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

Accumulating Snapshot Fact Tables

Chapter 4 used an accumulating snapshot fact table to track products identified by serial or lot numbers as they move through various inventory stages in a warehouse. Take a moment to recall the distinguishing characteristics of an accumulating snapshot fact table:

  • A single row represents the complete history of a workflow or pipeline instance.
  • Multiple dates represent the standard pipeline milestone events.
  • The accumulating snapshot facts often included metrics corresponding to each milestone, plus status counts and elapsed durations.
  • Each row is revisited and updated whenever the pipeline instance changes; both foreign keys and measured facts may be changed during the fact row updates.

Applicant Pipeline

Now envision these same accumulating snapshot characteristics as applied to the prospective student admissions pipeline. For those who work in other industries, there are obvious similarities to tracking job applicants through the hiring process...