Eclipse RDF4J
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| Fact | Eclipse RDF4J | FalkorDB |
|---|---|---|
| Documentation depth | Documentation includes getting-started tutorials, programming guides, reference material, tool manuals, release notes and JavaDoc links. | The documentation covers setup, graph modeling, querying, indexes, protocols, clients, GraphRAG and Docker ports 6379 and 3000. |
| Founded | 2015 | LinkedIn lists FalkorDB's founding year as 2023. |
| Integrations | Documented compatible systems include GraphDB, Halyard, Stardog, Amazon Neptune, Blazegraph, Oracle RDF, MarkLogic, Strabon and Virtuoso. | Official materials list Graphiti, LangChain, LlamaIndex, Cognee, g.v(), OpenAI, Gemini, Google Cloud, Azure, Grafana, Docker, Snowflake and Model Context Protocol integrations or ecosystem connections. |
| Intended users | The site presents RDF4J as a framework for Java developers building applications with RDF, Linked Data, SPARQL endpoints, and Semantic Web data. | The company describes FalkorDB as designed for development teams working with interconnected data in real-time or interactive environments. |
| License | The project states that Eclipse RDF4J is licensed under the Eclipse Distribution License version 1.0. | The documentation states FalkorDB is licensed under Server Side Public License v1 (SSPLv1). |
| Security | The Eclipse project page links to the Eclipse Foundation vulnerability reporting channel. | The Trust Center says customer data is encrypted at rest and in transit, with encryption keys rotated annually. |
| Self-hosting | RDF4J Server and Workbench are deployable as web applications, with documentation covering Tomcat installation and repository configuration. | Users can run FalkorDB locally with Docker or self-host it with an enterprise management console. |
| Storage | RDF4J includes a transactional in-memory store with optional disk persistence and a transactional Native Store that targets medium-sized datasets around 100 million triples. | FalkorDB stores graphs as sparse adjacency matrices for compact storage and fast traversal. |
It suits Java developers building applications with RDF, Linked Data, SPARQL endpoints, or Semantic Web data. Consider its stated dataset focus and Java runtime requirement when choosing a store.
FalkorDB is aimed at development teams working with interconnected data in real-time or interactive environments. It may suit teams building graph-based AI applications that need OpenCypher, vector search, or listed integrations.