This is the eighth 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 modelling 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 modelling 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. Three case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects. Steve Hoberman has trained over 10,000 people in data modelling since 1992.

Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organisations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognised as the most comprehensive data modelling course in the industry. Steve is the author of nine books on data modelling, including the bestseller Data Modeling Made Simple. Steve is also the author of Blockchainopoly. One of Steves frequent data modelling 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 Objectives: 1. Determine how and when to use each data modelling component; 2. Apply techniques to elicit data requirements as a prerequisite to building a data model; 3.

Build relational and dimensional conceptual, logical, and physical data models; 4. Incorporate supportability and extensibility features into the data model; 5. Assess the quality of a data model.