PuppyGraph is a graph query engine that enables organizations to query all their data as a graph directly from data warehouses and lakes, without the need for traditional ETL pipelines. PuppyGraph eliminates the time-consuming steps usually required to move and transform data before it can be analyzed as a graph, allowing users to connect directly to their existing data infrastructure.
PuppyGraph's platform is designed to streamline the deployment and onboarding process: users can deploy and begin running graph queries in as little as 10 minutes, removing many of the barriers associated with adopting graph database technology. Its engine supports seamless integration with third-party tools and does not require data or code migration when replacing existing databases.
A core differentiator for PuppyGraph is its petabyte-level scalability, made possible by separating computation from storage. The system uses auto-sharded, distributed computation to manage vast datasets, so users can query data across multiple sources—including warehouses and lakes—rather than being limited to what is loaded into a single graph database. Additionally, PuppyGraph is optimized for performance, handling complex multi-hop queries (such as 10-hop neighbor searches) in seconds, leveraging patent-pending technology to maximize computing resources.
Who Uses PuppyGraph?
PuppyGraph serves organizations that need advanced graph analytics and flexible data access, including technology, finance, cybersecurity, and research sectors. Notable customers include Coinbase, Clarivate, Cipher, Dawn, Protocol Labs, Alchemy Pay, Prevalent AI, Ebay, Netskope, and Palo Alto Networks.
How Was PuppyGraph Started?
PuppyGraph was founded by a team with extensive experience in software engineering and large-scale data systems. Key founders include Weimo Liu (with previous roles at Google, TigerGraph, and LinkedIn) and Lei Huang (who has held engineering positions at Instacart, Google, Airbnb, and Pinterest). Their backgrounds in building scalable software products and graph database technology have shaped PuppyGraph’s focus on easy deployment and high performance. Learn more about their story on the About page.
What Problem Does PuppyGraph Solve?
Traditional graph databases often require complex ETL processes to prepare and load data, which can be slow, error-prone, and difficult to scale. PuppyGraph addresses these challenges by enabling direct querying of data in place, enhancing speed, scalability, and reducing operational overhead for organizations dealing with large or complex datasets.
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