Unleashing a new breed of real-time graph analytics, PuppyGraph cuts through the complexity of traditional graph systems with its innovative approach
Traditional SQL operations struggle with complex, interconnected data, often becoming cumbersome and verbose. Graph query languages excel at modeling intricate traversal queries across dense data networks but are typically tied to systems that couple the query and storage layers, complicating scalability, adoption, and maintenance.
Being the first graph query engine, PuppyGraph brings a new paradigm shift where now users can query a single copy of data in both SQL and graph at the same time. 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 allows users to bypass the complexities of graph query languages for regular tasks, reserving these languages solely for specific graph-related inquiries like graph traversals. By streamlining these processes, PuppyGraph not only significantly reduces the learning curve but also boosts operational efficiency. This allows enterprises to continue using their SQL data stores while benefiting from graph-specific capabilities like complex pattern matching and efficient pathfinding.
PuppyGraph integrates effortlessly with widely-used data lakes and warehouses, including Apache Iceberg, Delta Lake, Apache Hudi, DuckDB, Databricks, Snowflake, AWS Redshift, BigQuery, CelerData, Hive, SingleStore, MySQL, and PostgreSQL, among others.
While 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.
Since its launch in March 2024, PuppyGraph has quickly gained traction and is now in production within industry leaders such as Coinbase, Clarivate, Dawn Capital and numerous enterprises. Prevalent AI, 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 have also been made, including adding support for major data sources like Unity Catalog, SingleStore, Vertica, and IBM watsonx.data.
PuppyGraph’s growth is evidenced by a 70% month-over-month increase in downloads of its forever-free developer edition. In addition, PuppyGraph has formed strategic partnerships with Google Cloud (BigQuery & AlloyDB) and has become Databricks’ first graph analytics partner for Unity Catalog further positioning PuppyGraph as the industry leading graph analytics innovator.
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PuppyGraph was co-founded by Weimo Liu, a Computer Science Ph.D graduate from George Washington University, leveraging his experience from Google’s F1 team and TigerGraph, alongside 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 bolster 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.
As lakehouse architecture matures, enabling enterprises to aggregate all their data in one place, the potential to extract meaningful insights from this unified data is just beginning. Graph technology stands as the cornerstone of AI-driven architectures, offering unparalleled prowess in discerning intricate relationships within data. Traditional RAG systems struggle with fragmented data, often missing critical relationships and yielding disconnected results. PuppyGraph’s innovative 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, enabling 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 pinpoint even the most hidden insights in vast datasets.
Eric Sun, Sr. Manager of Data Platform at Coinbase, highlighted the impact of PuppyGraph at the Data+AI Summit 2024: “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.”
“We are thrilled to partner with defy.vc on this journey to revolutionize graph analytics,” said Weimo Liu, CEO of PuppyGraph. “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.”
Brian Rothenberg, Partner at defy.vc, commented, “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.”
Source: Businesswire