Neo4j announced the extension of its strategic partnership with Google Cloud by introducing a new native integration with Google Cloud’s BigQuery cloud data warehouse. Users can now extend SQL analysis with graph-native data science and machine learning by working seamlessly between BigQuery and Neo4j Graph Data Science. Neo4j will also integrate within the BigQuery Partner Center, which will allow its customers to seamlessly utilize Neo4j Graph Data Science from within the BigQuery experience.
The Google Cloud BigQuery integration enables users to conduct deep graph analysis of connected data, in-memory within Neo4j, and return results to BigQuery for use in downstream models and analysis. This means that customers can build graph data models in BigQuery and move them directly into Neo4j Graph Data Science in a few simple steps, eliminating the need for extra software or data staged in a queuing system. Additionally, data engineers can work entirely within BigQuery to build models for deep graph analysis in Neo4j Graph Data Science.
This latest partnership milestone comes as graph databases and graph data science technology are dramatically accelerating advances in artificial intelligence and machine learning, helping enterprises find hidden relationships and patterns across billions of data connections deeply, easily, and quickly. According to Gartner, graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021, “facilitating rapid decision-making across the enterprise.”
“As graph technology continues to advance, we are excited about expanding our partnership with Neo4j to introduce this new integration with Google BigQuery. The powerful combination of Graph Data Science with BigQuery will enable customers to solve even more complex problems, with the ability to conduct deep graph analysis of connected data with a seamless experience. We are excited to bring these innovations to market, together with Neo4j.” said Ritika Suri, Director of Technology Partnerships at Google Cloud.
Emil Eifrem, CEO and Co-founder of Neo4j said, “Data scientists can now make better AI predictions thanks to Neo4j’s integration with Google Cloud’s BigQuery. Graph-powered AI has changed the landscape for intelligent applications and large language models (LLM), improving their accuracy and unlocking new use case possibilities with graph algorithms and graph visualization. We’re excited by what we can enable for our customers together with this latest milestone.”
“We chose Neo4j Graph Data Science on AuraDS because it is a completely managed, cloud-based infrastructure combined with an elegant and user-friendly set of tools and extensive library of production-ready data science algorithms that gives us confidence in our platform and allows us to focus on our data and application development,” said Matthew Bernardini, Founder & CEO, Zenapse. “Neo4j Graph Data Science makes it easy to quantify the relationships and similarities that exist in the digital world and to surface new insights about these connected relationships. We are excited about the integration capabilities between Google Cloud’s BigQuery and Neo4j AuraDS to further exploit additional data insights which can be discovered by moving the data seamlessly between the two platforms.”
Long-standing strategic partnership since 2019
Google Cloud and Neo4j launched their strategic partnership in 2019, with Neo4j becoming Google Cloud’s first graph database partner. The objective was to ensure businesses have more seamless access and insights into the data required for digital transformation. The partnership included integration with Vertex AI, an ML development platform, enabling enterprises to build and deploy improved graph-based machine learning models. The relationship also benefited customers who complemented Neo4j Graph Database with data from Google Cloud’s Enterprise Knowledge Graph to provide more complete information. Hundreds of large enterprises and SMBs, such as Monsanto, Lucinity, PwC Canada, and more around the world, have leveraged Neo4j on Google Cloud for use cases such as anti-money laundering, customer engagement, identity and access management, and more.
A Neo4j demo is also available, showing a use case of shipping networks and freight movement through a global system of airports. By pushing data seamlessly from BigQuery within Neo4j Graph Data Science and ML, the connections between the business process and the physical world were easily identified, highlighting inefficiencies (i.e. high operational loads and cargo bottlenecks), quantifying risk, and pinpointing regional interdependencies.
