Graph Databases
Discover relationships and connections in your data with databases designed for highly interconnected information
What Are Graph Databases?
Graph databases store data as nodes (entities) and edges (relationships). Unlike traditional databases that use tables and joins, graph databases excel at representing and querying complex relationships.
Each node can have properties, and edges define typed, directed relationships between nodes. This structure makes it natural to model social networks, recommendation engines, fraud detection, and knowledge graphs.
Popular Graph Databases
Neo4j
The most popular graph database with native graph storage. Uses Cypher query language for intuitive pattern matching.
Amazon Neptune
Fully managed graph database supporting both property graphs (Gremlin) and RDF graphs (SPARQL).
ArangoDB
Multi-model database combining graph, document, and key-value stores with a unified query language (AQL).
JanusGraph
Open-source, scalable graph database optimized for storing and querying massive graphs with billions of vertices.
Key Features
Relationship-First Design
Relationships are first-class citizens, not afterthoughts. Traverse connections at blazing speed.
Fast Traversals
Query multi-hop relationships without expensive joins. Performance doesn't degrade with depth.
Pattern Matching
Discover complex patterns and paths in your data with intuitive query languages like Cypher.
Natural Modeling
Model real-world relationships directly without forcing them into tables and foreign keys.
Common Use Cases
- ✓Social Networks: Model friendships, followers, and connections
- ✓Recommendation Engines: Discover "people who bought this also bought"
- ✓Fraud Detection: Identify suspicious patterns and transaction rings
- ✓Knowledge Graphs: Build semantic relationships and ontologies
- ✓Network Analysis: IT infrastructure, supply chain, telecommunications
- ✓Identity & Access Management: Complex permission hierarchies
When to Use Graph Databases
✓ Choose Graph Databases when:
- Your queries frequently traverse multiple relationships
- You need to discover patterns and connections
- Relationship depth varies and is unpredictable
- You're building social features or recommendation systems
✗ Avoid Graph Databases when:
- You primarily need simple key-value lookups
- Your data has few or no relationships
- You need heavy aggregations on large datasets
- Complex reporting and analytics are the primary use case
Explore Your Data Relationships
Use our database finder to see if a graph database is right for your use case