What are Graph Databases?

Graph databases are specialized databases designed to store, manage, and query data that is represented as graphs. Unlike traditional relational databases that use tables to store data, graph databases use nodes, edges, and properties to represent and store data. Nodes represent entities (such as people, products, or locations), edges represent relationships between entities, and properties store information about nodes and edges. Graph databases are particularly well-suited for applications that involve complex relationships and interconnected data, such as social networks, recommendation engines, fraud detection, and network analysis. Compare and read user reviews of the best Graph Databases currently available using the table below. This list is updated regularly.

  • 1
    KgBase

    KgBase

    KgBase

    KgBase, or Knowledge Graph Base, is a collaborative, robust database with versioning, analytics & visualizations. With KgBase, any community or individual can create knowledge graphs to build insights about their data. Import your CSVs and spreadsheets, or use our API to work on data together. Build no-code knowledge graphs with KgBase, our easy-to-use UI lets you traverse the graph, show the results as tables and charts, and much more. Play with your graph data. Build your query and see results update in real time. It's like writing query code in Cypher or Gremlin, except easier. And fast. Your graph can be viewed as a table, allowing you to browse all results - no matter the size. KgBase works great with large graphs (millions of nodes), as well as simple projects. In the cloud, or self-hosted, with wide database support. Introduce graphs into your organization by seeding graph from a template. Results of any query can be easily turned into a chart visualization.
    Starting Price: $19 per month
  • 2
    Fluree

    Fluree

    Fluree

    Fluree is an immutable RDF graph database written in Clojure and adhering to W3C standards, supporting JSON and JSON-LD while accommodating various RDF ontologies; it boasts a scalable, cloud-native architecture utilizing a lightweight Java runtime, with individually scalable ledger and graph database components, embodying a "Data-Centric" ideology that treats data as a reusable asset independent of singular applications, underpinned by an immutable ledger that secures transactions with cryptographic integrity, alongside a rich RDF graph database capable of various queries, and employs SmartFunctions for enforcing data management rules, including identity and access management and data quality.
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