Graph databases, which explicitly express the connections between nodes, are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. That gives graph databases a leg up for applications such as fraud detection and recommendation systems.

One of the major draws of graph databases is the ability to run graph computational algorithms. These are used for tasks that don’t lend themselves well to relational databases, such as graph search, pathfinding, centrality, PageRank, and community detection. Graph algorithms are mostly supported in analytical (OLAP and HTAP) graph databases, although some transactional (OLTP) graph databases such as Neo4j support them.

All of the graph databases discussed here have good horizontal scalability. Some also support read replicas, global distribution, and automatic horizontal sharding.

Amazon Neptune

is a fully managed transactional (OLTP) graph database service with ACID properties and immediate consistency, which has at its core a purpose-built, high-performance graph database engine that is optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports two of the most popular open source graph query languages, Apache TinkerPop Gremlin and W3C SPARQL.