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Chapter 1 - Introduction
Page 1 - Book chapterAbstract only
Chapter 2 - Data Mapping Stages
Pages 3-4 - Book chapterAbstract only
Chapter 3 - Data Mapping Types
Page 5 - Book chapterAbstract only
Chapter 4 - Data Models
Pages 7-16 - Book chapterAbstract only
Chapter 5 - Data Mapper’s Strategy and Focus
Pages 17-20 - Book chapterAbstract only
Chapter 6 - Uniqueness of Attributes and its Importance
Pages 21-24 - Book chapterAbstract only
Chapter 7 - Prerequisites of Data Mapping
Pages 25-28 - Book chapterAbstract only
Chapter 8 - Surrogate Keys versus Natural Keys
Pages 29-30 - Book chapterAbstract only
Chapter 9 - Data Mapping Document Format
Pages 31-35 - Book chapterAbstract only
Chapter 10 - Data Analysis Techniques
Pages 37-66 - Book chapterAbstract only
Chapter 11 - Data Quality
Pages 67-82 - Book chapterAbstract only
Chapter 12 - Data Mapping Scenarios
Pages 83-165 - Book chapterNo access
Glossary and Nomenclature List
Pages 167-168 - Book chapterNo access
Bibliography
Page 169
About the book
Description
Data mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.
Data mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.
Key Features
- Covers all stages of data warehousing and the role of data mapping in each
- Includes a data mapping strategy and techniques that can be applied to many situations
- Based on the author’s years of real-world experience designing solutions
- Covers all stages of data warehousing and the role of data mapping in each
- Includes a data mapping strategy and techniques that can be applied to many situations
- Based on the author’s years of real-world experience designing solutions
Details
ISBN
978-0-12-805185-6
Language
English
Published
2016
Copyright
Copyright © 2016 Elsevier Inc. All rights reserved.
Imprint
Morgan Kaufmann