Hybrid methodology for effective maintenance of data warehouse

Abstract

Data Warehousing has been a buzz word in the industry. Researches have been constantly involved in finding new ways of designing and developing Data Warehousing Architectures, algorithms and tools for bringing together some selected data from multiple heterogeneous databases into a single repository. The real work of taking output from the data warehouse depends largely on how it is managed. Although a lot of research is going on to enhance the design and development of Data warehouse, Very little effort has been spent on the maintenance side. Without proper maintenance data warehouse is not going to give the desired output which is expected of it. In this work a comparison of various architectures of Data Warehousing system, based on analyzed on concepts like wrapper, monitor, integrator, metadata, data quantity indicator is considered. A major reason for data warehouse project failures is poor maintenance. Without proper maintenance desired results are nearly impossible to attain from a data warehouse. Unlike operational systems data warehouses need a lot more maintenance and a support team of qualified professionals is needed to take care of the issues that arise after its deployment including data extraction , data loading, network management, training and communication, query management and some other related tasks.