Introduction

Neo4j Graph Analytics for Snowflake is offers efficient and parallelized graph algorithms ranging from Dijkstra’s algorithm to graph neural networks. Graph algorithms can provide important insights in almost any domain, as graphs naturally model the world.

Various types of graph algorithms are supported, including centrality measures, community detection, clustering, similarity metrics, pathfinding, and graph machine learning. These can be applied to Fraud detection, Product recommendation, Entity resolution, Customer segmentation, Patient journeys, Credit scoring and more.

Using this application, you can load your Snowflake data into a graph and leverage the power of graph algorithms. The results of computations are written back to Snowflake tables. This makes it easy to chain algorithms together.

The application is designed around the ability to scale out computations using Snowflake compute pools. The amount of compute resources is adjusted to fit the required workload.

Algorithms are invoked via a simple-to-use API consisting of SQL procedures.