How does graph database performance compare to relational databases for connected queries?

Graph databases outperform relational databases for multi-hop traversal queries (friends-of-friends, path finding, influence analysis) that would require multiple self-joins in SQL. For 3+ hop traversals over millions of nodes, graph databases can be 100–1000x faster than equivalent SQL joins.

What query languages do enterprise graph databases support?

Neo4j uses Cypher (also used by AWS Neptune and RedisGraph). Apache TinkerPop Gremlin is supported by AWS Neptune, Azure Cosmos DB, and JanusGraph. SPARQL is used for RDF-based knowledge graphs in Neptune and GraphDB. openCypher is an open standard adopted by multiple vendors for broader interoperability.

What are the top use cases driving enterprise adoption of managed graph databases?

Leading use cases include fraud detection in financial services (identity linkage, transaction graph analysis), knowledge graphs for enterprise search and recommendation, supply chain relationship mapping, identity and access management (IAM) authorization graphs, and network/IT infrastructure topology analysis.

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Best Solutions for Managed Graph Database

Expert evaluation of the best managed graph database solutions for connected data, including AWS Neptune, Neo4j AuraDB, TigerGraph, Azure Cosmos DB Gremlin API, and alternatives.

Frequently Asked Questions

AWS Neptune supports both Property Graph (Gremlin, openCypher) and RDF (SPARQL) models, widely used for fraud detection and knowledge graphs. Neo4j AuraDB Enterprise provides the most mature graph data science library with graph algorithms (PageRank, community detection) built-in. TigerGraph excels for real-time deep link analysis.

Tags:managed graph databaseAWS NeptuneNeo4j AuraDBTigerGraphgraph analyticsknowledge graph