Data Modeling Master Class Training Manual 3rd Edition: Steve Hoberman's Best Practices Approach to Understanding and Applying Fundamentals Through ... Through Advanced Modeling Techniques - Tapa blanda

Sinopsis

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:

1. Apply requirements elicitation techniques including interviewing and prototyping
2. Explain data modeling constructs and employ the "6 Questions" approach to ensure model precision
3. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book
4. Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard®
5. Practice finding structural soundness issues and standards violations
6. Build relational and dimensional subject area, logical, and physical data models
7. Recognize situations where abstraction would be most valuable and situations where abstraction would be most dangerous
8. Use a series of templates for scoping and validating requirements, and for data profiling
9. Express how to write clear, complete, and correct definitions
10. 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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