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Data Mapping for Data Warehouse Design
1st Edition - December 8, 2015
Author: Qamar Shahbaz
Paperback ISBN:9780128051856
9 7 8 - 0 - 1 2 - 8 0 5 1 8 5 - 6
eBook ISBN:9780128053355
9 7 8 - 0 - 1 2 - 8 0 5 3 3 5 - 5
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… Read more
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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.
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
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
No. of pages: 180
Language: English
Published: December 8, 2015
Imprint: Morgan Kaufmann
Paperback ISBN: 9780128051856
eBook ISBN: 9780128053355
QS
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