🐶 The PuppyGraph Origin Story: From Frustration to Innovation

Tired of complex #ETL pipelines holding back your data analytics? Meet PuppyGraph – the graph query engine turning relational data into actionable insights WITHOUT migration or restructuring.

🐾 The Origin Story: From ETL Frustration to Graph Innovation

What is PuppyGraph?

PuppyGraph is not a database — it’s a graph query engine 🧩 that revolutionizes how organizations analyze relationships in their data. Unlike traditional databases or graph systems, PuppyGraph doesn’t force you to migrate or restructure your data. Instead, it meets your data where it lives— SQL databases(PostgreSQL, MySQL), data warehouse (Snowflake, BigQuery), data lakes (Iceberg, Parquet, Databricks), or warehouses — and lets you query your relational data as a graph in real-time, bypassing costly ETL pipelines.

How It’s Different

PuppyGraph combines the familiarity of SQL with the power of graph analytics, making it ideal for fraud detection, cybersecurity, telemetry logs analytics, supply chain optimization, and enhancing AI accuracy with GraphRAG.

🚀 Why PuppyGraph Matters for Leaders

1️⃣ Speed Without Sacrifice

• Faster fraud detection, transitioned from an offline system to an online one at Coinbase (BusinessWire)

• 10-hop queries in 2.26s across 1B+ edges without ETL (Yahoo Finance)

2️⃣ Scalability Meets Simplicity

• Deploy via Docker/Kubernetes in <10 minutes

• Powers 40% fewer AI hallucinations via GraphRAG (VentureBeat)

3️⃣ Cost Efficiency

• 80%-90% lower TCO than traditional systems

• $5M seed funding fuels petabyte-scale solutions (Yahoo Finance)

🐾 The Origin Story: A Rescue Pup, Frustration, and a Lightbulb Moment

The Breaking Point

The journey began with Weimo Liu, a Computer Science Ph.D. graduate from George Washington University. Liu's time at TigerGraph exposed him to the immense potential of graph databases. However, he also witnessed a significant challenge: despite strong interest in graph technology, many users struggled to implement it effectively in production environments. Liu noted that "a lot of users showed strong interest in graphs, but most of them finally end in nothing."

This frustration with the traditional graph database🧩 approach and Liu's extensive experience at Google, where he contributed to the development of F1, a unified SQL query engine within Google, led Liu to envision a solution that would eliminate the burdens associated with data extraction, transformation, and loading (ETL) processes. He wanted to create a system that allowed users to query existing data without the complexities typically tied to specialized graph storage systems.

The Founding Team

Joining Liu in this endeavor were Danfeng Xu and Lei Huang. Xu brought nine years of experience from LinkedIn's infrastructure team, while Huang, another ex-Googler, was recognized as a three-time Google Code Jam world finalist.

Together with Zhenni Wu, an experienced go-to-market veteran that helped Dgraph to grow to 1M downloads/quarter and led Head of Marketing for Arcion which was acquired by Databricks for $100M, they formed a formidable founding team committed to revolutionizing graph analytics.

PuppyGraph's innovative approach centers around its ability to function as the first and only graph query engine that allows users to query their existing relational data as a graph without ETL. This capability simplifies data preparation and management by allowing teams familiar with SQL to seamlessly integrate graph analytics into their workflow.

The Spark

Since PuppyGraph operates at the same level as SQL query engines like Trino, users can query relational data in both SQL and graph formats without needing separate copies of the data.

This means you can continue using SQL for familiar tasks like aggregations and simple queries, while leveraging PuppyGraph for complex graph analytics—eliminating the steep learning curve that traditional graph databases impose, where even basic operations like GROUP BY or SELECT FROM must be rewritten in a graph query language.

Weimo’s vision was further solidified when they realized that existing data lakes could serve as a robust foundation for their engine. By leveraging open table formats like Apache Iceberg, Delta Lake and Hudi, PuppyGraph enables organizations to run complex graph queries directly on their existing data without needing extensive ETL processes.

Beyond storage efficiency, PuppyGraph is optimized for graph workloads with massively parallel processing and vectorized evaluation, ensuring fast computation even without traditional indexing and caching. While relational data warehouses fine-tune their query engines for SQL, PuppyGraph is purpose-built for graph analytics—delivering speed and scale where it matters most.

Puppy Love : The Name and Mascot

Interestingly, the name "PuppyGraph" has an endearing origin. A team member's puppy became the unofficial mascot during the early days of development. The founders felt that just as puppies bring joy and companionship, their technology could make graph analysis more approachable and delightful for users. Plus, they managed to secure the domain name PuppyGraph.com for just three dollars—a decision they fondly refer to as a "tail-waggingly good deal."

Rapid Growth and Recognition

Since its launch in March 2024, PuppyGraph has quickly gained traction in the industry, securing partnerships with major players like Coinbase and Clarivate. The company raised $5 million in seed funding led by defy.vc in November 2024, further validating its innovative approach to graph analytics.

With a focus on real-time analytics for applications such as fraud detection, cybersecurity, log analytics, and enhancing large language models (LLMs) with GraphRAG, PuppyGraph is poised to transform how enterprises leverage their data.

Ready to innovate?
👉Try PuppyGraph’s Free Developer Edition

References & Further Reading

1. PuppyGraph Technical Architecture — Datanami

2. ML Failure Rates — Forbes Tech Council

3. Weimo Liu’s Background — PuppyGraph Bio

4. And most of all, special thanks to the PuppyGraph visionary core 🧠 team:
   🐶 Weimo Liu (CEO) - Ex-Google F1 engineer turned graph disruptor
   🔧 Danfeng Xu (CTO) - LinkedIn infra veteran who scaled microservices
   ⚡ Lei Huang - 3x Google Code Jam finalist optimizing petabyte queries
   🎯 Zhenni Wu - Growth leader behind Arcion's $100M Databricks exit
   
   Very much appreciate the unprecedented access and spending valuable time for me to immerse myself and get to know these amazing people!
   
   Muchos gracias, mi amigos!