Data Modeler's Workbench: Tools and Techniques for Analysis and Design (Paperback)

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Editorial Reviews

Product Description

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.

From the Back Cover

"This book is chock-full of useful techniques and tips for improving data models and designs. And it’s an entertaining read as well–a terrific combination!"
–Wayne Eckerson, Director of Education and Research, The Data Warehousing Institute

A data model is the heart and soul of any application, providing a foundation for efficient data entry and retrieval. It needs to be consistent with other models within your organization, accurately capture the current business requirements, and evolve to support changing business needs.

Data Modeler’s Workbench contains more than twenty finely tuned tools aimed at improving the speed, accuracy, flexibility, and consistency of your database, data warehouse, and operational applications. Steve Hoberman explains each tool with the help of detailed examples, showing how to apply each tool and where in the operational and reporting environment each tool is most effective. You can customize the tools in this book for your particular industry, organization, or project.

The companion Web site features:

• Downloadable copies of the worksheets and checklists that modelers can use on their own projects
• Updates on the latest tools, techniques, and discussion forums
• Links to other data modeling sites

Advance Praise:

"Any data modeler should own a copy of Steve Hoberman’s book on data modeling tools and techniques."
–David Marco, President, Enterprise Warehousing Solutions, Inc.

"Steve Hoberman has written a truly valuable book that is sure to advance the discipline of data modeling."
–David Wells, Founder and Principal Consultant, Infocentric

Wiley Computer Publishing
Timely. Practical. Reliable.

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Product Details

• Paperback: 416 pages
• Publisher: Wiley; 1st edition (December 7, 2001)
• Language: English
• ISBN-10: 0471111759
• ISBN-13: 978-0471111757
• Product Dimensions: 9.1 x 7.3 x 1.3 inches
• Shipping Weight: 1.9 pounds (View shipping rates and policies)
• Average Customer Review: 4.8 out of 5 stars  See all reviews (17 customer reviews)
• Amazon.com Sales Rank: #81,380 in Books (See Bestsellers in Books)

  Popular in these categories: (What's this?)

  #16 in	Books > Computers & Internet > Programming > Software Design, Testing & Engineering > Structured Design
  #20 in	Books > Computers & Internet > Computer Science > Modeling & Simulation
  #39 in	Books > Computers & Internet > Databases > Database Design

Customer Reviews

Most Helpful Customer Reviews

10 of 10 people found the following review helpful:

5.0 out of 5 stars The Best New Data Modeling Book in Years!, March 4, 2002

By	D. Wells (Kirkland, WA United States) - See all my reviews (REAL NAME)

Steve Hoberman has written a truly valuable book that is sure to advance the discipline of data modeling. The writing style make it a comfortable and interesting book to read, and the organization makes it useful as a reference. Steve's concepts, definitions, and classification schema help to advance data modeling as a learnable and repeatable process. Many aspects of this book added to my knowledge of data modeling. I learned some new techniques, and I've been a data modeling practitioner since the early 1980's. This is a must for every data modeler's bookshelf. I believe the single greatest impact this book will make is in its attention to data modeling as a human process as well as a technical one.

8 of 8 people found the following review helpful:

5.0 out of 5 stars great resource for novice and tenured data professionals, October 18, 2005

By	Kevin T. Mcgrath "Databaselawyer" (Minneapolis, MN USA) - See all my reviews (REAL NAME)

I just finished reading the Data Modeler's Workbench cover to cover. It is an excellent piece - or as Steve would say, a great story!

I wish I had read this when I first bought it years ago. It would have really helped my modeling career - and I've been a modeler the past seven years.

I think this book is a must read for modelers of any level - from start to finish, not skipping chapters. It nails requirements gathering and analysis, providing templates for capturing same, recognizing that is where the most work lies. I really like the normalization hike and analogy to hiking to the highest peak, then denormalizing and/or abstracting.

It is not a book on theory written by some college professor or glorified lecturer that never or rarely worked in the field; rather, Steve is a tenured data professional who took the time to write down how he does it successfully. Why reinvent the wheel or proceed along in a haphazard way when he lays out how to do it?

Steve also has all the templates on his website, which is a big help.

8 of 8 people found the following review helpful:

5.0 out of 5 stars A must read for data modelers, November 19, 2004

By	Carla S. (Baltimore, MD) - See all my reviews

I bought this book about a month ago based upon a recommendation from a colleague. Technical books don't tend to be the most interesting reading material, but this book with its practical and entertaining format kept me engaged. I have over 9 years of modeling experience (mostly transactional but getting more into data warehousing), and yet I picked up several new techniques. I have already created a Reality Check Subject Area Model of our data warehouse, and am currently working on a Family Tree of several operational systems.