Summary
Graph databases treat relationships as first-class citizens, making connected data queries natural and performant.
Core concepts:
- Property graph model: nodes (entities) + relationships (connections)
- Cypher query language for pattern matching
- Built-in graph algorithms
Use cases: Social networks, fraud detection, recommendation engines, knowledge graphs
vs. Relational: Graphs excel at deep traversals and relationship queries; relational wins at aggregations and transactions.
When to use: Multi-hop queries, flexible schema, relationship-centric data model.