Graph query engine company PuppyGraph announced a $5 million seed funding round led by defy.vc. This zero-ETL unlocks real-time graph analytics for enterprises, and this power translates into significant industry benefits, from generating GraphRAG to enhance LLM accuracy and reduce hallucinations to real-time fraud detection and cybersecurity analyses. It also transforms logistics for retail and supply chains and healthcare outcomes by efficiently revealing deep insights from extensive biological and patient data relationships.
Graph query languages excel at modeling intricate traversal queries across dense data networks. However, they are typically tied to systems that couple the query and storage layers, complicating scalability, adoption, and maintenance.
As the first graph query engine, PuppyGraph brings a new transformative shift. Now, users can query a single copy of data in both SQL and graph simultaneously. For individuals familiar with SQL and venturing into graph analytics for the first time, PuppyGraph simplifies the process of data preparation, aggregation, and management by utilizing the data lake and tools they are already comfortable with.
This design enables users to bypass the complexities of graph query languages for regular tasks, reserving these languages solely for specific graph-related inquiries like graph traversals. And by streamlining these processes, PuppyGraph significantly reduces the learning curve and boosts operational efficiency. This enables enterprises to continue using their SQL data stores while benefiting from graph-specific capabilities like complex pattern matching and efficient pathfinding.
PuppyGraph integrates with widely-used data lakes and warehouses, such as Apache Iceberg, Delta Lake, Apache Hudi, DuckDB, Databricks, Snowflake, AWS Redshift, BigQuery, CelerData, Hive, SingleStore, MySQL, PostgreSQL, and others.
Even though the native graph storage systems can take months to set up due to building complex data replication process, PuppyGraph goes from deployment to query in just 10 minutes, handles petabyte-scale data with ultra-low latency, executing 10-hop neighbor queries across half a billion edges in just 2.26 seconds.
PuppyGraph has quickly gained traction since launching in March 2024 and is now in production within industry leaders like Coinbase, Clarivate, Dawn Capital, and numerous other enterprises. Prevalent A (an ISTARI Collective member and a Cyber Security leader) is integrating its Security Data Fabric offering used by large enterprises with PuppyGraph. Significant product advancements were also made, such as supporting major data sources like Unity Catalog, SingleStore, Vertica, and IBM watsonx.data.
PuppyGraph’s growth was proven by a 70% month-over-month increase in downloads of its forever-free developer edition. And PuppyGraph has formed strategic partnerships with Google Cloud (BigQuery and AlloyDB) and has become Databricks’ first graph analytics partner for Unity Catalog.
PuppyGraph was founded by Weimo Liu (a Computer Science Ph.D graduate from George Washington University utilizing his experience from Google’s F1 team and TigerGraph), Danfeng Xu (a nine-year veteran of LinkedIn’s infrastructure team), and Lei Huang (a three-time Google Code Jam world finalist). Zhenni Wu (an experienced go-to-market veteran from the graph database sector) also joined the founding team. To support their expertise, Gary Hagmueller, former CEO of Dgraph and Arcion (recently acquired by Databricks as its third largest acquisition to date), has come on board as an active advisor.
PuppyGraph’s GraphRAG framework addresses these challenges by integrating a ‘Knowledge Graph’ that understands and utilizes the structure and connections within data, significantly enhancing the contextual understanding and navigational capabilities of LLMs.
PuppyGraph’s Agentic GraphRAG, a pioneering development in the industry, simplifies the creation of large-scale knowledge graphs with zero ETL. It enables rapid deployment to query in under 10 minutes and supports both Gremlin and Cypher query languages to tailor precision and optimize performance. With the ability to handle petabyte-scale data with ultra-low latency, PuppyGraph can detect even the most hidden insights in vast datasets.
KEY QUOTES:
“PuppyGraph is a very interesting graph query engine. It doesn’t require us to load or ETL any data into a specialized or proprietary database storage layer for graphs. We can simply query everything directly on our data lake—whether it’s Delta, Iceberg, or just plain Parquet files. PuppyGraph can integrate this data into a graph model and another distributed computation engine to render all the results. We use it in conjunction with Unity Catalog to unlock all our transactional and crypto data already on our Delta Lake. PuppyGraph then queries this data directly to perform all sorts of graph-based exploration and aggregation. This capability is so powerful, and our users really enjoy this level of flexibility.”
– Eric Sun, Sr. Manager of Data Platform at Coinbase
“We are thrilled to partner with defy.vc on this journey to revolutionize graph analytics. This funding will accelerate our product development, expand our team, and increase our market presence, bringing our powerful graph query engine to more organizations worldwide.”
– Weimo Liu, CEO of PuppyGraph
“PuppyGraph is changing the way companies approach graph analytics. We are excited to support this innovative team that is not only advancing graph technology but also making it universally accessible and easy to use. PuppyGraph’s approach will significantly accelerate the adoption of graph analytics across industries.”
– Brian Rothenberg, Partner at defy.vc
