PuppyGraph
ExternalPuppyGraph transforms existing SQL and relational databases into queryable knowledge graphs without ETL or data duplication, enabling instant access to complex relationships. Supporting Gremlin and openCypher query languages, it decouples compute from storage for petabyte-scale performance using columnar reads, making it ideal for enterprise analytics. It's perfect for teams in fraud detection, cybersecurity, machine learning, and any domain needing multi-hop queries on relational data warehouses.
Description
PuppyGraph transforms existing SQL and relational databases into queryable knowledge graphs without ETL or data duplication, enabling instant access to complex relationships. Supporting Gremlin and openCypher query languages, it decouples compute from storage for petabyte-scale performance using columnar reads, making it ideal for enterprise analytics. It's perfect for teams in fraud detection, cybersecurity, machine learning, and any domain needing multi-hop queries on relational data warehouses.
Key capabilities
- Queries existing SQL/relational stores as unified graph without ETL or data duplication
- Supports Gremlin and openCypher query languages
- Decouples compute from storage for petabyte-scale performance using columnar reads
Core use cases
- 1.Fraud detection
- 2.Cybersecurity threat analysis
- 3.Knowledge graphs for LLMs and machine learning
- 4.Social network analysis
- 5.Complex multi-hop relationship queries on relational data
Is PuppyGraph Right for You?
Best for
- Teams with relational data warehouses/lakes needing graph analytics
- Fraud detection, cybersecurity, knowledge graph for LLMs users
Not ideal for
- Users with non-relational or poorly structured data
- Teams avoiding graph modeling or query language learning curves
Standout features
- Zero-ETL setup in ~10 minutes
- Intuitive web UI with schema visualization, graph explorer, and dashboards
- Horizontal scaling for enterprise workloads
- Developer Edition free via Docker
- Enterprise 30-day trial available
Pricing
Enterprise
Developer
User Feedback Highlights
Most Praised
- Quick setup with no data pipelines needed
- Fast performance on complex multi-hop queries across billions of edges
- Scales horizontally without separate graph DB overhead
Common Complaints
- Zero user reviews on major sites like PeerSpot
- Past bugs including parallel traversal issues, schema timeouts, and security vulnerabilities
- Relies on quality of existing relational data sources