ER for identity graphs is a great use case! We see teams do this a lot and with not-great tools. (Ex: users/IPs in splunk/elastic, which are better for simpler matches.)
For one Graphistry project, we run a single node neo4j with 0.5b nodes/edges, so something in the description isn't adding up for me here wrt perf. Maybe an open benchmark would help?
I do agree indexing matters, as that was night/day for our use cases. For ML workloads, we are looking at vector indexes, which graph DBs do not currently support. The ones in this article are on text and take > 100ms, so I'm curious..
For one Graphistry project, we run a single node neo4j with 0.5b nodes/edges, so something in the description isn't adding up for me here wrt perf. Maybe an open benchmark would help?
I do agree indexing matters, as that was night/day for our use cases. For ML workloads, we are looking at vector indexes, which graph DBs do not currently support. The ones in this article are on text and take > 100ms, so I'm curious..