02 May TigerGraph: The parallel graph database explained
Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both performance and analysis capabilities—when the volume of data grows very large, and when the answers must be provided in real time.
That’s because existing graph technologies have trouble loading large quantities of data, or ingesting fast-arriving data, in real time. They also struggle to deliver fast traversal speed. While deeper analytics require deeper traversal of the graph, today’s graph databases typically slow down or time out after two hops of traversal.
Learn more about TigerGraph on InfoWorld.