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Data Modeling Master Class Training Manual 3rd Edition:
Steve Hoberman's Best Practices Approach to Understanding
and Applying Fundamentals Through Advanced Modeling
Techniques
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Data Modeling Master Class Training Manual 3rd Edition:
Steve Hoberman's Best Practices Approach to Understanding
and Applying Fundamentals Through Advanced Modeling
Techniques
BOOK DETAIL
Paperback: 462 pages Publisher: Technics Publications, LLC; Third edition (August 12, 2011) Language:
English ISBN-10: 1935504169 ISBN-13: 978-1935504160 Product Dimensions: 8.5 x 1 x 11 inches
Shipping Weight: 2.9 pounds (View shipping rates and policies) Customer Reviews:
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Book Description
This is the third edition of the training manual for the Data Modeling Master Class that Steve
Hoberman teaches onsite and through public classes. This text can be purchased prior to attending
the Master Class, the latest course schedule and detailed description can be found on Steve
Hoberman's website, stevehoberman.com. The Master Class is a complete course on requirements
elicitation and data modeling, containing four days of practical techniques for producing solid
relational and dimensional data models. After learning the styles and steps in capturing and
modeling requirements, you will apply a best practices approach to building and validating data
models through the Data Model Scorecard®. You will know not just how to build a data model,
but also how to build a data model well. Three case studies and many exercises reinforce the
material and enable you to apply these techniques in your current projects. By the end of the
course, you will know how to: Apply requirements elicitation techniques including interviewing
and prototyping Explain data modeling constructs and employ the "6 Questions" approach to
ensure model precision Demonstrate reading a data model of any size and complexity with the
same confidence as reading a book Validate any data model with key "settings" (scope,
abstraction, timeframe, function, and format) as well as through the Data Model Scorecard®
Practice finding structural soundness issues and standards violations Build relational and
dimensional subject area, logical, and physical data models Recognize situations where
abstraction would be most valuable and situations where abstraction would be most dangerous
Use a series of templates for scoping and validating requirements, and for data profiling Express
how to write clear, complete, and correct definitions Describe the two reasons an enterprise data
modeling project can fail, and the factors that must be in place for the enterprise data model to
succeed

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