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.

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Use case

Telecom Customer 360 Graph Demo

This demo shows how to use PuppyGraph to spot churn risk early and improve customer satisfaction in a telecom support scenario. Power your customer support interactions with more accurate and faster insights with PuppyGraph. No ETL or data duplication required.

Tech stack

Docker

PuppyGraph

Queries in natural language

Find how many churned customers each non-churned customer has contacted via phone calls, and sort customers by that count in descending order
Identify the top 5% of phone numbers by number of calls made, then retrieve all support tickets filed by the customers who own those phone numbers
Find all pairs of different customers who both filed escalated support tickets and have called each other
Identify family plans where none of the members have made phone calls to any other member within the same plan
For each family plan, count the number of calls made between different members of the same plan, and sort the plans by this count in descending order