SumoDB in Neo4j: Graph Analytics in Snowflake — Part 2

This blog post by Benjamin Squire explores the integration of Neo4j's Graph Analytics with Snowflake, specifically detailing the use of graph databases to analyze data from the world of Grand Sumo. It builds on a prior model that visualized sumo bouts using Neo4j and now describes the installation and utilization of Neo4j Graph Analytics for Snowflake. The focus is on expanding the dataset to include the top 42 rikishi from the Makuuchi division in the Haru Basho 2026, resulting in a graph of approximately 20,000 nodes and 86,000 relationships. The post outlines the advantages of using Neo4j Graph Analytics in Snowflake, such as cloud scalability and on-demand pricing, and elaborates on the application of the Louvain Community Detection algorithm to identify community structures within the sumo data. It demonstrates how the algorithm can infer the ranks of rikishi by analyzing their bout pairings, and concludes with a preview of future analyses combining multiple graph algorithms to gain deeper insights into rikishi performance and techniques.