12-08, 18:00–18:30 (UTC), Data Track
This talk presents Kùzu: a new open-sourced graph database management system (GDBMS) that is designed for Python graph data science (GDS) eco-system. Kùzu's embedded architecture makes it very easy to import as a library without a server setup and also provides performance advantages. Specifically users can: (i) ingest and model their application records in various raw file formats as a graph; (ii) query and transform these graphs using Cypher query language; and (iii) export graphs into popular Python GDS packages with no copy cost. We will live demo Kùzu's integration with NetworkX and Pytorch Geometric.
Building graph data science (GDS) applications require a series of data processing steps, such as extracting data from tabular sources into a graph, cleaning and transforming the graph, extracting node features, and finally moving data into a GDS package, such as NetworkX and Pytorch-Geometric for graph analytics. These steps can be performed easily and efficiently by GDBMSs, which provide with high-level graph-based data models and query languages. Kùzu is a GDBMS designed to serve as an essential storage system for GDS developers. Kùzu is very easily imported as Python package and integrates with many Python GDS packages.
The talk assumes familiarity with graph analytics. In two demonstration scenarios, we will demonstrate the benefits of using Kùzu when developing GDS pipelines and the usability features Kùzu.
Previous knowledge expected
Postdoc at University of Waterloo. Working on the open source project Kùzu, which is a highly scalable, extremely fast, and very easy-to-use embeddable graph database.