Graph Databases
A list of databases and data stores specialized for Graph Databases.
| No | Name | Stars | Description | Trend | License | Language | Official Site |
|---|---|---|---|---|---|---|---|
| 1 | Amazon Neptune | - | AWS fully managed graph database. Supports both PropertyGraph (Gremlin) and RDF (SPARQL). Provides high availability and automatic scaling. | Growing with cloud-first graph DB demand. Expanding enterprise adoption through integration with AWS machine learning services. Gaining attention due to operational load reduction needs. | Commercial | Gremlin/SPARQL | Official |
| 2 | ArangoDB | - | Multi-model database. Provides document, graph, and key-value in single system. Enables efficient manipulation of complex data relationships through AQL query language. | Differentiated by multi-model integration demand. Adopted by startups and SMEs to avoid system complexity. Improved competitiveness through performance improvements and cloud service enhancement. | Apache 2.0/Commercial | AQL/Various | Official |
| 3 | Neo4j | - | World's most popular graph database. Enables intuitive relational data operations through Cypher query language. Optimal for social networks, recommendation systems, and fraud detection. | Rapidly expanding demand in AI/machine learning fields. GraphRAG gaining attention through knowledge graph construction and LLM integration. Increasing adoption for fraud detection and risk analysis in finance and healthcare industries. | GPL v3/Commercial | Cypher/Various | Official |
| 4 | OrientDB | - | Open source multi-model database. Integrates document, graph, object, and key-value models. Provides flexible data management in Java environments. | Maintains adoption in small to medium-scale projects. Challenges include community edition limitations and high enterprise edition costs. Struggling in competition with PostgreSQL and Neo4j. | Apache 2.0/Commercial | SQL/Various | Official |
| 5 | TigerGraph | - | Ultra-fast graph analytics platform. Design specialized for real-time analytics and machine learning. Achieves fast query processing on large-scale graph data. | Growing with graph analytics demand in large enterprises. Valued for real-time fraud detection and network analysis in financial services and telecommunications. Strengthened competitiveness with AI integration features. | Commercial | GSQL/Various | Official |