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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals,

Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives:

  1. Know when a data model is needed and which type of data model is most effective for each situation
  2. Read a data model of any size and complexity with the same confidence as reading a book
  3. Build a fully normalized relational data model, as well as an easily navigatable dimensional model
  4. Apply techniques to turn a logical data model into an efficient physical design
  5. Leverage several templates to make requirements gathering more efficient and accurate
  6. Explain all ten categories of the Data Model Scorecard
  7. Learn strategies to improve your working relationships with others
  8. Appreciate the impact unstructured data has, and will have, on our data modeling deliverables
  9. Learn basic UML concepts
  10. Put data modeling in context with XML, metadata, and agile development

Book Review by Johnny Gay
In this book review, I address each section in the book and provide what I found most valuable as a data modeler. I compare, as I go, how the book's structure eases the new data modeler into the subject much like an instructor might ease a beginning swimmer into the pool.

This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools.

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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals,

Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives:

  1. Know when a data model is needed and which type of data model is most effective for each situation
  2. Read a data model of any size and complexity with the same confidence as reading a book
  3. Build a fully normalized relational data model, as well as an easily navigatable dimensional model
  4. Apply techniques to turn a logical data model into an efficient physical design
  5. Leverage several templates to make requirements gathering more efficient and accurate
  6. Explain all ten categories of the Data Model Scorecard
  7. Learn strategies to improve your working relationships with others
  8. Appreciate the impact unstructured data has, and will have, on our data modeling deliverables
  9. Learn basic UML concepts
  10. Put data modeling in context with XML, metadata, and agile development

Book Review by Johnny Gay
In this book review, I address each section in the book and provide what I found most valuable as a data modeler. I compare, as I go, how the book's structure eases the new data modeler into the subject much like an instructor might ease a beginning swimmer into the pool.

This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools.

44.95 In Stock
Data Modeling Made Simple: A Practical Guide for Business and IT Professionals,

Data Modeling Made Simple: A Practical Guide for Business and IT Professionals,

by Steve Hoberman
Data Modeling Made Simple: A Practical Guide for Business and IT Professionals,

Data Modeling Made Simple: A Practical Guide for Business and IT Professionals,

by Steve Hoberman

Overview

Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives:

  1. Know when a data model is needed and which type of data model is most effective for each situation
  2. Read a data model of any size and complexity with the same confidence as reading a book
  3. Build a fully normalized relational data model, as well as an easily navigatable dimensional model
  4. Apply techniques to turn a logical data model into an efficient physical design
  5. Leverage several templates to make requirements gathering more efficient and accurate
  6. Explain all ten categories of the Data Model Scorecard
  7. Learn strategies to improve your working relationships with others
  8. Appreciate the impact unstructured data has, and will have, on our data modeling deliverables
  9. Learn basic UML concepts
  10. Put data modeling in context with XML, metadata, and agile development

Book Review by Johnny Gay
In this book review, I address each section in the book and provide what I found most valuable as a data modeler. I compare, as I go, how the book's structure eases the new data modeler into the subject much like an instructor might ease a beginning swimmer into the pool.

This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools.

Product Details

ISBN-13: 9780977140060
Publisher: Technics Publications, LLC
Publication date: 10/28/2009
Edition description: TECHNICS PUBLICATIONS LLC
Pages: 360
Sales rank: 914,168
Product dimensions: 7.00(w) x 9.90(h) x 1.10(d)

About the Author

Steve Hoberman is one of the world's most well-known data modeling gurus. He understands the human side of data modeling and has evangelized "next generation" techniques. Steve taught his first data modeling class in 1992 and since then has educated more than 10,000 people about data modeling and business intelligence techniques. He has presented at over 50 international conferences, authored three data modeling books, founded the Design Challenges group, and invented the Data Model Scorecard®.

Table of Contents

SECTION I: Data Modeling Introduction 9
CHAPTER 1: What is a data model? 11
CHAPTER 2: Why do we need a data model? 21
CHAPTER 3: What camera settings also apply to a data model? 27
SECTION II: Data Model Components 37
CHAPTER 4: What are entities? 39
CHAPTER 5: What are attributes? 45
CHAPTER 6: What are relationships? 51
CHAPTER 7: What are keys? 63
SECTION III: Conceptual, Logical, and Physical Data Models 73
CHAPTER 8: What are conceptual data models? 75
CHAPTER 9: What are logical data models? 97
CHAPTER 10: What are physical data models? 119
SECTION IV: Data Model Quality 131
CHAPTER 11: Which templates can help with capturing requirements? 133
CHAPTER 12: What is the Data Model Scorecard®? 143
CHAPTER 13: How can we work effectively with others? 153
SECTION V: Essential Topics Beyond Data Modeling 169
CHAPTER 14: What is unstructured data? 171
CHAPTER 15: What is UML? 187
CHAPTER 16: What are the Top 5 most frequently asked modeling questions? 203
Suggested Reading 213
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