There are multiple features of PuppyGraph that I personally love. One of them is zero-ETL graph querying, which helps us connect with our data warehouses easily and link via dynamic JSON graph schemas. This enables us to explore relational tables, nodes, and edges in under seven minutes, which previously took three and a half hours, so it is a great reduction. I also love the massive distributed graph scalability. The distributed vectorized compute engine helps us auto-shard queries across nodes and consistently execute 10-hop traversals across multi-billion edge data sets in under two seconds. Additionally, I appreciate the unified single source of truth. It helps us commit to the underlying tables, and those commits are immediately queryable in the graph layer within zero seconds of ETL replication lag. This ensures that our AI agents never reason over stale data and always access the latest data without any lag or changes left. The governance and security of PuppyGraph are very good, and I would rate it 10 out of 10 in that aspect. PuppyGraph's governance and security features have helped our organization, as it queries lakehouse tables directly where they originate. This delivers an exceptional governance posture for our enterprise applications with the help of inherent source-level governance and ABAC, which has supported the existing row-level security while helping us implement RBAC and column masking policies. It stabilizes operations inside Snowflake in our data warehouses and has achieved 100% policy consistency with zero security drift. The accuracy and reliability of PuppyGraph's output are really great.
