Principles of Graph Data Modelling

3.1 Principles of Graph Data Modelling • Think in relationships, not tables • Identifying entities, relationships, and properties • Common modelling patterns: linked lists, trees, bipartite graphs Check out our full course at Udemy – https://www.udemy.com/course/knowledge-graphs-for-enterprise-ai-a-practical-guide Master Graph Data Modeling for Enterprise AI! 🚀 Welcome to Module 3! Knowing how to store data is one thing, but designing an intuitive, queryable, and performant graph structure is what separates good practitioners from great ones . In this video, we shift from relational "table" thinking to modeling real-world relationships . 🔍 What You'll Learn in This Video: 🧠 Graph Thinking: Learn the golden rule: model what you query, not what you store, and let your most important business questions drive your structure . 🏗️ Nodes, Relationships, & Properties: Master a simple decision framework to know exactly when an entity should be a node, a descriptive verb should be a relationship, or an attribute should be a property ⚡ Designing for Performance: Discover index strategies, avoid anti-patterns like making everything a node , and master the PROFILE and EXPLAIN Cypher commands 🎯 Common Graph Patterns: Learn to easily spot reoccurring structural shapes like Linked Lists for timelines, Trees for hierarchies , and Bipartite graphs for recommendation engines 💼 Domain-Driven Design: Walk through a structured 5-step process applied to real-world examples, including a pharmaceutical clinical trial graph and a financial portfolio graph 🔒 Schema & Constraints: Learn how Neo4j enforces data quality through constraints and when to leverage a strict schema versus a flexible approach Stop reaching for complex database joins and start building powerful traversal paths! 👍 If you found this helpful, please like, subscribe, and share it with your team! Let us know your data modeling questions in the comments below.