I loaded this same IMDB dataset into a Kuzu DB the other day, just for fun. It was an interesting project but I would agree that this dataset in particular lends itself to a graph database. Kuzu started to choke with > 10M nodes & 10M edges but I was doing a naive insert & probably could fit the entire dataset in without trouble using their direct CSV import.
Interesting points here. I’ve found the “graph DB vs. relational DB” discussion usually gets framed as an either/or, but there’s a middle ground.
A lot of teams already have their data sitting in Postgres, Mongo, or a lakehouse. Spinning up a separate graph database just for traversals often means duplicating data, building pipelines, and keeping two systems in sync. That’s fine if you need deep graph algorithms, but for many workloads it’s overkill.
What some folks are exploring now is running graph queries directly on top of their existing data, without having to ETL into a dedicated graph DB. You still get multi-hop traversal and knowledge graph use cases, but avoid the “yet another database” tax.
So yeah...graph databases are great, but they’re not the only way to model or query graphs anymore.
