Graph-Based Relationship Engine for Learning Platform

To better model and query complex relationships between content, users, and learning paths, I designed and built a graph-based relationship engine as part of a backend redesign for a learning platform.

Instead of forcing relational patterns into a traditional SQL structure, I introduced Neo4j to represent entities like courses, lessons, quizzes, skills, and user progress as nodes and edges. This allowed us to answer advanced queries such as:

"What lessons are prerequisites for this course?"

"Which users are struggling with interconnected concepts?"

"Which content should be recommended next based on current path and skill gaps?"

The engine exposed a clean API layer over Cypher queries, integrated with the platform's core services via a lightweight GraphQL facade. This architecture made it easy to power intelligent recommendations, personalized content delivery, and adaptive learning paths.

Key Skills: Graph Databases, Neo4j, Cypher, GraphQL, Learning Analytics

Problem Solved: Enabled rich, performant querying of deeply interconnected learning content and user interactions—something that was too complex and slow with relational databases.