Showing posts with label data model. Show all posts
Showing posts with label data model. Show all posts

Information Modeling and Relational Databases, Second Edition (The Morgan Kaufmann Series in Data Management Systems) Review

Information Modeling and Relational Databases, Second Edition (The Morgan Kaufmann Series in Data Management Systems)
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Information Modeling and Relational Databases, Second Edition (The Morgan Kaufmann Series in Data Management Systems) ReviewEveryone needs this book. Read more to find out why:
If you intend to create genuinely useful business applications without first creating an accurate conceptual data model and deriving the database schema from the model, then I hope your projects have very large budgets and flexible deadlines, because you'll need both. Accurate conceptual data models are not an academic curiousity, they are a practical necessity. Well designed databases are the heart of every business application, and accurate conceptual data models are the foundation of every well designed database.
This book presents a method for data modeling called Object Role Modeling (ORM). If you've never created a data model before, you might as well learn the best method from the start. If you've used E-R (Entity Relationship) modeling before, this is your chance to learn a method that overcomes the limitations of E-R, while building on the knowledge you already have.
ORM is based on facts (assertions about the business sphere you are modeling), not entities and attributes. Business users understand facts much better than they understand data modeling abstractions. By using ORM facts, you create your data model in a language that business users can understand and validate. Poor communication with business users and inadequate understanding of requirements are major causes of design deficiencies. ORM solves these issues through its fact based approach.
ORM is also much more expressive than any other popular data modeling notation, ncluding UML and all major flavors of E-R. Many business rules should be expressed as data constraints, but traditional data modeling languages don't do well at capturing these constraints. By capturing the constraints in an ORM model and validating with the users, you make the construction of a good application much easier.
Halpin is an excellent writer, and this book is very easy to read. The many examples and crisp writing style mean that you'll actually understand what the author intends, a refreshing change from most computer books. If you've read the previous edition of this book, this update is very worthwhile. There is a lot of expanded and new material, and you'll be happy you purchased the new edition.Information Modeling and Relational Databases, Second Edition (The Morgan Kaufmann Series in Data Management Systems) OverviewInformation Modeling and Relational Databases, second edition, provides an introduction to ORM (Object-Role Modeling)and much more. In fact, it is the only book to go beyond introductory coverage and provide all of the in-depth instruction you need to transform knowledge from domain experts into a sound database design. This book is intended for anyone with a stake in the accuracy and efficacy of databases: systems analysts, information modelers, database designers and administrators, and programmers. Terry Halpin, a pioneer in the development of ORM, blends conceptual information with practical instruction that will let you begin using ORM effectively as soon as possible. Supported by examples, exercises, and useful background information, his step-by-step approach teaches you to develop a natural-language-based ORM model, and then, where needed, abstract ER and UML models from it. This book will quickly make you proficient in the modeling technique that is proving vital to the development of accurate and efficient databases that best meet real business objectives. *Presents the most indepth coverage of Object-Role Modeling available anywhere, including a thorough update of the book for ORM2, as well as UML2 and E-R (Entity-Relationship) modeling. *Includes clear coverage of relational database concepts, and the latest developments in SQL and XML, including a new chapter on the impact of XML on information modeling, exchange and transformation. * New and improved case studies and exercises are provided for many topics. * The book's associated web site provides answers to exercises, appendices, advanced SQL queries, and links to downloadable ORM tools.

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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition Review

Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition
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Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition ReviewI think very highly of Data Modeling Made Simple (the first edition), so when this second edition came out I had great expectations - which were not only met but also exceeded. Although this second edition is more than twice the number of pages as the first edition, it is still an easy read.
Here are my favorite things about this book:
1.Clearly delivers on its ten objectives. Read the back cover and you will understand the key takeaways you will get after reading the book. After I read the book, I went back over each of these objectives and I was able to check each of these off as accomplished. Everything from justifying the model to building data models to assessing data models was knowledge I gleaned from the book. If you are interested in just one or a subset of these ten objectives, read the Read Me First section and it will reference the sections and chapters you need to read to meet your specific objective.
2.More examples more thoroughly presented. The first edition took a business card example from beginning to end. This edition further expands the business card example and adds several other examples including an ice cream example and many real world examples. The author uses spreadsheets to illustrate many modeling examples, and I too have found spreadsheets to be a very effective way to communicate data and business rules.
3.Data Model Scorecard. The first edition touched on the Scorecard which is the author's technique to reviewing a data model. This second edition goes into detail including providing the template which I can use on my modeling assignments to review my models.
4.Treating a dimensional model as more than just a physical data model. Many texts treat the dimensional as only a physical data model yet there is a business level that this book illustrates at both the subject area and logical levels.
5.Getting other Greats for free. Bill Inmon, Graeme Simsion, and Michael Blaha have all written chapters in this book. I have already starting using Simsion's technique of a diary on my assignments and found it very useful.
My only area for improvement would be to expand the book with more modeling conventions such as ORM and IDEF1X. There is a chapter on UML though that I did find informative. I question however if adding these extra notations would detract from the book's simplicity.
Overall, an excellent read that I would recommend to every business or techie that works with data.
Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition OverviewData 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


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