Seamless graph intelligence for your Snowflake data estate: The Neo4j and Snowflake partnership Neo4j and Snowflake have partnered to bring Neo4j's graph intelligence capabilities into Snowflake's AI Data Cloud, with the integrated portfolio spanning Virtual Graph, Graph Analytics for Snowflake, and the Aura Data Importer for Snowflake. Virtual Graph for Snowflake, now in Public Preview with general availability planned for November 16, builds knowledge graphs directly over Snowflake data with zero data movement by translating Cypher queries into deterministic SQL pushed to the Snowflake warehouse, returning results rather than data. Neo4j says the approach lets users generate a first graph model and adopt a working knowledge graph in minutes rather than weeks while preserving existing Snowflake governance rules and compute investments. Seamless graph intelligence for your Snowflake data estate: The Neo4j and Snowflake partnership Partner Alliance Director, Neo4j 7 min read Modern data warehouse providers like Snowflake https://neo4j.com/partners/snowflake/ deliver a consolidated platform for an organization’s data and AI ecosystem. Large Language Models LLMs offer extraordinary evolutionary capabilities in human-to-IT interactions. Yet traditional analytics and vector searches alone often fail to uncover the deep, complex relationships hidden across enterprise data. This slows down decision-making and limits the value of existing data assets. It can also cause critical errors for generative AI applications. To solve this, Neo4j and Snowflake have partnered to bring enterprise context to Snowflake’s AI Data Cloud. This partnership enables organizations to gain deeper insights, drive innovation, and set a new standard for intelligent decision-making without the painstaking, time-consuming ETL processes. However, one size never fits all. Our co-development efforts aim to bring the Neo4j graph intelligence capabilities that users need to their Snowflake data estate, with deployment options suited for different use cases. The integrated portfolio includes Virtual Graph https://neo4j.com/blog/auradb/neo4j-virtual-graph-is-now-in-public-preview/ , Graph Analytics for Snowflake https://neo4j.com/neo4j-graph-analytics-snowflake/ , and the Aura Data Importer for Snowflake https://neo4j.com/docs/aura/import/introduction/ . Build knowledge graphs without moving Snowflake data with Virtual Graph Build and query knowledge graphs directly over Snowflake data with zero data movement – translate Cypher to SQL automatically, preserve existing governance, and accelerate AI reasoning. Virtual Graph for Snowflake https://neo4j.com/blog/auradb/neo4j-virtual-graph-is-now-in-public-preview/ , currently in Public Preview with planned GA on November 16, allows users to build a knowledge graph over an organization’s Snowflake data, enabling them to run Cypher queries against data in the data warehouse with zero data movement. Virtual Graph translates Cypher queries into deterministic SQL statements that are pushed to a Snowflake data warehouse, returning the results not the data as a graph for exploration and use within Bloom and Query, use by Aura Agents, and more. Cypher is GQL-compliant, which assures organizations requiring robust standards for query languages. This brings the power of Neo4j’s AI-powered graph tools to an organization’s relational data core, leveraging existing data governance rules and the compute platform in which they’ve already invested. Virtual Graph provides AI-powered graph schema design against existing Snowflake data. Users can generate their first graph model instantly and adopt a working knowledge graph in minutes not weeks , accelerating time to business value. With Cypher in their toolkit, users can craft queries to ask connected data questions like “if Warehouse X in Houston shuts down due to a storm, which pending orders need to be rerouted, through which secondary hubs, to hit guaranteed delivery dates?” or “what clinical outcomes were achieved by patients with similar genetic mutations, age profiles, and co-morbidities when given Treatment A vs. Treatment B?”. Users unfamiliar with Cypher can make use of the AI-powered Copilot to translate natural language to graph queries. Beyond tried and true knowledge graph use cases such as customer/product 360, supply chain management, fraud prevention, and medical research, the exponential growth of agents and agentic tools in the market is accelerating the adoption of horizontal graph use cases with the capacity to improve agentic outcomes. LLMs offer a vast breadth of knowledge and astounding capabilities to generate multimodal content, but their lack of company-specific context and understanding can limit their usefulness and, therefore, their adoption at scale. Recent research