Daniel ten Wolde
In this presentation, we introduce DuckPGQ, an open-source community extension for DuckDB, an in-process analytical database system with a relational data model. DuckPGQ extends DuckDB’s capabilities to support graph processing, leveraging the property graph data model and implementing the SQL/PGQ standard. This enables users to query and analyze graph data within the familiar SQL environment. By harnessing DuckDB’s efficient in-memory architecture, DuckPGQ facilitates fast and seamless graph operations on tabular data and has been shown to outperform traditional graph databases like Neo4j on certain pattern matching queries. Additionally, DuckPGQ supports efficient execution of graph algorithms, enabling complex analytics such as PageRank and clustering operations. We’ll explore how DuckPGQ bridges the gap between relational and graph data, empowering users to perform pattern matching, path-finding, and more—all without needing specialized graph databases and from the convenience of your own laptop.
Events
| Title | Day | Room | Track | Start | End |
|---|---|---|---|---|---|
| Empowering Data Analytics: High-Performance Graph Queries in DuckDB with DuckPGQ |
Saturday | UB5.132 | Data Analytics | 11:50 | 12:20 |