The best graph databases

These stellar databases combine horizontal scalability with highly efficient engines for storing and analyzing connected data

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

To continue reading this article register now