TL;DR
- High Performance: Purpose-built storage engine optimized for both graph and vector operations
- Graph-Vector Hybrid: Store both graph and vector data in the same database (less overhead than separate databases)
- Query Language: Designed for traversing and manipulating graph and vector data efficiently
- Type Safety: Explicit type definitions to ensure data integrity
- Developer-Friendly: Simple setup and migration
- Secure: Options for encryption at rest
Data Model
- Nodes: entities with unique identifiers and properties
- Vectors – entities with unique identifiers and vector embeddings
- Edges - directed relationships between nodes and/or vectors, carrying properties
Common Use Cases
- Social Networks – friend graphs, content sharing, community detection
- Recommendation Engines – product/content suggestions, “people you may know”, interest matching
- Fraud Detection – transaction pattern recognition, suspicious-relationship analysis, risk scoring
- Knowledge Graphs – semantic search, data integration, ML feature store, research discovery
When to Use a Graph Database
- Data contains dense, interconnected relationships
- Queries require multi-hop traversals or pattern matching
- Traditional SQL joins become complex or slow
- Hierarchical structures or path finding dominate access patterns
Benefits Over Traditional Databases
- Performance: Faster for relationship-heavy queries
- Flexibility: Easier to modify and extend the data model
- Intuitive: More natural way to model connected data
- Scalability: Better handling of complex relationship patterns