Neo4j graph analytics integrated with Snowflake’s AI cloud

Graph database and analytics player Neo4j has integrated its product within the Snowflake AI Data Cloud.

A graph database is a systematic collection of data that emphasizes the relationships between different data entities. It stores nodes and relationships instead of tables or documents.

By 2025, Gartner has estimated that graph technologies will be used in 80 percent of “data and analytics innovations”, up from 10 percent in 2021.

Neo4j says its graph data science is an analytics and machine learning (ML) solution that identifies and analyzes hidden relationships across billions of data points to improve predictions and discover new insights.

Neo4j says customers include the likes of Nasa, Comcast, Adobe, Cisco and Novartis.

Neo4j graph example

The Snowflake integration enables users to instantly execute more than 65 graph algorithms, eliminating the need to move data out of their Snowflake environment, the company says.

Neo4j’s library of graph algorithms and ML modelling enables customers to answer questions like “what’s important”, “what’s unusual”, and “what’s next,” said the provider. Customers can build knowledge graphs, which capture relationships between entities, ground LLMs (large language models) in facts, and enable LLMs to reason, infer, and retrieve relevant information more accurately and effectively, it claimed.

The algorithms can be used to identify anomalies and detect fraud, optimize supply chain routes, unify data records, improve customer service, power recommendation engines, and many other use cases.

The technology potentially allows Snowflake SQL users to get more projects into production faster, accelerate time-to-value, and generate more accurate business insights for better decision-making.

The alliance empowers users to leverage graph capabilities using the SQL programming language, environment, and tooling they already know, said the partners, removing complexity and learning curves for customers seeking insights crucial for AI/ML, predictive analytics, and GenAI applications.

Customers only pay for what they need. Users create ephemeral graph data science environments seamlessly from Snowflake SQL, enabling them to pay only for Snowflake resources utilized during the algorithms’ runtime using Snowflake credits. These temporary environments are designed to match user tasks to specific needs for more efficient resource allocation and lower cost. Graph analysis results also integrate seamlessly within Snowflake, facilitating interaction with other data warehouse tables.

Sudhir Hasbe, chief product officer at Neo4j, added: “Neo4j’s graph analytics combined with Snowflake’s unmatched scalability and performance redefines how customers extract insights from connected data, while meeting users in the SQL interfaces where they are today, for unparalleled insights and decision-making agility.”

The new capabilities are available for preview and early access, with general availability “later this year” on Snowflake Marketplace, the partners said.

Earlier this year, Snowflake reported that GenAI analysis of data in its cloud data warehouses is greatly rising.