Analysis Services in High Data Volume Business scenarios
Executive Summary
The realization of the importance of data warehouses coupled with ever decreasing price of hardware and advanced computing options has resulted in datawarehouses of thousands of gigabytes over the last few years. This in turn has paved way for OLAP solutions that store detail and aggregate data for optimally answering complex business queries related to multi-dimensional data. However, with increasing data volumes and complexity of business requirements, OLAP solutions are also challenged to meet the processing and reporting SLA’s defined by business. Parallel Processing of OLAP cubes is a very powerful option for meeting such performance and timeline demands. This white paper is an initiative to understand the benefits Parallel Processing Utility provides in processing large Analysis Services 2000 cubes in high volume business scenarios. The paper also explains how improvements in Analysis Services 2005 ensure that Parallel Processing is inbuilt in the upgraded version and there is no requirement for Parallel Processing Utility in 2005.
Introduction
This white paper explains the benefits derived by use of Parallel Processing utility particularly where large volumes of data need to be processed as part of the overnight batch process. It explains the underlying data processing and storage needs in large businesses across domains with the implications of impact on overnight batch loads. The complex data processing requirements in terms of data volumes and business logic have implications on the OLAP and relational database design and in the use of optimal processing methods whereby over night batch processes can meet the SLA(service level agreement) and also address the business needs for large complex data sets. This white paper addresses how Parallel Processing Utility in Analysis Services plays a stellar role in addressing such requirements.
Tags: performance