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

  • 4h 6m
  • Steve Hoberman
  • Technics Publications
  • 2009

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 navigable 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

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.

In this Book

  • What is a Data Model?
  • Why Do We Need a Data Model?
  • What Camera Settings Also Apply to a Data Model?
  • What are Entities?
  • What are Data Elements?
  • What are Relationships?
  • What are Keys?
  • What are Subject Area Models?
  • What are Logical Data Models?
  • What are Physical Data Models?
  • Which Templates Can Help with Capturing Requirements?
  • What is the Data Model Scorecard®?
  • How Can We Work Effectively with Others?
  • What is Unstructured Data?
  • What is UML?
  • What are the Top Five Most Frequently Asked Modeling Questions?
SHOW MORE
FREE ACCESS

YOU MIGHT ALSO LIKE

Course Software Product Management: Software Process Model Options
Rating 4.8 of 4 users Rating 4.8 of 4 users (4)
Book Data Analytics: Principles, Tools, and Practices: A Complete Guide for Advanced Data Analytics Using the Latest Trends, Tools, and Technologies
Book Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python