Only a few things are more satisfying for a graph data scientist than playing with Neo4j Graph Data Science library algorithms, most probably running them in production and at scale. Possibly also using them to fight against scammers and fraudsters that every day threatens your business.
This is the second of a two post series on monitoring the Neo4j graph database with popular enterprise solutions such as Prometheus and Grafana. Monitoring the status and performance of connected data processes is a crucial aspect of deploying graph based applications. In Part 1 we have seen how to expose the graph database internals and custom metrics to Prometheus, where they are stored as multi-dimensional time series.
Enterprise IT requirements are demanding and solutions are expected to be reliable, scalable, and continuously available. Databases accomplish this through clustering, the ability of several instances to connect and conceptually appear and operate as a single unit.