A good data model is the most critical of all database activities.  You cannot tune a poorly designed database and a firm understanding of data modeling is critical to a successful implementation. Unfortunately, many of the theory books are extremely complex and fail to provide a plain English guidelines for  creating a robust data mode.  This book addresses that issue and provides proven guidelines that you can apply to any database.  This practical book explores all aspects of data modeling and shows you a proven real-world approach that ensures that your data is easy to retrieve and manage.

* Learn the essential principles of successful data modeling. * See application of techniques using included freeware & sample models. * See how to avoid the most common data modeling pitfalls. * Understand how to apply reusable data modeling patterns.

About the Authors:

 

Bert Scalzo	Bert Scalzo is one of the world's most highly-respected Oracle professionals with a breadth of experience spanning over two decades (beginning with Oracle 4). A former Oracle employee (Oracle Education and Consulting). Dr. Scalzo holds several Oracle Masters certifications, a BS, MS and PhD in Computer Science and a Masters Degree in Business Administration (MBA). Dr. Scalzo is very active in the database community and he has written landmark articles for Oracle Technology Network (OTN), Oracle Informant, Oracle Magazine, PC Week, Linux Journal, Dell Power Edge Magazine, www.orafaq.com and www.linux.com.  Dr Scalzo is also the author of many database books, including TOAD Handbook, Toad Pocket Reference for Oracle (Pocket Reference (O'Reilly) and Oracle DBA Guide to Data Warehousing and Star Schemas.
Ramon Graham	Ramon Graham is a senior product manager at Quest Software and is responsible for leading the development efforts for Toad for Oracle. He has been with Quest since 1999, progressing through several roles including product management, requirements analysis and technical support. Prior to Quest, Ramon worked in various IT development roles for several companies in Southern California on Oracle and Web development projects. He holds a B.S. in Engineering Physics from the University of California Berkley and is a member of both Pragmatic Marketing and the Project Management Institute. Ramon speaks regularly at Oracle and Toad User Groups around the world.

Table of Contents:

 

Introduction to Data Modeling   Why Do We Model Everyone Data Models History of Data Modeling Common Data Modeling Terms Software Development Life Cycle Various Data Modeling Notations Numerous Data Modeling Tools   Logical Data Models   It’s All About the Business Information Gathering Entities Attributes Relationships Unique Identifiers Surrogate Identifiers Bridging or Intersection Entities Super and Sub-Types Other Business Rules Logical Sub-Models   Physical Data Models   It’s All About the Database Reverse Engineering Tables Columns Foreign Keys Primary and Unique Keys Other Constraints Surrogate Keys Additional Indexing Database Constraints Physical Sub-Models   Translating Logical to Physical   Basic Transformation Process Tailor to Database Platform Specifics Typical Information Systems Additions Common Pitfalls to Avoid            i.     Many To Many Relationships           ii.     Avoid Partial Unique Keys            iii.     Avoid Candidate Key Loss             iv.     Avoid Surrogate Key Loss             v.     Avoid Partial Foreign Keys             vi.     Avoid Indirect Foreign Keys            vii.     Avoid Bogus Foreign Keys           viii.     Problematic Relationships             ix.     Using Proper Normal Forms              x.     Super and Sub-Types Choices

Reusable Modeling Patterns   Not Everything is Custom Side Effects of Customization Benefits of Reusable Models Roles Assemblies   Model Based Database Change Management   All Databases Change with Time Key Change Management Goals Common Change Management  Tasks Change Management Handling Options Why Data Models Should be the Source Example Change Management via Model   Dimensional Modeling   Known Your Application Star Schema Design Dimension Hierarchies Avoid Snowflakes Common Aggregations Specialized Index Design Tuning Star Schema Queries   Other Modeling Considerations   Organizing Models Printing Large Models Keeping Models Current Running “JAD” Sessions Switching Modeling Tools Shared Modeling Among Teams Migrating Database Platforms Best Practices