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Item #:
83710764
Data Model Patterns: Conventions of Thought
Item #:
83710764
€ 12
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- Learning the basics of a modeling technique is not the same as learning how to use and apply it. To develop a data model of an organization is to gain insights into its nature that do not come easily. Indeed, analysts are often expected to understand subtleties of an organization's structure that may have evaded people who have worked there for years. Here's help for those analysts who have learned the basics of data modeling (or entity/relationship modeling) but who need to obtain the insights required to prepare a good model of a real business. Structures common to many types of business are analyzed in areas such as accounting, material requirements planning, process manufacturing, contracts, laboratories, and documents. Topics In each chapter, high-level data models are drawn from the following business areas: * The Enterprise and Its World * The Things of the Enterprise * Procedures and Activities * Contracts * Accounting * The Laboratory * Material Requirements Planning * Process Manufacturing * Documents * Lower-Level Conventions
| Publisher | Dorset House |
| Publication date | January 1, 1996 |
| Language | English |
| Print length | 268 pages |
| ISBN-10 | 0932633293 |
| ISBN-13 | 978-0932633293 |
| Item Weight | 1.6 pounds (730 grams) |
| Dimensions | 7.5 x 0.75 x 10.5 inches (19.1 x 1.9 x 26.7 cm) |
| Part of series | Dorset House eBooks |
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Data Model Patterns: Conventions of Thought
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Structured Design Editorial Review
Data Model Patterns: Conventions of Thought (Dorset House eBooks) is an essential read for anyone involved in data modeling. David Hay's insights distill complex relational data architecture principles into foundational patterns that are invaluable for both new and experienced developers. Although some readers note that it may not cover extensive real-world scenarios or practical applications, many agree that its high-level models help frame various data modeling problems effectively. The book's clarity and scope allow for better comprehension of enterprise data modeling, aligning well with current industry practices, making it a useful addition to a professional's library and approach to data architecture.
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Pros
- High-level models suitable for various data problems
- Offers foundational principles of data modeling
- A great reference for database design
- Useful for both new and experienced developers
- Enhances understanding of data architecture
Cons
- May lack in-depth real-world examples
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