Data Modeling Master Class Training Manual 4th Edition: Steve Hoberman's Best Practices Approach to Understanding and Applying Fundamentals Through Advanced Modeling Techniques

Editor

Technics Publications, LLC

Fecha de publicaci�n

2012

Idioma

Ingl�s

ISBN 10

193550441X

ISBN 13

9781935504412

Encuadernaci�n

Paperback

Edici�n

4� Edici�n

Estado

Very Good

Estado de la sobrecubierta

No Jacket

Cat�logos de vendedores

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Sinopsis

This is the fourth 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 three 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. Two 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…

Explain data modeling building blocks and identify these constructs by following a question-driven 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
Apply requirements elicitation techniques including interviewing and prototyping
Build relational and dimensional conceptual, logical, and physical data models through two case studies
Practice finding structural soundness issues and standards violations
Recognize situations where abstraction would be most valuable and situations where abstraction would be most dangerous
Use a series of templates for capturing and validating requirements, and for data profiling
Express how to write clear, complete, and correct definitions
Leverage the Grain Matrix, enterprise data model, and available industry data models for a successful enterprise architecture.

Acerca del autor

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his ability to translate from business requirements to technical specifications. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. Steve is also the author of Blockchainopoly. One of Steve's frequent data modeling consulting assignments is to review BTMs using his Data Model Scorecard� technique. He is the creator of the Data Modeling Institute's Data Modeling Certification exam, Data Modeling Zone Conference Chair, lecturer at Columbia University, and recipient of the Data Administration Management Association International Professional Achievement Award.

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