Neo4j Brings Instantaneous Graph-Enabled Insights to Snowflake Data

Neo4j, the graph database and analytics leader, is announcing a partnership with Snowflake that brings Neo4j’s fully integrated, native graph data science solution to the Snowflake AI Data Cloud. This news—which was announced at Snowflake’s annual user conference, Snowflake Data Cloud Summit 2024—empowers enterprises to leverage advanced, graph-enabled insights without moving their data from the Snowflake environment.

Neo4j graph data science is an analytics and machine learning (ML) solution designed to surface and analyze hidden relationships across billions of data points to improve predictions and unveil new insights. Neo4j’s expanding library of graph algorithms and ML modeling empowers customers to answer a variety of questions about their data, including what’s important, what’s unusual, and what’s next, according to the company.

Bringing Neo4j’s graph data science power to Snowflake allows users to instantly execute over 65 graph algorithms on their data stored in Snowflake. This eliminates the complexity, management hurdles, and learning curves for enterprises looking to power AI and machine learning (ML), predictive analytics, and generative AI (GenAI) apps with graph-enabled insights, according to the company. Applicable to a wide variety of use cases—including anomaly identification, fraud detection, supply chain route optimization, data record unification, customer service improvement, and more—any user leveraging Snowflake SQL can greatly accelerate project production, time-to-value, and insight generation with Neo4j’s graph algorithms.

“Integrating Neo4j’s proven graph data science capabilities with the Snowflake AI Data Cloud marks a monumental opportunity for our joint customers to optimize their operations,” said Jeff Hollan, head of applications and developer platform, Snowflake. “Together, we’re equipping organizations with the tools to extract deeper insights, drive innovation at an unprecedented pace, and set a new standard for intelligent decision-making."

In addition to over 65 out-of-the-box, easy-to-use Neo4j graph algorithms, customers benefit from native graph capabilities that integrate with an environment and tooling they already know. Using Snowflake SQL, data scientists and developers can seamlessly streamline development, accelerate time-to-insight, and derive greater value from their data, according to Neo4j.

“We've all talked about the capabilities of graph analytics and graph data science for many years, but now we are making these capabilities accessible to thousands of customers, natively, closer to where the data is, without having to move that data everywhere,” explained Sudhir Hasbe, chief product officer, Neo4j. “We are enabling this with SQL interface—which is basically what audiences already know how to go ahead and use—[which delivers] the simplification of using graph analytics closer to where your data is…the learning curve is going to be pretty low for people to start leveraging these algorithms.”

Another advantage to Neo4j’s integrated Snowflake solution is the elimination of extract, transform, load (ETL) processes. Joint customers can simply access and run Neo4j’s library of graph algorithms without having to undergo procurement and security sign-off to transfer their data to another SaaS provider. With zero ETL, customers benefit from simplified security and data workflows while eradicating additional overhead from data prep, according to Neo4j.

This integration is particularly pertinent for GenAI, as large language models require proprietary data to be stored in a specific way to answer enterprise questions or solve enterprise problems. Neo4j brings complex graph algorithms to Snowflake natively, enabling customers to create knowledge graphs and generate vectors based on structured, unstructured, and relationship data. Neo4j offers the foundation necessary to extract enterprise context for GenAI based on data stored within Snowflake.

Neo4j’s integrated graph data science solution is both fully serverless and flexible, where customers only pay for the Snowflake resources used during the algorithms’ runtime with Snowflake credits. These temporary environments are specifically tailored to match user tasks to specific needs, ensuring efficient resource allocation and lower expenditure, according to Neo4j.

To learn more about Neo4j and Snowflake’s partnership, please visit or