Book Image

The Data Warehouse Toolkit - Third Edition

By : Ralph Kimball, Margy Ross
5 (1)
Book Image

The Data Warehouse Toolkit - Third Edition

5 (1)
By: Ralph Kimball, Margy Ross

Overview of this book

The volume of data continues to grow as warehouses are populated with increasingly atomic data and updated with greater frequency. Dimensional modeling has become the most widely accepted approach for presenting information in data warehouse and business intelligence (DW/BI) systems. The goal of this book is to provide a one-stop shop for dimensional modeling techniques. The book is authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence. The book begins with a primer on data warehousing, business intelligence, and dimensional modeling, and you’ll explore more than 75-dimensional modeling techniques and patterns. Then you’ll understand dimension tables in-depth to get a good grip on retailing and moved towards the topics of inventory. Moving ahead, you’ll learn how to use this book for procurement, order management, accounting, customer relationship management, and many more business sectors. By the end of this book, you’ll be able to gather all the essential knowledge, practices, and patterns for designing dimensional models.
Table of Contents (31 chapters)
Free Chapter
1
Cover
2
Title Page
3
Copyright
4
About the Authors
5
Credits
6
Acknowledgements
29
Index
30
Advertisement
31
End User License Agreement

Lifecycle Wrap-up Activities

The following sections provide recommendations to ensure your project comes to an orderly conclusion, while ensuring you’re poised for future expansion.

Deployment

The technology, data, and BI application tracks converge at deployment. Unfortunately, this convergence does not happen naturally but requires substantial preplanning. Perhaps more important, successful deployment demands the courage and willpower to honestly assess the project’s preparedness to deploy. Deployment is similar to serving a large holiday meal to friends and relatives. It can be difficult to predict exactly how long it will take to cook the meal’s main entrée. Of course, if the entrée is not done, the cook is forced to slow down the side dishes to compensate for the lag before calling everyone to the table.

In the case of DW/BI deployment, the data is the main entrée. “Cooking” the data in the ETL kitchen is the most unpredictable task...