Neo4j Graph Analytics for Snowflake: Bringing Graph-Powered Insights to the AI Data Cloud

Neo4j Graph Analytics for Snowflake, available now, brings the power of highly optimized, enterprise-ready graph algorithms to the Snowflake AI Data Cloud. A zero-ETL offering using elastic compute, Graph Analytics for Snowflake allows you to rapidly generate deeper insights from your Snowflake data using familiar SQL—no specialized graph expertise required.

Graph analytics improves decision-making by uncovering hidden patterns and relationships in complex data, delivering more accurate insights with richer context. Yet implementation has often demanded significant technical investment—until now. Our new offering makes graph analytics immediately accessible to every Snowflake user, allowing business decision-makers and data teams to focus on outcomes, not overhead.

Uncover Valuable Hidden Patterns in Your Snowflake Data

Many critical relationships and patterns in your Snowflake data are invisible to traditional analytics, obscuring business opportunities and forestalling innovation. Graph Analytics for Snowflake offers enterprise-ready graph algorithms that deliver more analytical power and flexibility than simple SQL queries.

These advanced algorithms can dramatically improve business outcomes across hundreds of use cases—including fraud detection, supply chain management, and customer 360—and you can use them to solve some of the most complex challenges in analytics and data science, such as:

• Topological link prediction
• Centrality and importance
• Pathfinding and search
• Community detection
• Similarity

Audience Acuity has used this technology to solve a key analytical challenge—entity resolution—to deliver more personalized marketing experiences for its customers. “Our approach using Neo4j Graph Analytics for Snowflake ensures marketers stay ahead of the curve by stitching together records from 20 distinct data sources—encompassing 2.2 billion records—using SQL without ever moving the data,” says Benjamin Squire, Principal Data Scientist at Audience Acuity. “Neo4j’s graph-powered algorithms provide advanced insights, offering a transformative edge over traditional methods.”

Graph Analytics for Snowflake uses graph embeddings to transform graph structures into ML-ready features—an approach that uncovers deeper patterns in complex connected data while improving model accuracy by up to 80%.

The new offering prioritizes speed, delivering insights 2x faster than open-source alternatives with parallelized in-memory processing of graph algorithms. It can run different machine learning and data science research instances simultaneously, further improving data analyst productivity. And you can scale graph analytics across your organization with unlimited concurrent sessions, each running independently.

Graph Analytics for Snowflake also provides Snowflake users with enriched data for downstream analytics, visualization, and applications, including Streamlit.   

No ETL, Just SQL: Streamlined Development in the AI Data Cloud

Graph Analytics for Snowflake streamlines every stage of the development process required to generate and implement advanced insights in the AI Data Cloud. You can run algorithms and write results directly to Snowflake tables, eliminating the need for costly ETL, and use the available library of ready-to-use algorithms to avoid custom implementations.

Sticking to the tools you already know, use SQL procedures to run graph algorithms in Snowsight worksheets and Snowflake Notebooks, or include the results of the algorithms in Streamlit applications. You can also enable traditional machine-learning pipelines by encoding the context of your connected data into vectors with graph embeddings.

Cost-Efficient, Zero-Administration Graph Analytics in Snowflake

Graph Analytics for Snowflake uses serverless architecture and offers a pay-as-you-go pricing model to simplify cost management, allowing you to align your infrastructure spending with your business needs.

You can tailor each analytics session to your workload’s precise requirements, scaling your compute pool independently to account for large datasets and resource-intensive data analysis. Overall, this granular control over spending can reduce your infrastructure TCO by up to 70%.

Graph Analytics for Snowflake is also a fully managed analytics offering, reducing administrative costs by leveraging the built-in governance provided by Snowflake AI Data Cloud.

Explore the Potential of Graph Analytics in the AI Data Cloud

Neo4j Graph Analytics for Snowflake allows our shared customers to explore the endless possibilities of graph analytics within Snowflake’s large-scale AI Data Cloud. By enabling Snowflake users to run Neo4j advanced graph algorithms using Snowflake SQL and familiar tools like Streamlit and Snowflake Notebooks, we’re redefining how organizations extract insights from connected data. Product and engineering teams can now develop, scale, and operate applications without any additional infrastructure or operational burden—a pivotal step towards using relationships in data to produce deeper, more comprehensive insights at an unprecedented pace.