PuppyGraph Secures $5 Million to Advance Zero-ETL Graph Querying

Businesses are using AI, predictive analytics, and other advanced tools to gather more data than ever before with the hopes of gathering a deeper understanding of their environment, identifying patterns, and making more informed strategic decisions.

However, companies often rely on traditional relational databases to store and manage their data. These systems often lack the scalability and flexibility required for modern analytics. In response to these challenges, many organizations have turned to graph technology to better map the intricate connections in their data.

Managing both graphs and relations databases together can be challenging in terms of cost, time, and complexity. This is where PuppyGraph, a San Francisco-based startup founded by former engineers from Google and LinkedIn, steps in.

The startup recently raised $5 million in seed funding to advance what it describes as the first and only graph query engine. PuppyGraph’s zero-ETL engine is designed to allow users to query their relational data as a unified graph, removing the need for a separate graph database and the time-consuming extract, transform, and load (ETL) processes.

While traditional SQL operations are well suited for handling structured data in tables, they struggle to manage complex and interconnected data. Users may have to rely on complicated JOIN operations across tables and ETL pipelines but this can significantly reduce the efficiency of querying and analyzing complex data sets.

PuppyGraph aims to address this with its zero-ETL engine by enabling businesses to work with their existing SQL infrastructure while accessing the advanced capabilities of graph analytics. It does this by Integrating the power of both relational and graph databases.

Users can perform their graph-based queries on top of their relational data. This eliminates the need to learn new graph query languages or redesign their existing systems. For users who are well-versed in SQL and want to explore graph analytics, PuppyGraph simplifies the process for them by allowing them to work with familiar data lakes and tools for data preparation, aggregation, and management.

According to PuppyGraph, their engine can go from deployment to query in just 10 minutes and is capable of scaling with petabytes of data and executing complex 10-hop queries in seconds. Users simply connect it to their data source, after which the engine automatically generates a graph schema and enables queries on tables as graph models.

PuppyGraph was founded in 2023 with a mission to bring simplicity to graph analysis. Recognizing the challenges of navigating complex relationships in traditional databases, PuppyGraph founders wanted to create a tool that would eliminate the need for additional layers of complexity, costs, and maintenance.

The startup’s name comes from a puppy owned by one of the founders. The idea was to make graph technology as approachable as a puppy.

Within the year of its launch, the startup has scaled quickly and is now in production with Dawn Capital, Clarivate, Prevalent AI, Coinbase, and numerous other enterprises. It has also integrated with popular data lakes and warehouses such as Snowflake, DuckDB, and AWS Redshift.

The impressive growth is reflected in the 70% month-over-month increase in downloads for PuppyGraph’s free developer edition. The startup has also become Databricks’ first graph analytics partner for Unity Catalog.

Commenting on the impact of PuppyGraph at the Data+AI Summit 2024, Eric Sun, Sr. Manager of Data Platform at Coinbase shared: “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.”

As graph database technologies mature and become more popular, Gartner predicts that the market will grow to $3.2 billion by 2025, expanding at a compound annual growth rate (CAGR) of 28.1%.

PuppyGraph is off to a solid start, but it faces stiff competition from the likes of Tigergraph, AWS Neptune, Neo4j, ArrangoDB, and Aerospike. With its new capital, PuppyGraph plans to expand its team, accelerate product development, and increase its global presence.

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