Data Modeling Master Class Training Manual 6th Edition: Steve Hoberman’s Best Practices Approach to Developing a Competency in Data Modeling - Tapa blanda

Hoberman, Steve

 
9781634620901: Data Modeling Master Class Training Manual 6th Edition: Steve Hoberman’s Best Practices Approach to Developing a Competency in Data Modeling
Tapa blanda
ISBN 10: 1634620909 ISBN 13: 9781634620901
Editorial: Technics Publications, 2015

Sinopsis

This is the sixth 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 data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL 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 how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects.

Top 10 Objectives:

  1. Explain data modeling components and identify them on your projects by following a question-driven approach
  2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book
  3. Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard
  4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing
  5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions
  6. Practice finding structural soundness issues and standards violations
  7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start
  8. Use a series of templates for capturing and validating requirements, and for data profiling
  9. Evaluate definitions for clarity, completeness, and correctness
  10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture.

"Sinopsis" puede pertenecer a otra edición de este libro.

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.

De la solapa interior

This is the sixth 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 data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL 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 how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects.Top 10 Objectives 1. Explain data modeling components and identify them on your projects by following a question-driven approach 2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book 3. Validate any data model with key settings (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard® 4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing 5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions 6. Practice finding structural soundness issues and standards violations 7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start 8. Use a series of templates for capturing and validating requirements, and for data profiling 9. Evaluate definitions for clarity, completeness, and correctness 10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Technics Publications
Año de publicación
2015
Idioma
Inglés
ISBN 10 
1634620909
ISBN 13 
9781634620901
Encuadernación
Tapa blanda
Número de edición
6
Número de páginas
346
Contacto del fabricante
no disponible
Persona responsable
no disponible