Using a common set of attributes to determine which methodology to use in a particular data warehousing project . A Comparison of Data Warehousing Methodologies

79 Using a common set of attributes to determine which methodology to use in a particular data warehousing project. have experienced explosive growth in the last few years, and data warehousing has played a major role in the integration process. A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data that supports managerial decision making [4]. Data warehousing has been cited as the highest-priority post-millennium project of more than half of IT executives. A large number of data warehousing methodologies and tools are available to support the growing market. However, with so many methodologies to choose from, a major concern for many firms is which one to employ in a given data warehousing project. In this article, we review and compare several prominent data warehousing methodologies based on a common set of attributes. Online transaction processing (OLTP) systems are useful for addressing the operational data needs of a firm. However, they are not well suited for supporting decision-support queries or business questions that managers typically need to address. Such questions involve analytics including aggregation, drilldown, and slicing/dicing of data, which are best supported by online analytical processing (OLAP) systems. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Data in an OLAP warehouse is extracted and loaded from multiple OLTP data sources (including DB2, Oracle, IMS databases, and flat files) using Extract, Transfer, and Load (ETL) tools. The warehouse is located in a presentation server. It can span enterprisewide data needs or can be a collection of " conforming " data marts [8]. Data marts (subsets of data warehouses) are conformed by following a standard set of attribute declarations called a data warehouse bus. The data warehouse uses a meta-data repository to integrate all of its components. The metadata stores definitions of the source data, data models for target databases, and transformation rules that convert source data into target data. The concepts of time variance and nonvolatility are essential for a data warehouse [4]. Inmon emphasized the importance of cross-functional slices of data drawn from multiple sources to support a diversity of needs [4]; the foundation of his subject-oriented design was an enterprise data model. Kimball introduced the notion of dimensional modeling [8], which addresses the gap between relational databases and