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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition
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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
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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).
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Product details
Paperback: 244 pages
Publisher: Technics Publications; Second edition (August 18, 2009)
Language: English
ISBN-10: 0977140067
ISBN-13: 978-0977140060
Product Dimensions:
7.5 x 0.6 x 9.2 inches
Shipping Weight: 1.7 pounds (View shipping rates and policies)
Average Customer Review:
3.9 out of 5 stars
24 customer reviews
Amazon Best Sellers Rank:
#588,138 in Books (See Top 100 in Books)
This book starts out with great potential but doesn't quite achieve its value proposition. You get a decent theoretical foundation, but right at the point where the rubber should hit the road the book moves on to random topics.I'm a software developer and needed concrete advice on modeling, nullability, joins, when and where to index, etc. Nope. For example, it describes many-to-many relationships and models them with an additional lookup table, but never explains why that is done. Still wondering. In place of that we get chapters on XML, unstructured data, and working with people. Useful information, but not a substitute for the above.The quality scorecard basically says a model should match requirements, completely, and follow org standards. Really, what about scalability and maintenance? Don't need a treatise and realize this was written for Business and IT, but one more chapter would have been great. I think an overview of those issues would be helpful to managers as well to understand when they receive push-back on initiatives as impractical.A minor gripe, the 1990s design, awkward full justification, and "inkjet quality" printing don't help but hinder readability.On the plus side---am now a lot better at reading modeling/UML diagrams and understanding the SQLAlchemy documentation better---real benefits.This book is decidedly mixed, I'd throw out every 5-star and 1-star reviews as carelessness, malice, or ballot stuffing.
As a computer science graduate, this book was a breath of fresh air. It was easy to understand, and actually an enjoyable read. It covered many of the topics I learned in school in my data modeling class, but it laid them out in an easier to understand, Real world example, business oriented way. I found that the best part of this book is the step by step approaches the author takes to building a Subject Area Model (which is very useful in the business world in modeling everything), and the Scorecard he provides to rate and grade the models. All the terminology is explained and the book does a really great job at refreshing your memory if you need to review basic concepts.Overall, I would recommend this book to other computing students and professionals alike, especially ones in the field of business. This book will be the most useful to you if you are trying to model business needs or data, or if you are trying to gather data to build a database.
I'm coming from a "home-grown" Business Analyst perspective, or someone with a technical/front-end web development background, attempting to learn some basics in order to guide a cross-functional team through the effort of building a data warehouse. I found this book to be tremendously helpful. I love the style in which it is written, and how each chapter builds and re-iterates (in context) what has been previously mentioned. This is a very easy read and not overly presumptuous of technical knowledge. I highly recommend this book, and I was stretch to say this author in general... his approach to writing makes the concepts presented easy to learn.
The author provides a concise and easy to understand book on data modeling. The explanations are clear and the order in which is material is covered is good. I found the explanation for normalization in this book to be the best simple explanation I have heard yet. The material progresses from higher level modeling through logical modeling down to the physical model and why it differs from the logical. There are additional chapters help evaluate model thoroughness and quality. As someone looking for a refresher this book was not bad. This book would likely not be useful for an experienced modeler.The book holds true to the title and is Data Modeling Made Simple.
I have been developing data models for over 20years and have read a number of texts on the subject. This is an excellent introductory book on the subject. The examples used are practical and illustrative of the concepts presented in the material. The author also presents several templates, including the Data Model Scorecard, that I plan on incorporating into my own practices. The book is written without any preconceived notions about a person's data modeling or technology background. It is a must read for new practitioners and business professionals who will be playing a data stewardship role.Highly recommended.
Excellent outline of modeling as it relates to business and IT intersections. Great presentation and visuals as well as application that you can take in to work with you the same day. Has really helped me to communicate for effectively with my business users and present things in a way they can embrace and understand.
This excellent book delivers on its promise. That promise is to provide a readable and accessible approach to the vital business process of data modeling. Hoberman has accomplished this admirable task within the corpus of this important text better than any other author I've ever encountered. And I've been involved in logical data modeling and relational database design for now over twenty-five years.Here is my most emphatic endorsement of this important book. I intend to disseminate the teachings of this book to as many interested parties as are willing to learn. Hoberman has accomplished a huge and terribly important task in support of the craft of data modeling. And I intend henceforth to sing his praises in this regard. I would now strongly recommend this important book to any business person whose responsibilities include, or are in any way related to, effective logical data modeling and relational database design. God bless.
The basis of all web development , desktop or app is a good modeling database and this book is perfect to understand that.La base de todo desarrollo web, de escritorio o app es un buen modelado de base de datos y este libro es perfecto para entender eso.
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