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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition, by Steve Hoberman
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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:
Know when a data model is needed and which type of data model is most effective for each situation Read a data model of any size and complexity with the same confidence as reading a book Build a fully normalized relational data model, as well as an easily navigatable dimensional model Apply techniques to turn a logical data model into an efficient physical design Leverage several templates to make requirements gathering more efficient and accurate Explain all ten categories of the Data Model Scorecard Learn strategies to improve your working relationships with others Appreciate the impact unstructured data has, and will have, on our data modeling deliverables Learn basic UML concepts Put data modeling in context with XML, metadata, and agile development - Sales Rank: #245391 in Books
- Brand: Brand: Technics Publications
- Published on: 2009-09-15
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .55" w x 7.50" l, 1.60 pounds
- Binding: Paperback
- 244 pages
Features
- Used Book in Good Condition
Review
This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools.
The chapters in the book are divided into 5 sections. The chapters in each section end with an exercise and a list of the key points covered to reinforce what you've learned. I find myself comparing the teaching structure of the book to the way most of us learn to swim.
SECTION I: Data Modeling Introduction
The first section is like the shallow end of the pool, where as a beginning swimmer, you can dip your toes in to test the water. These easy chapters are short and concise. Here the author uses very common objects to describe what a data model is, and why it is so valuable. His first examples made excellent use of what's truly a universal data model to millions of computer users in school and business: the spreadsheet.
SECTION II: Data Model Components
In the second section, Steve Hoberman introduces you to the simplest components that make up a data model, and explains the important terms that we apply when we discuss them. By the end of section 2, you now have both feet comfortably in the water. You're ready and eager to plunge deeper into the depths of this pool of data model knowledge.
SECTION III: Subject Area, Logical, and Physical Data Models
You've made it to the deep end of the pool where you get a real workout as you lap through the 3 levels of data models: subject area (or conceptual), logical, and physical. Just as there are different strokes for different folks, there are different models for different audiences. By the end of section 3, you'll be able to swim through the intricacies of a data model like a barracuda.
SECTION IV: Data Modeling Quality
Just as swimmers can kick-start their movement through the water with the use of swimming aids (maybe a flotation device or fins will help), you can utilize Steve's 4 favorite templates to collect and organize the requirements that will define your data model. You may recall the scorecard the Olympic judges use to rate a dive. Steve introduces his Data Model Scorecard, which applies a quality rating to a data model. It's an objective look at the quality of the model built. We are actually adopting this tool where I work, after applying our own weightings to his 10 criteria.
SECTION V - Beyond Data Modeling
Believe it or not, you're ready to leave the pool and jump head first into a small part of the ocean of outside influences that affect a data modelers' work. Bill Inmon tackles unstructured data with taxonomies. Here he simply provides the best explanation about taxonomies and ontologies that I've found. Michael Blaha, who literally wrote the book on the subject of the Unified Modeling Language (UML), follows with an introduction about UML. Steve ends by answering the 5 most frequently asked modeling questions that he has encountered.
This revision took the first edition up several notches from what some deemed a data modeling for dummies book, to what is now a full-fledged textbook. It's easy to see how it could quickly and easily light the way for many future data modelers in any classroom. I have it on good authority that the author wrote this book to be the most easy-to-read and comprehensive data modeling text on the planet. I agree. This is in itself a wonderful way-finding tool for data modelers that's very easy on the eyes and complete in its coverage. --The Data Administration Newsletter (tdan.com), by Johnny Gay
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(r).
Most helpful customer reviews
16 of 16 people found the following review helpful.
The title is accurate!
By Amazon Customer
I have taught data modeling at local colleges from thick, expensive books. I wish I had used this one instead. The examples begin with business cards and ice cream cones -- things people know -- instead of dropping the reader into the middle a major corporation.
The writing style is simple, clean and chatty. Each chapter ends with a list of the key points, so you check yourself. What surprised me is how much he covered without giving you the feeling that you were being buried by too much technical stuff at once.
In short, the title was accurate -- it really did make Data Modeling simple!
11 of 11 people found the following review helpful.
Helps You Communicate With Your Business Partner
By Catherine Nolan
The subtitle, "A Practical Guide for Business and IT Professionals", describes an important reason to read this book. Often it is hard to make Business People understand why a Data Model is as valuable to them as it is to the Database Administrators who will ultimately build the physical database. Steve Hoberman gives us questions to ask the Business that will allow them to fully understand their needs and allow us to capture and communicate those requirements in a form known as a Logical Data Model.
I would recommend this book for those wanting to know more about data models because it is easy to read and understand. At the same time I would recommend it for experienced data professionals because it reinforces and reminds us of the concepts and practices we should all be following as we create our "blueprints" for data.
8 of 8 people found the following review helpful.
Wow!
By Niranjan Joshi
I wish I had found this book earlier! I have tried understanding data modeling in the past, only to be confounded on how to generate value in real world. This book makes that real life applicability possible.
Following are my favorites about this book:
1. This book makes you 'think' data. Each concept is articulated in clear, concise, and practical words, and shows how to make sense of it in the real world.
2. Brings out the context of why, what and how about data modeling in a practical way.
3. There are recommendations on how and when to use certain techniques.
4. Gives a well rounded and practicable context on data/data modeling, the interactions that surround the business of receiving, providing and eliciting data, in a sense the various modes and settings that data communication and data modeling occurs in, and how to deal with it.
5. Gives you a new perspective on 'data' and 'data modeling' (Wanted to leave this point for last, as this is the most profound one out of all). Once absorbed, its so simple, makes you wonder why it was difficult in the past.
The second edition, also talks about capturing requirements and its various methods. In fact, I am embarking on a data requirements project and find these techniques extremely useful.
The conversational style doesnot teach but involves the reader. After the book has been read, its very clear what data modeling is , what it constitutes of, what is the context it happens in, why its important to model data and how the decomposition process works.
All in all, this book contains a wealth of information that can be used right away and is a invaluable resource.
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