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Data Model Patterns: Conventions of Thought (Dorset House eBooks)

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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 thos 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


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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 thos 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

30 review for Data Model Patterns: Conventions of Thought (Dorset House eBooks)

  1. 5 out of 5

    Pavels Kletnojs

    This book is extremely boring and extremely interesting at the same time.

  2. 5 out of 5

    Graham

    Recommended on Scott Amblers Agile Data site

  3. 5 out of 5

    Vladyslav Bondarenko

    I found this book rather difficult. There are very interesting concepts, but in order to utilize them properly you have to be an advanced developer.

  4. 5 out of 5

    Chad Dinerman

  5. 4 out of 5

    Paul Bond

  6. 4 out of 5

    Brett

  7. 4 out of 5

    Jim Butler

  8. 4 out of 5

    Rosey Fergusson

  9. 5 out of 5

    JDM

  10. 5 out of 5

    Jason Benedict

  11. 4 out of 5

    Travis Bement

  12. 4 out of 5

    Bojan Nedelkovski

  13. 4 out of 5

    Scot

  14. 5 out of 5

    Warren F

  15. 5 out of 5

    Smith Powell

  16. 5 out of 5

    Todd Everett

  17. 5 out of 5

    Chris

  18. 5 out of 5

    geoff w miller

  19. 4 out of 5

    Malkotigmail.Com

  20. 5 out of 5

    Dave Wright

  21. 4 out of 5

    Benoît Fleury

  22. 5 out of 5

    Anton

  23. 4 out of 5

    Bruce McCartney

  24. 4 out of 5

    Bojan Nedelkovski

  25. 4 out of 5

    Rosey Fergusson

  26. 5 out of 5

    Daniel Scott

  27. 5 out of 5

    Tatiana

  28. 5 out of 5

    David Medinets

  29. 4 out of 5

    John

  30. 5 out of 5

    John

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