PuppyGraph is the first and only real time, zero-ETL graph query engine in the market, empowering data teams to query existing relational data stores as a unified graph model that deployed in under 10 minutes, bypassing traditional graph databases' cost, latency, and maintenance hurdles. Capable of scaling with petabytes of data and executing complex 10-hop queries in seconds, PuppyGraph supports use cases from enhancing LLMs with knowledge graphs to fraud detection, cybersecurity and more. Trusted by industry leaders, including Coinbase, AMD, Netskope, Palo Alto Network, eBay, and more.

How does PuppyGraph compare to Neo4j?

Unlike Neo4j, which requires you to load and sync data into its proprietary graph store, PuppyGraph runs directly on your data sources—eliminating ETL, reducing TCO, and enabling faster time-to-value. PuppyGraph also integrates natively with Databricks Unity Catalog, Google BigQuery, and AlloyDB.

What are the performance benefits of PuppyGraph?

PuppyGraph delivers multi-hop traversals in seconds over billions of edges. Real customer stories cite 5-hop queries on 1B+ edges in under 3 seconds.

Does PuppyGraph support my cloud data stack?

Yes. PuppyGraph natively integrates with Databricks Unity Catalog, Google BigQuery, AlloyDB, and AWS, keeping a single governed copy of your data.

How does PuppyGraph handle data governance and security?

PuppyGraph leverages your existing catalog and security (Unity Catalog, BigQuery, AlloyDB), so all graph queries respect your current access controls.

Can PuppyGraph power AI and LLM applications (GraphRAG)?

Yes. PuppyGraph enables Graph-based Retrieval Augmented Generation (GraphRAG) directly on your governed data—providing explainable, multi-hop context for LLMs and enterprise AI.

Hao Wu

Hao Wu is a Software Engineer with a strong foundation in computer science and algorithms. He earned his Bachelor’s degree in Computer Science from Fudan University and a Master’s degree from George Washington University, where he focused on graph databases.