Graph database and analytics leader Neo4jⓇ announced at Snowflake’s annual user conference, Snowflake Data Cloud Summit 2024, a partnership with Snowflake to bring its fully integrated native graph data science solution within Snowflake AI Data Cloud. The integration enables users to instantly execute more than 65 graph algorithms, eliminates the need to move data out of their Snowflake environment, and empowers them to leverage advanced graph capabilities using the SQL programming languages, environment, and tooling that they already know.
The offering removes complexity, management hurdles, and learning curves for customers seeking graph-enabled insights crucial for AI/ML, predictive analytics, and GenAI applications. The solution features the industry’s most extensive library of graph algorithms to identify anomalies and detect fraud, optimize supply chain routes, unify data records, improve customer service, power recommendation engines, and hundreds of other use cases. Anyone who uses Snowflake SQL can get more projects into production faster, accelerate time-to-value, and generate more accurate business insights for better decision-making.
Neo4j 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’s library of graph algorithms and ML modeling enables customers to answer questions like what’s important, what’s unusual, and what’s next. Customers can also build knowledge graphs, which capture relationships between entities, ground LLMs in facts, and enable LLMs to reason, infer, and retrieve relevant information more accurately and effectively. Neo4j graph data science customers include Boston Scientific, Novo Nordisk, OrbitMI,and Zenapse, among many others.
“By 2025, graph technologies will be used in 80% of data and analytics innovations — up from 10% in 2021 — facilitating rapid decision-making across the enterprise,” predicts Gartner® in its Emerging Tech Impact Radar: Data and Analytics November 20, 2023 report. Gartner also notes, “Data and analytics leaders must leverage the power of large language models (LLMs) with the robustness of knowledge graphs for fault-tolerant AI applications,” in the November 2023 report AI Design Patterns for Knowledge Graphs and Generative AI.
Neo4j with Snowflake: new offering capabilities and benefits
Enterprises can harness and scale their secure, governed data natively in Snowflake and augment it with Neo4j’s graph analytics and reasoning capabilities for more efficient and timely decision-making, saving customers time and resources.
- Instant algorithms. Joint customers can use SQL to build knowledge graphs and run more than 65 Neo4j graph algorithms out of the box, including easy-to-use machine learning tools. Neo4j’s library is available as a native service within Snowflake. Graph algorithms are available as SQL functions, enabling users to easily enhance ML pipelines with influencer scores, community identifiers, page rank, outliers, and other graph features for greater ML accuracy.
- Zero ETL (Extract, Transform, Load). Customers can access and run Neo4j’s extensive library of graph algorithms entirely within their Snowflake environment without the need to go through procurement and security sign-off to move their data to another SaaS provider. The ability to use their data as-is without having to go through the painful exercise of extracting, transforming, and loading it into another database and provider. Zero ETL simplifies security and data workflows and eliminates the overhead of data preparation.
- Familiar languages and tooling. Customers benefit from native graph capabilities as part of a toolset and environment with which they already know. Data scientists and developers can use Snowflake SQL in their workflows to streamline development, accelerate time-to-insight, and easily derive greater value from their data. Neo4j works with the latest Snowpark Container Services (SPCS) that Snowflake announced today.
- GenAI enabled. Joint customers can create knowledge graphs and generate vectors that take advantage of structured, unstructured, and relationship data. These features are part of a complete GenAI stack within Snowflake that includes both vector search and Snowflake Arctic LLM models. The result organizes and represents the data in ways that make it easier to understand and retrieve insights in GenAI applications and make these insights more accurate, transparent, and explainable.
- Fully serverless and flexible. Customers pay only 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.
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