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The Data Warehouse Toolkit

The Data Warehouse Toolkit - Third Edition

By : Ralph Kimball, Margy Ross
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The Data Warehouse Toolkit

The Data Warehouse Toolkit

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)
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1
Cover
2
Title Page
3
Copyright
4
About the Authors
5
Credits
6
Acknowledgements
29
Index
30
Advertisement
31
End User License Agreement

Develop Incremental ETL Processing

One of the biggest challenges with the incremental ETL process is identifying new, changed, and deleted rows. After you have a stream of inserts, modifications, and deletions, the ETL system can apply transformations following virtually identical business rules as for the historic data loads.

The historic load for dimensions and facts consisted largely or entirely of inserts. In incremental processing, you primarily perform inserts, but updates for dimensions and some kinds of fact tables are inevitable. Updates and deletes are expensive operations in the data warehouse environment, so we’ll describe techniques to improve the performance of these tasks.

Step 7: Dimension Table Incremental Processing

As you might expect, the incremental ETL system development begins with the dimension tables. Dimension incremental processing is very similar to the historic processing previously described.

Dimension Table Extracts

In many cases, there is a customer...

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The Data Warehouse Toolkit
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