Graph Analytics in Snowflake: Beyond Traditional SQL with Neo4j
Chapters 16
- Play from Introduction and Speaker Welcome
- Play from Understanding Snowflake: The Data Cloud
- Play from Important Disclaimer
- Play from Anatomy of an Algorithm Call (The 3-Step Process)
- Play from Compute pool selection
- Play from Modeling Tabular Data as Graphs
- Play from Schema Requirements for Node and Relationship Tables
- Play from Table of differences
- Play from Overview of Available Graph Algorithms
- Play from Result Types Overview
- Play from Estimation API
- Play from Visualisation Capabilities
- Play from Live Demo
- Play from The Job Life Cycle and Architecture
- Play from Future Integrations: Snowflake Cortex AI Agent. Upd.: Now available for preview https://medium.com/snowflake/announcing-the-private-preview-of-graph-agents-for-snowflake-intelligence-fbce69464798
- Play from Resources & Quick Links
Join Olga Razvenskaia, Senior Software Engineer in the Neo4j Graph Data Science team, as she demonstrates how to unlock powerful graph insights directly from your data stored in the *Snowflake Data Cloud* using the *Neo4j Graph Analytics* application from the Snowflake Marketplace.
Traditional SQL can struggle with complex, interconnected data. This session shows you a seamless, zero-ETL approach to modeling your tabular data as a graph and running sophisticated algorithms—all within your Snowflake environment.
Speaker: Olga Razvenskaia
*What You Will Learn in This Session:*
The Zero-ETL Advantage: Understand why running graph analytics directly on Snowflake data, without the performance and cost drag of extracting, transforming, and re-importing (ETL), is the superior approach.
Anatomy of an Algorithm Call: Learn the three logical steps executed within a single SQL query in Snowflake:
- Graph Projection: Defining how to construct a graph from your node and relationship tables.
- Algorithm Execution: Running advanced algorithms like PageRank on the projected graph.
- Writing Results Back: Storing the computed results (e.g., node scores, embeddings) into new Snowflake tables.
- Modeling Tabular Data as Graphs: Dive into the technical requirements for your Snowflake tables (like requiring node ID, source node ID, and target node ID columns) and how to use SQL views to satisfy the schema requirements without data duplication.
- Live Demo: PageRank on Flight Data: Watch a hands-on demonstration using an open flights dataset to calculate the most influential airports, with results immediately written back to Snowflake tables and visualized using the Neo4j visualization package.
- Future Integrations: Get an exciting preview of the Snowflake Cortex AI Agent integration for conversational graph analytics, which simplifies complex configurational JSONs and helps analyze algorithm results in a chat window.
This is a must-watch for data scientists, data engineers, and analysts looking to leverage the power of graph algorithms on their massive Snowflake datasets using familiar SQL tools.
*Resources:*
Resources mentioned
- View Presentation (opens in a new tab)
- Get Started with Aura (opens in a new tab)
- Deployment Center (opens in a new tab)
- Ground AI Systems and Agents with Neo4j (opens in a new tab)
- Try the Application: Search for "Neo4j Graph Analytics" on the Snowflake Marketplace (Includes a 30-day free trial) (opens in a new tab)
- Official Documentation
- GitHub Examples (opens in a new tab)


