Introducing Neo4j Aura Graph Analytics: Scalable, Easy-to-Deploy Graph Analytics for Any Data Source

Today, we’re announcing Neo4j Aura Graph Analytics, a new serverless offering that delivers deeper insights than traditional analytics—from any data source and on any platform.

Enterprises looking to make their data AI-ready and increase its business value will find Aura Graph Analytics especially useful. When combined with a graph database like Neo4j AuraDB, this new offering is uniquely optimized to help make sense of complex, fragmented enterprise data by uncovering critical patterns and insights, all while aligning to changing business requirements. This is what it takes to go from AI experimentation to AI deployment.

Responding to the release of Aura Graph Analytics, IDC Research Director for Data Management Devin Pratt highlighted its potential to transform business decision-making: “Neo4j’s new serverless graph analytics solution, developed with ease of use and accessibility in mind, will allow enterprises to scale analytics across any data source or cloud platform, transforming their data into a wealth of actionable knowledge, and providing deeper insights for improved organizational decision-making.”

Generally available now and compatible with all relational and non-relational databases, cloud platforms, and data lakes, Neo4j Aura Graph Analytics offers:

• Up to 80% increase in model accuracy and deeper insights by transforming graph structures into ML-ready features with graph embeddings
• 2x faster time to insights over open-source alternatives through parallelized in-memory processing of graph algorithms 
• 75% less code and zero ETL with 65+ prebuilt, optimized graph algorithms, plus native Neo4j integration and support for projections using any data source
• No administrative overhead and lower TCO with no server provisioning, pay-as-you-go pricing, and independent scaling of compute and storage

Go Beyond Traditional Data Analytics to Realize the Full Value of Organizational Data

Many critical relationships and patterns in organizational data are invisible to traditional analytics, obscuring business opportunities and forestalling innovation. Neo4j Aura Graph Analytics opens up entirely new analytical possibilities, unlocking the full value of business data.

Aura Graph Analytics offers 65+ fine-tuned algorithms that can dramatically improve outcomes across hundreds of use cases, such as:

• Fraud detection
• Anti-money laundering
• Disease contact tracing
• Customer 360 
• Supply chain management
• Recommendation engines
• Social network analysis 

These algorithms offer more flexibility than rigid queries, which must be tailored to fit your data, and solve essential analytics and data science problems, including:

• Pathfinding and search
• Community detection
• Centrality and importance
• Topological link prediction
• Similarity
• Node embeddings
• Supervised machine learning

Using graph embeddings, we transform graph structures into ML-ready features. Based on our customer experiences, this approach not only uncovers deeper patterns in complex connected data but also improves model accuracy by up to 80%.

Neo4j Aura Graph Analytics also prioritizes speed, delivering insights 2x faster than open-source alternatives with parallelized in-memory processing of graph algorithms. It can run different data science and machine learning research instances simultaneously, further improving data analyst productivity. You can scale graph analytics across your organization with unlimited concurrent sessions, each running independently.

Cost-Efficiency and Ease of Development Across Clouds, Data Sources, and Languages

Neo4j Aura Graph Analytics streamlines every stage of the development process required to generate and implement advanced insights. We designed it to work with your existing data layer via native integration with Neo4j databases or projecting any data source using our client library. This easy-to-use tooling allows developers and data analysts to quickly run algorithms without ETL.

With the Graph Analytics Python client, developers and data scientists can project graphs, execute algorithms, and use machine learning pipelines all directly in Python. Direct Python integration allows you to work with minimal context-switching between languages. With our library of ready-to-use algorithms, you can reduce your code footprint by 80% relative to custom implementations of graph algorithms.

Together, these capabilities reduce development time for complex analytics workflows from weeks to minutes, eliminating the need for additional data science resources.

Neo4j Aura Graph Analytics also allows you to align your infrastructure spending with your business needs through serverless architecture and a pay-as-you-go pricing model. You can tailor each Graph Analytics session to your workload’s precise resource needs, enabling granular control over spending. Overall, this can reduce your infrastructure TCO by up to 70%.

Dor Shoef, Data Engineering Tech Lead at bedding ecommerce company Resident Home, has experienced these benefits firsthand: “We moved to AuraDB with serverless graph analytics capabilities for its combination of reliability, high availability, and powerful advanced algorithms. It was quick and easy—in less than a few hours, we were able to make the switch in our code and get it up and running. We could also scale up our graph analytics memory on demand, for even faster results without touching the main instance’s configurations.”

Success Stories: Neo4j Aura Graph Analytics in Action

Using Neo4j Aura Graph Analytics to go beyond traditional analytics has opened up new business possibilities for organizations across industries and use cases. Below, we highlight key achievements in fraud detection, talent retention, and data protection. 

BNP Paribas: Reducing Fraud by 20%

BNP Paribas Personal Finance used Neo4j Aura to build a fraud detection system that identifies fraud patterns in less than two seconds. The company has reduced fraud by 20% while ensuring that over 800,000 credit applications are processed efficiently.

DXC Technology: Decreasing Employee Attrition by 40%

DXC Technology developed a Career Navigator platform with Neo4j Aura, which helps train and retain its highly skilled workforce. It has reduced attrition by 40%, increased internal hiring by 12%, and developed career profiles for over 8,000 employees.

Intuit: Protecting the Data of 100 Million Customers

Intuit uses Neo4j Aura graph algorithms to safeguard its network infrastructure and protect the data of 100 million customers. It can now attribute 500,000+ endpoints to host names in milliseconds, enabling rapid responses to zero-day vulnerabilities.

Get Started With Neo4j Aura Graph Analytics

It’s easy to get started with Neo4j Aura Graph Analytics and deliver the kinds of insights that proved transformative for BNP Paribas, DXC, and Intuit. You can also join us on May 22 for one of our regional Aura Graph Analytics webinars:

• Americas
• Europe, Middle East, Africa
• Asia-Pacific

And if you’d like to learn more about graph analytics or Neo4j graph algorithms, take a look at these resources:

• Graph Algorithms for Data Science
• How to Identify What’s Important, What’s Unusual, and What’s Next Using Graph Algorithms
• Neo4j Graph Algorithm Documentation