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

Big Data Overview

What is big data? Its bigness is actually not the most interesting characteristic. Big data is structured, semistructured, unstructured, and raw data in many different formats, in some cases looking totally different than the clean scalar numbers and text you have stored in your data warehouses for the last 30 years. Much big data cannot be analyzed with anything that looks like SQL. But most important, big data is a paradigm shift in how you think about data assets, where you collect them, how you analyze them, and how you monetize the insights from the analysis.

The big data movement has gathered momentum as a large number of use cases have been recognized that fall into the category of big data analytics. These use cases include:

  • Search ranking
  • Ad tracking
  • Location and proximity tracking
  • Causal factor discovery
  • Social CRM
  • Document similarity testing
  • Genomics analysis
  • Cohort group discovery
  • In-flight aircraft status
  • Smart utility meters
  • Building sensors
  • Satellite...