Data Modeling Master Class Training Manual 5th Edition: Steve Hoberman’s Best Practices Approach to Developing a Competency in Data Modeling - Softcover

Hoberman, Steve

Softcover

ISBN 10: 1935504886 ISBN 13: 9781935504887

Publisher: Technics Publications, 2014

Synopsis

This is the fifth 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 and prototyping
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 industry data models can give us a great head start
8. Use a series of templates for 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.

About this title

About the Author

Steve Hoberman balances the formality and precision of data modeling with the realities of building software systems with severe time, budget, and people constraints. In his consulting and teaching, he focuses on templates, tools, and guidelines to reap the benefits of data modeling with minimal investment. He taught his first data modeling class in 1992 and has educated more than 10,000 people about data modeling and business intelligence techniques since then, spanning every continent except Africa and Antarctica. Steve is the author of seven books on data modeling, including the bestseller Data Modeling Made Simple. He is the founder of the Design Challenges group, inventor of the Data Model Scorecard®, Conference Chair of the Data Modeling Zone conference, recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award, and highest rated presenter at Enterprise Data World 2014. Steve can be reached at me@stevehoberman.com, @DataMdlRockStar on Twitter, or through Steve Hoberman on Linked-In.

From the Inside Flap

This is the fifth 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 and prototyping 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 industry data models can give us a great head start 8. Use a series of templates for 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.

Edition details

• Publisher: Technics Publications
• Publication date: 2014
• Language: English
• ISBN 10: 1935504886
• ISBN 13: 9781935504887
• Binding: Paperback
• Edition number: 5
• Number of pages: 328