3412: PuppyGraph at the IT Press Tour: Graph Power Without the Pain

Sep 6, 2025

Weimo Liu, CEO and co-founder of PuppyGraph, brings a fresh perspective on graph technology for enterprises with a 'zero ETL' architecture. He explains how this innovative approach allows companies to query existing data seamlessly, eliminating time-consuming data movements. Weimo highlights the potential of graph analytics in fields like cybersecurity and AI, emphasizing improved latency and accuracy. With PuppyGraph's user-friendly design, traditional SQL teams can adapt quickly, transforming complex data processes into streamlined solutions.

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INSIGHT

No-ETL Graph Querying

PuppyGraph merges graph DB experience with Google's F1 to query data without moving it.
This 'no ETL' approach removes a major blocker to enterprise graph adoption.

INSIGHT

Operator-Based Query Engine

PuppyGraph defines node and edge actions to express any graph query as composable operators.
They optimize cost, parallelize, vectorize and scale across machines for subsecond performance.

ADVICE

Map Tables To Logical Graphs

Map existing tables to node and edge types instead of copying data into a graph database.
Keep the physical tables and use a logical graph schema to avoid costly reloads and schema churn.

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During the IT Press Tour, I had the pleasure of speaking with Weimo Liu, CEO and co-founder of PuppyGraph, and hearing firsthand how his team is rethinking graph technology for the enterprise.

In this episode of Tech Talks Daily, Weimo joins me to share the story behind PuppyGraph’s “zero ETL” approach, which lets organizations query their existing data as a graph without ever moving or duplicating it.

We discuss why graph databases, despite their promise, have struggled with mainstream adoption, often because of complex pipelines and heavy infrastructure requirements. Weimo explains how PuppyGraph borrows from his time at TigerGraph and Google’s F1 engine to build something new: a distributed query engine that maps tables into a logical graph and delivers subsecond performance on massive datasets. That shift opens the door for use cases in cybersecurity, fraud detection, and AI-driven applications where latency and accuracy matter most.

We also unpack the developer experience. Instead of rewriting schemas or reloading data every time requirements change, PuppyGraph allows teams to define nodes and edges directly from existing tables. That design lowers the barrier for SQL-focused teams and accelerates time to value. Weimo even touches on the role of graph in reducing AI hallucinations, showing how structured relationships can make enterprise AI systems more reliable.

What struck me most in our conversation is how PuppyGraph’s playful branding belies its serious engineering depth. Behind the “puppy” name lies a distributed engine built to scale with today’s data volumes, backed by strong early adoption and a team that listens closely to customer needs. Whether you’re exploring graph for cybersecurity, AI chatbots, or supply chain analytics, this discussion offers a glimpse of how the next generation of graph tech might finally break free from its niche and go mainstream.

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