimensions, while MOLAP can generally perform efficiently with ten or fewer dimensions.
- ROLAP supports OLAP analysis against large volumes of input (atomic-level) data. But, MOLAP provides adequate performance only when the input data set is small (fewer than five gigabytes).
Online Analytical Processing is an interactive instrument for the analytic processing and data-recall facility in large databases. It allows rapid access to performance data from different viewpoints, to assist business analysts and managers throughout an enterprise.
This article was written by Brian May who has worked with companies that offer data warehousing consulting. He truly understands the value that a data warehousing can offer.
Article Source: http://EzineArticles.com/?expert=Brian_May
No comments:
Post a Comment