Data Modeler's Workbench: Tools and Techniques for Analysis and Design
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- Wiley
More About This Title Data Modeler's Workbench: Tools and Techniques for Analysis and Design
English
A goldmine of valuable tools for data modelers!
Data modelers render raw data-names, addresses, and sales totals, for instance-into information such as customer profiles and seasonal buying patterns that can be used for making critical business decisions. This book brings together thirty of the most effective tools for solving common modeling problems. The author provides an example of each tool and describes what it is, why it is needed, and how it is generally used to model data for both databases and data warehouses, along with tips and warnings. Blank sample copies of all worksheets and checklists described are provided in an appendix.
Companion Web site features updates on the latest tools and techniques, plus links to related sites offering automated tools.
English
STEVE HOBERMAN is the Lead Data Warehouse Developer for Mars, Inc. He has been data modeling since 1990 for the telecommunications, financial, and manufacturing industries. He speaks regularly at The Data Warehousing Institute conferences on advanced data modeling.
English
Foreword
Introduction
Acknowledgments
PART 1: BUILDING THE FOUNDATION
Chapter 1 Using Anecdotes, Analogies, and Presentations to Illustrate Data Modeling Concepts
Chapter 2 Meta Data Bingo
Chapter 3 Ensuring High-Quality Definitions
Chapter 4 Project Planning for the Data Modeler
PART 2: ANALYZING THE REQUIREMENTS
Chapter 5 Subject Area Analysis
Chapter 6 Subject Area Modeling
Chapter 7 Logical Data Analysis
PART 3: MODELING THE REQUIREMENTS AND SOME ADVICE
Chapter 8 The Normalization Hike and Denormalization Survival Guide
Chapter 9 The Abstraction Safety Guide and Components
Chapter 10 Data Model Beauty Tips
Chapter 11 Planning a Long and Prosperous Career in Data Modeling
Suggested Reading
Index
Introduction
Acknowledgments
PART 1: BUILDING THE FOUNDATION
Chapter 1 Using Anecdotes, Analogies, and Presentations to Illustrate Data Modeling Concepts
Chapter 2 Meta Data Bingo
Chapter 3 Ensuring High-Quality Definitions
Chapter 4 Project Planning for the Data Modeler
PART 2: ANALYZING THE REQUIREMENTS
Chapter 5 Subject Area Analysis
Chapter 6 Subject Area Modeling
Chapter 7 Logical Data Analysis
PART 3: MODELING THE REQUIREMENTS AND SOME ADVICE
Chapter 8 The Normalization Hike and Denormalization Survival Guide
Chapter 9 The Abstraction Safety Guide and Components
Chapter 10 Data Model Beauty Tips
Chapter 11 Planning a Long and Prosperous Career in Data Modeling
Suggested Reading
Index
