Methodology to minimize cost and time for new data warehouse implementations

Abstract

A data warehouse's many different components make setting one up a non-trivial and expensive task (R.K. 98). This thesis describes a new light-weight data warehouse implementation methodology that minimizes cost and production time while ensuring the core requirements of a typical data warehouse system are included. The methodology enables fast initial development with scalable architecture for future growth. From a maintenance perspective, the methodology minimizes the ongoing data refresh cost. This is important because data refresh cost is the major maintenance cost in a typical data warehouse system. The data quality is also improved by using the methodology. The above advantages are due to the small team structure, single database architecture, metadata approach, data modeling approach, Extract Transformation and Loading (ETL) approach, tools selection and web portal presented in the methodology.