Data Mapping for Data Warehouse Design

Data Mapping for Data Warehouse Design

1st Edition - December 8, 2015

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  • Author: Qamar Shahbaz
  • eBook ISBN: 9780128053355
  • Paperback ISBN: 9780128051856

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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


data modelers and/or developers working with DB and DW

Table of Contents

    • Dedication
    • Chapter 1. Introduction
      • Abstract
      • Definition
    • Chapter 2. Data Mapping Stages
      • Abstract
      • Mapping from the Source to the Data Warehouse Landing Area
      • Mapping from the Landing Area to the Staging Database
      • Mapping from the Staging Database to the Load Ready or Target Database
      • Mapping from Logical Data Model to the Semantic or Access Layer
    • Chapter 3. Data Mapping Types
      • Abstract
      • Logical Data Mapping
      • Physical Data Mapping
    • Chapter 4. Data Models
      • Abstract
      • Definition
      • Normalized Data Model
      • Dimensional Data Model
      • Star Schema
    • Chapter 5. Data Mapper’s Strategy and Focus
      • Abstract
      • Mapper Who? How Does He or She Do It?
    • Chapter 6. Uniqueness of Attributes and its Importance
      • Abstract
      • Telecom
      • Manufacturing
      • Finance
      • Uniqueness in Data Warehouse
    • Chapter 7. Prerequisites of Data Mapping
      • Abstract
      • Logical Data Model
      • Entities and Their Description
      • Attributes and Their Description
      • Physical Data Model
      • Source System Data Model
      • Source System Table and Attribute Details
      • Subject Matter Expert
      • Production Quality Data
    • Chapter 8. Surrogate Keys versus Natural Keys
      • Abstract
      • Natural Keys
      • Surrogate Keys
    • Chapter 9. Data Mapping Document Format
      • Abstract
      • Header-Level Rules
      • Column-Level Rules
      • Major Parts of the Data Mapping Document
      • Data Mapping Columns Explained
    • Chapter 10. Data Analysis Techniques
      • Abstract
      • Source Data Sample
      • What to Look For
      • Uniqueness
      • History Pattern Analysis
      • SQL Tools
      • Microsoft Excel and Other Tools
    • Chapter 11. Data Quality
      • Abstract
      • What is Data Quality?
      • How Do You Benefit from Data Quality?
      • Factors Determining Data Quality
      • Stages of Data Warehousing Susceptible to Data Quality Problems
      • Classification of Data Quality Issues
      • How Can You Assess Data Quality?
      • What Can You Do to Make Data Quality a Success?
    • Chapter 12. Data Mapping Scenarios
      • Abstract
      • Data Transformation (Normalized Model)
      • Data Joining (Normalized Model)
      • Data Integration from Multiple Sources (Normalized Model)
      • Data Quality Improvement
      • Prioritized Data Consolidation or Joining
      • History Handling (Normalized Model)
      • History Handling Done in the Source (Normalized Model)
      • History Handling with No Rules on Date or Time
      • Joining the Source Data with the Target Table
      • History Handling from Snapshots
      • Master Data (Normalized Model)
      • Surrogate Keys
      • Call Detail Record (CDR) Mapping
      • Performance Issue Handling in Mapping
      • Business Mapping, Reference, and Lookup Data (Normalized Model)
      • Business Key, Surrogate, or Helping Table with Multiple Unique IDs for the Same Logical Concept
      • Denormalized or Data Mart Table
      • Access, Semantic, or Presentation Layer Attributes Mapping
      • Dimensions Mapping
      • Apply Logic versus Transformation Logic
      • Dividing the Dataset Into Smaller Chunks
      • Unstructured Data
      • Data Transpose
      • Aggregate Functions and Loading Cycle
      • Initial Load versus Delta Load
      • Recursive Query
      • Loading Sequence of Mapping
    • Glossary and Nomenclature List
    • Bibliography

Product details

  • No. of pages: 180
  • Language: English
  • Copyright: © Morgan Kaufmann 2015
  • Published: December 8, 2015
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9780128053355
  • Paperback ISBN: 9780128051856

About the Author

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.

Affiliations and Expertise

Senior business intelligence consultant, Stewart Title, Lahore, Pakistan

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  • John D. Mon Jun 13 2022

    Good book on a Niche topic in data platform design

    Finally there is well written book on data mapping, which in my opinion is most important area within information modeling domain. Learned a lot from different scenarios presented in the book. I would recommend 2nd Edition covering new challenges after Data Science has become mainstream.