zw17

If your use case is OLAP based, please check it out PuppyGraph. It’s a graph query engine that sits on top of your Lakehouse (no ETL required). Our benchmark has shown consistently that 10-hop queries across billions of edges in <2 seconds. Our customers including some most data demanding companies like Coinbase, Datadog, Palo Alto Network, Netskope, AMD, etc.

Thank you so much for liking our blog! This is Zhenni, cofounder of PuppyGraph. Happy to answer any questions.

Just want to chime in. Zhenni, cofounder of PuppyGraph. We created the first graph query engine that can sit on top of your relational databases (think Postgres, Iceberg, Delta lake, etc.), and query your relational data as a graph using Cypher and Gremlin, without any ETL or a separate graphdb needed. It's much more lightweight and easy to spin up. Because we sit on top of column based storage and our compute engine is distributed, we can achieve subsecond query speed across 1 billion nodes. Please check it out!

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.