Query your relational data as a graph in real-time. Zero ETL.

PuppyGraph empowers you to seamlessly query one or multiple data stores as a unified graph model.

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deploy to query in

10 Mins

10-hop query across 600M edges in

2.26 Secs

in production for data volume of

5-10PB

Say goodbye to complex ETL and specialized storage

PuppyGraph is a very interesting graph query engine. It doesn’t require us to load or ETL any of the data into a specialized or proprietary database storage layer for graphs. PuppyGraph queries all our transactional and crypto data directly and performs all kinds of graph-based exploration and aggregation. It’s so powerful, and our users really enjoy this type of flexibility.

Eric Sun

Sr. Manager of Data Platform at Coinbase, at Data+AI Summit 2024

PuppyGraph’s relationship engine has been instrumental in building our correlation platform, enabling seamless connectivity and insights. The team’s efficiency, fast response, and proactive engagement have made a significant impact, providing us with valuable guidance every step of the way.

Naiming Chu

Vice President,
Infrastructure and Platform Operations, Netskope

PuppyGraph’s plug and play design is exceptionally user-friendly for newcomers to graph technology. We experienced virtually no learning curve, swiftly showcasing our first demo to the leadership team. Moreover, PuppyGraph did not increase our system complexity because it eliminated the need for data duplication, making the daily maintenance cost very low.

Po-Chun Chang

Engineering Manager, Protocol Labs

After evaluating a few different graph solutions, we struggled to find one that could scale to our data needs. PuppyGraph’s approach, decoupling the query engine from the storage, enabled us to connect to our data files and perform analytics across different data categories, this could assist with unlocking insights and new possibilities.

Hongliang Liu

Director of Data Science at Clarivate

We found that GPEs like PuppyGraph—especially in cached mode—excel at analytical and summarization use cases, outperforming most graph databases by a wide margin.

Namith Cholappurath

Head of Architecture, PrevalentAI

Natively integrating with all your favorite data sources

Deploy to query in 10 minutes

Step 1

Launch PuppyGraph in Docker

Step 2

Connect PuppyGraph to a relational data source

Step 3

Create graph schema

Step 4

Query your data in Gremlin or Cypher

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Single copy of data - query it in both SQL & Graph

Why choose between SQL and Graph? Prepare, aggregate, and manage data using SQL, and dive into graph queries for intricate traversals.

Why PuppyGraph

Zero ETL

Query your data as a graph directly from data warehouses and lakes, eliminating complex ETL pipelines and delays with PuppyGraph.

Petabyte-level scalability

PuppyGraph conquers scalability by auto-sharding across data sources, letting you graph-query petabytes of data directly from your warehouses and lakes without traditional constraints.

Complex queries in seconds

PuppyGraph can deliver a 10-hop neighbor query in 2.26 seconds across billions of edges with a 4-node cluster with our parallel processing and vectorized evaluation technology.

Deploy to query in 10 mins

As the first and only graph query engine, PuppyGraph eliminates onboarding hassles that comes with a graph database, letting you deploy and start querying within just 10 minutes.

Free Download

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Tailored insights for every industry

From AI to finance to pharma, see how PuppyGraph unleashes industry-specific powers.