Data Warehouse Life Cycle

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The Data Warehouse Lifecycle Toolkit, Second Edition
by Ralph Kimball et al. John Wiley & Sons (US). (c) 2008. Copying Prohibited.  

  Reprinted for Surendranadha Reddy, Bank of America   surendranadha.reddy@bankofamerica.com   Reprinted with permission as a subscription benefit of Books24x7, http://www.books24x7.com/

 

All rights reserved. Reproduction and/or distribution in whole or in part in electronic,paper or other forms without written permission is prohibited.

The Data Warehouse Lifecycle Toolkit, Second Edition

Chapter 6: Introducing Dimensional Modeling
Overview
Beginning with this chapter, you embark on the data track of the Kimball Lifecycle. The majority of the effort required to build the data warehouse/ business intelligence (DW/BI) system is expended in this track of activities, so it's critical that you get started on the right foot with a properly designed data model that addresses the business requirements. The authors of this book have spent most of their careers, going back to the early 1980s, designing and using databases that support the business's decision making process. From this collective experience, we have concluded that dimensional modeling is the most viable technique to deliver data for business intelligence because it addresses the twin non-negotiable goals of business understandability and fast query performance. Fortunately, many others in our industry have been similarly convinced over the years so we're no longer repeatedly debating this fundamental matter as we did in the 1990s. This chapter begins with a brief discussion comparing dimensional models to normalized models. We then provide a primer of core dimensional modeling concepts, followed by a discussion of the enterprise data warehouse bus architecture for ensuring consistency and integrating your organization's dimensional models. We then delve deeper into the common patterns you'll likely encounter in your source data and the corresponding dimensional response. Finally, we describe misleading myths and misperceptions about dimensional modeling that unfortunately continue to circulate in the industry. Chapter 6 provides the conceptual foundation of core dimensional modeling techniques, which is subsequently leveraged in Chapter 7 with its discussion of the dimensional modeling design process and associated tasks. This introduction to dimensional modeling is a must-read for the DW/BI team's data architects and dimensional modelers. Given the critical role played by the dimensional model, other team members such as the business analysts, ETL architects and developers, and BI application architects and developers should minimally read the first half of this chapter through the bus architecture discussion. REFERENCE Obviously, we can't synthesize everything there is to know about dimensional modeling into a single chapter. We strongly suggest that the modeling team has access to The Data Warehouse Toolkit, Second Edition (Wiley Publishing, 2002) by Ralph Kimball and Margy Ross as a reference. That book includes more detailed guidance using examples from a variety of industries, such as retail, financial services, telecommunications, education, and healthcare/insurance, as well as diverse business functions, including inventory, procurement, customer relationship management, accounting, and human resources. It's everything you wanted to know about dimensional modeling, but were afraid to ask. You can also search the articles and Design Tips available on our website at http://www.kimballgroup.com for additional dimensional modeling recommendations.

Making the Case for Dimensional Modeling
Before diving into specific guidance for designing dimensional models, we begin with a comparison to the normalized models typically encountered in the transaction source systems.

What Is Dimensional Modeling?
Dimensional modeling is a logical design technique for structuring data so that it's intuitive to business users...
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