Data Modeling Essentials / Edition 3

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Overview

Data Modeling Essentials, Third Edition provides expert tutelage for data modelers, business analysts and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management.

The third edition of this popular book retains its distinctive hallmarks of readability and usefulness, but has been given significantly expanded coverage and reorganized for greater reader comprehension. Authored by two leaders in the field, Data Modeling Essentials, Third Edition is the ideal reference for professionals and students looking for a real-world perspective.

· Thorough coverage of the fundamentals and relevant theory.
· Recognition and support for the creative side of the process.
· Expanded coverage of applied data modeling includes new chapters on logical and physical database design.
· New material describing a powerful technique for model verification.
· Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict.
· Extensive online component including course notes and other teaching aids (www.mkp.com).

UML diagrams now available! Visit the companion site for more details.

Click here to view a book review by Steve Hoberman!

Data Modeling Essentials, Third Edition provides expert tutelage for data modelers, business analysts and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management.

The third edition of this popular book retains its distinctive hallmarks of readability and usefulness, but has been given significantly expanded coverage and reorganized for greater reader comprehension. Authored by two leaders in the field, Data Modeling Essentials, Third Edition is the ideal reference for professionals and students looking for a real-world perspective. Thorough coverage of the fundamentals and relevant theory.
* Recognition and support for the creative side of the process.
* Expanded coverage of applied data modeling includes new chapters on logical and physical database design.
* New material describing a powerful technique for model verification.
* Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict.
* Extensive online component including course notes and other teaching aids

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Editorial Reviews

From the Publisher
"The perfect balance of theory and practice, giving the reader both the foundation and the tools to deliver high-quality data models."
-Karen Lopez, Principal, InfoAdvisors, Inc.

"The complete guide to data modeling for the reflective practitioner. Students like this book and so do I -- it is clear and accessible without sacrificing rigor."
-Professor Graeme Shanks, School of Business Systems, Monash University, Australia

"A unique, practical and comprehensive guide, providing an invaluable resource to anyone involved in data modeling from the novice to the expert data modeler."
-Len Silverston, author of The Data Model Resource Book, Volumes 1 and 2.

"Includes an extraordinary amount of good, useful, and well articulated information about the field."
-David Hay, President, Essential Strategies, Inc. and author of Data Model Patterns

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Product Details

Meet the Author

Graeme C. Simsion has over 25 years experience in information systems as a DBA, data modeling consultant, business systems designer, manager, and researcher. He is a regular presenter at industry and academic forums, and is currently a Senior Fellow with the Department of Information Systems at the University of Melbourne.

Graham C. Witt is an independent consultant with over 30 years of experience in assisting enterprises to acquire relevant and effective IT solutions. His clients include major banks and other financial institutions; businesses in the insurance, utilities, transport and telecommunications sectors; and a wide variety of government agencies. A former guest lecturer on Database Systems at University of Melbourne, he is a frequent presenter at international data management conferences.

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Table of Contents

Part I: Basics

Chapter 1: What is Data Modeling?
Chapter 2: Basics of Good Structure
Chapter 3: The Entity-Relationship Approach
Chapter 4: Subtypes & Supertypes
Chapter 5: Attributes and Columns
Chapter 6: Primary Keys and Identity
Chapter 7: Extensions and Alternatives

Part II: Putting it Together

Chapter 8: Organizing the Data Modeling Task
Chapter 9: Understanding the Business Requirements
Chapter 10: Conceptual Modeling
Chapter 11: Logical Database Design
Chapter 12: Physical Database Design

Part III: Advanced Topics

Chapter 13: Advanced Normalization
Chapter 14: Modeling Business Rules
Chapter 15: Time-Dependent Data
Chapter 16: Modeling for Data Warehouses and Data Marts
Chapter 17: Enterprise Data Models and Data Management

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Sort by: Showing 1 Customer Reviews
  • Anonymous

    Posted August 15, 2005

    COMPETENCY IN ENTERPRISE DATA MODELING

    Once you have a basic understanding of your application development tool, it will be a lot easier to learn the principles of data modeling. Authors Graeme Simsion and Graham Wittdone have done an outstanding job in this book of helping IT professionals to acquire competency in data modeling. Simsion and Wittdone begin this book by covering the basics of data modeling. Next, the authors look at some fundamental techniques for organizing data. In addition, the authors present a top-down approach to data modeling, supported by a widely used diagramming convention. They also look at a particular and very important type of choice in data modeling. Then, they turn to the nuts and bolts of data: attributes and columns. The authors then look in detail at the technical criteria governing primary key selection. Next, they look at some of the more common alternatives and extensions, focusing on conceptual modeling. Then, they look at the critical data modeling issues in project planning and management, with the aim of giving you the tools to examine critically any proposed approach from a data modeling perspective. The authors continue by looking at a variety of techniques for gaining a holistic understanding of the relevant business area and the role of the proposed information system. Next, they cover the development and use of a repertoire of standard solutions that are a large part of practical data modeling. In addition, they then look at the most common situation and describe the transformations and design decisions that are needed to apply to the conceptual model to produce a logical model suitable for direct implementation as a relational database. The authors then review the inputs that the physical database designer requires in addition to the Logical Data Model as well as, looking at a number of options available for achieving performance goals. Next, they look at three further stages of normalization: Boyce-Codd normal form (BCNF), fourth normal form (4NF), and fifth normal for (5NF). They then continue to look in a broad fashion at the business rules and then focus on the types of rules that are of particular concern to the data modeler. In addition, the authors look at some basic principles and structures for handling time-related data. Next, they look at how the requirements for data marts and data warehouses differ from those for operational databases. Finally, they look briefly at data management in general, and then discuss the uses of enterprise data models. With the preceding in mind, the authors have done an excellent job of showing how to develop enterprise data modeling. At the same time, the authors caution that 'while enterprise data models can be powerful vehicles for promulgating new ideas, they may also stifle original thinking by requiring conformity.'

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