The second part of the "how to model data" series explores different types of logical data models, providing examples and analyzing their strengths and weaknesses. It builds on the first part, which covered conceptual, logical, and physical steps in data modeling. The text describes common logical data models such as relational, JSON, key-value, graph, wide-column, and text search models, explaining their unique features, use cases, and associated query languages. Relational models, for instance, are the most popular and rely heavily on SQL, while JSON models are favored for their scalability and flexibility in NoSQL environments. Tools for data modeling like JetBrains DataGrip, Erwin Data Modeler, Hackolade, and Idera are mentioned as resources to facilitate the process. The series aims to provide a comprehensive understanding of data modeling, with the next installment set to focus on physical JSON data modeling.