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

Summary

In this chapter we focused on two primary concepts. First, we looked at the accumulating snapshot fact table to track application or research grant pipelines. Even though the accumulating snapshot is used much less frequently than the more common transaction and periodic snapshot fact tables, it is very useful for tracking the current status of a short-lived process with standard milestones. As we described, accumulating snapshots are often complemented with transactional or periodic snapshot tables.

Second, we explored several examples of factless fact tables. These fact tables capture the relationship between dimensions in the case of an event or coverage, but are unique in that no measurements are collected to serve as actual facts. We also discussed the handling of situations in which you want to track events that didn’t occur.