“For a user-facing application, query latency and data freshness are non-negotiable. PuppyGraph allows us to execute multi-hop graph traversals directly on our existing data platform without moving or duplicating a single row. By removing the ETL bottleneck, we eliminated pipeline maintenance entirely while delivering lightning-fast graph analytics to our end users in real time.”

“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.”

“With PuppyGraph, standing up a new graph is a schema definition, not a data migration. We connect directly to the tables we already maintain in Snowflake and Databricks and start querying immediately. No pipelines. No second copy of the data, and no data movement.

The architecture scales with our existing data platform, even at petabyte scale. And with PuppyGraph MCP, we can query complex relationships in real time using natural language. It has become part of how we work with our data every day.”

“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.”

“PuppyGraph is the only solution we found that supports Iceberg natively at that scale, with no data loading required. You just connect to the lakehouse underneath, and the cluster itself doesn’t manage any data. Querying it as a graph in place, without duplicating a single row, is what made this viable for us”

“We run AI agents across thousands of tables and billions of edges a day. Two years ago, I’d have told you the graph side alone made this impractical at this scale. It’s one of those products where you ask: how is this just now?”

“Although Neo4j is a popular knowledge graph solution, we used PuppyGraph due to its open-source flexibility and seamless integration capabilities. PuppyGraph functions as a zero-ETL graph analytics engine that allows direct querying of data from sources like Databricks without requiring data movement. This approach reduces onboarding costs and improves performance.”

“Historically, graph queries required specialized storage and compute, but now PuppyGraph is running directly on plain vanilla Iceberg tables. Just point to a table with your nodes and edges, and let PuppyGraph go. The performance you get out the other side is amazing.”

“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.”

“After evaluating a few different graph solutions, we struggled to find one that could scale to our data needs. PuppyGraph's approach, decopling 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.”

“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.”

“In traditional architectures, working with graph data usually means ETL into a dedicated graph database like Neo4j, TigerGraph, or DataStax—which adds complexity and cost. Here, this is a different route: plug a graph engine directly into your Iceberg lakehouse. Simple, unified, and more cost-effective.”

“We needed a query engine that could run complex graph traversals on Iceberg tables fast enough to power a real-time product. PuppyGraph gave us sub-second performance on millions of records without requiring us to move data into a separate graph store. It's the layer that makes our architecture actually work.”

“In our evaluation, PuppyGraph handled a graph with hundreds of millions of relationships, with strong performance on multi-hop queries. Being able to query our existing Databricks data as a graph gives us a foundation for building AI applications without creating a separate copy of that data.”