Data Mapping for Data Warehouse Design

Data Mapping for Data Warehouse Design

by Qamar Shahbaz


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Data Mapping for Data Warehouse Design by Qamar Shahbaz

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.

  • 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

Product Details

ISBN-13: 9780128051856
Publisher: Elsevier Science
Publication date: 12/22/2015
Pages: 180
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Qamar shahbaz Ul Haq is currently a senior business intelligence consultant with Stewart Title where he creates cloud based business intelligence and SAAS Big Data applications. He has more than 9 years of experience designing Business Intelligence / Data Warehouses solutions and has spent most of this time in data mapping, working across different industries and cultures learning different aspects of this field. In previous roles he has created solutions ranging from billing systems to semantic design to performance optimization for maximum throughput of data processing.

Table of Contents

  1. Introduction
  2. Data Mapping Stages
  3. Data Mapping Types
  4. Data Models
  5. Data Mapper's Strategy and Focus
  6. Uniqueness of Attributes and Its Importance
  7. Pre-Requisites of Data Mapping
  8. Surrogate Keys Vs. Natural Keys
  9. Data Mapping Document Format
  10. Data Analysis Techniques
  11. Data Quality
  12. Data Mapping Scenarios

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