Publisher's Synopsis
Steve Hobermans Best Practices Approach to Developing a Competency in Data ModelingThis is the ninth edition of the training manual for the Data Modeling Master Class that Steve Hobermanteaches 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 forproducing conceptual, logical, and physical relational and dimensional and NoSQL data models. Afterlearning the styles and steps in capturing and modeling requirements, you will apply a best practicesapproachto building and validating data models through the Data Model Scorecard®. You will know not justhow to build a data model, but how to build a data modelwell. Three case studies and many exercises reinforcethe material and will enable you to apply these techniques in your current projects. Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. 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 Steves frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, lecturer at Columbia University, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award. Top 5 Objectives1.Determine how and when to use each data modeling component2.Apply techniques to elicit data requirements as a prerequisite to buildinga data model3.Build relational and dimensional conceptual, logical, and physical data models4.Incorporate supportability and extensibility features into the data model5.Assess the quality of a data model.
