Graph Processing v. Graph Databases – Jim Webber

Graph processing is a technique used to analyze and process large amounts of data that are stored in a non-relational format, typically in the form of nodes and edges. Unlike graph databases, which focus on storing and querying connected data for online transaction processing, graph processing platforms like Pregel or Hadoop are better suited for niche problems where high latency can be tolerated in exchange for higher throughput. Graph databases, such as Neo4j, optimize storage and querying of connected data for online transaction processing scenarios, providing deep insights in near real-time at enterprise scale. The choice between graph processing and graph databases depends on the specific needs of the application, including requirements for insight, latency, and scalability.

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