quotHYBRID METHODOLOGY FOR EFFECTIVE MAINTENANCE OF DATA WAREHOUSEquot

Abstract

quotData Warehousing has been a buzz word in the industry. Researches newlinehave been constantly involved in finding new ways of designing and newlinedeveloping Data Warehousing Architectures, algorithms and tools for newlinebringing together some selected data from multiple heterogeneous databases newlineinto a single repository. The real work of taking output from the data newlinewarehouse depends largely on how it is managed. Although a lot of research newlineis going on to enhance the design and development of Data warehouse, Very newlinelittle effort has been spent on the maintenance side. Without proper newlinemaintenance data warehouse is not going to give the desired output which is newlineexpected of it. In this work a comparison of various architectures of Data newlineWarehousing system, based on analyzed on concepts like wrapper, monitor, newlineintegrator, metadata, data quantity indicator is considered. newlineA major reason for data warehouse project failures is poor newlinemaintenance. Without proper maintenance desired results are nearly newlineimpossible to attain from a data warehouse. Unlike operational systems newlinedata warehouses need a lot more maintenance and a support team of newlinequalified professionals is needed to take care of the issues that arise newlineafter its deployment including data extraction , data loading, network newlinemanagement, training and communication, query management and some newlineother related tasks. newlinevquot newline newline