What does “infrastructure” mean when your data stays exactly where it is, yet suddenly behaves like a graph?

I met Weimo Liu, CEO and co-founder of PuppyGraph, during an IT Press Tour presentation, and I wanted to bring his story to Infrastructure As A Conversation because this is a data infrastructure conversation at its core. Weimo’s pitch is simple to say and harder to pull off: keep a single copy of data in your lake or warehouse, skip the ETL pipelines, and still run graph queries with subsecond performance.

Weimo’s background explains why this is more than a clever demo. He worked at TigerGraph, then on Google’s F1 team, and PuppyGraph sits right between those worlds. In our conversation, he walks me through how they treat graph queries as a set of node and edge operations that can be optimized, parallelized, and evaluated in a vectorized way, which is how they keep performance predictable when workloads get real.

We also get into the practical details infrastructure teams care about. PuppyGraph is a read-only engine, which changes the trade-offs around concurrency, governance, and operational risk. Instead of copying data into a separate graph store and building a second set of controls, you can query relationships where the data already lives, then write results back into the lake for other engines to consume. The upside is simpler architecture and less duplication. The compromise is that you are not getting transactional graph updates, and Weimo is clear about why that is acceptable for the OLAP-style workloads his customers run.

From there, the use cases start to make sense fast. Cybersecurity teams with logs sitting in object storage, fraud detection scenarios where latency matters, and internal AI chatbots that struggle with too many tables and brittle SQL generation. Weimo has a sharp analogy for that last part, text-to-graph queries behave more like a train on rails, which can help AI stay inside defined relationships and reduce messy answers.

If you are building modern data platforms and you are tired of pipelines multiplying, this episode is a thought-provoking look at what happens when graph analytics becomes a query layer rather than a destination system. And it all started with a dog-themed name and a surprisingly cheap domain.