Abstract:

Modern enterprises increasingly store their most valuable data in large-scale cloud warehouses such as Snowflake, yet the ability to reason over relationships in that data remains limited by traditional relational and tabular paradigms. At the same time, graph-based approaches have become essential for use cases such as fraud detection, supply chain analysis, GraphRAG and agentic AI reasoning where multi-hop relationships and contextual traversal matter more than isolated records.

In this session, we explore Neo4j Virtual Graph, a new capability from Neo4j that enables graph querying and graph algorithms to run directly over existing data in Snowflake without data movement or duplication. Through a zero-copy architecture, Virtual Graph dynamically maps relational schemas into a graph model, translates Cypher into optimised SQL and executes graph reasoning directly inside the warehouse environment.

Attendees will learn:

- How Neo4j Virtual Graph maps relational data in Snowflake into a graph model and executes Cypher queries without moving or duplicating data.

- How graph reasoning (multi-hop traversals and graph algorithms) can be applied directly on warehouse-scale datasets to uncover relationships that are hard to express in SQL.

- How to use Virtual Graph for real-world workloads, such as fraud detection, over existing Snowflake data.

Presenter:

Akmal Chaudhri

Technical Curriculum Developer, Neo4j

Bio:

Akmal is a technical leader and evangelist with extensive experience across databases, AI and developer enablement. Specialised in technical writing, education and community strategy. Proven ability to translate complex technology into clear, engaging narratives that inspire learning and adoption. Regular international speaker, published author and contributor to thought leadership in data systems, AI and developer education.