Data Mapping for Data Warehouse Design

Data Mapping for Data Warehouse Design

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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.
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Product details

  • Paperback | 180 pages
  • 152 x 229 x 12.7mm | 290g
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • 012805185X
  • 9780128051856

Table of contents

Introduction
Data Mapping Stages
Data Mapping Types
Data Models
Data Mapper's Strategy and Focus
Uniqueness of Attributes and Its Importance
Pre-Requisites of Data Mapping
Surrogate Keys Vs. Natural Keys
Data Mapping Document Format
Data Analysis Techniques
Data Quality
Data Mapping Scenarios
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About Qamar Shahbaz

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.
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