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Data Modeling Made Simple, 2nd Edition: A Practical Guide for Business and IT Professionals Second Edition
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Product Details
- Provides practical working knowledge of data modeling concepts and best practices
- Teaches how to read data models of any size and complexity with confidence
- Covers building fully normalized relational data models and easily navigatable dimensional models
- Offers techniques for turning logical data models into efficient physical designs and leveraging templates for requirements gathering
- Explains ten categories of the Data Model Scorecard(r) and strategies to improve working relationships with others
- Puts data modeling in context with XML, metadata, and agile development, and teaches basic UML concepts
| Publisher | Technics Publications; Second edition (August 18, 2009) |
| Language | English |
| Paperback | 244 pages |
| ISBN-10 | 0977140067 |
| ISBN-13 | 978-0977140060 |
| Item Weight | 1.62 pounds (0.73 kg) |
| Dimensions | 7.5 x 0.55 x 9.25 inches (19.1 x 1.4 x 23.5 cm) |
Product Description
Data Modeling Made Simple, 2nd Edition: A Practical Guide for Business and IT Professionals Second Edition
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Steve Hoberman , Carol Lehn (Editor) Programming Editorial Review
This book on data modeling is a mixed bag for readers, with some finding it helpful and others feeling it falls short of expectations. While it provides a decent theoretical foundation, it fails to deliver practical advice on some topics. The book covers many-to-many relationships but doesn't explain the purpose of using an additional lookup table. Chapter topics like XML and unstructured data, while useful, don't substitute for more practical advice. The style of writing and easy-to-understand explanations make it useful for those with limited tech knowledge. Overall, the book is an excellent reference for data warehouse modeling, but some aspects like scalability and maintenance could use more attention.
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Pros
- Provides a decent theoretical foundation
- Easy-to-understand explanations and writing style
- A helpful reference for data warehouse modeling
Cons
- Falls short on practical advice for certain topics
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