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Neo4j AuraDB: Fully Managed Graph Database

Mirror your data design like sketching on a whiteboard and adapt effortlessly to changing business needs. Store data and relationships natively with a flexible schema.

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Uncover Hidden Patterns

Reveal insights with Cypher queries and graph algorithms for your connected data.

Build Applications With Ease

Simplify development using a native graph model, flexible schema, and expressive Cypher.

Always-On, Zero Admin

99.95% uptime SLA, automated upgrades, patches, and maintenance.

Enterprise-Grade Security

Protect your data and ensure compliance with robust security features.

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Neo4j AuraDB: A Smarter Way to Store and Connect Data

Traditional databases strip away valuable context and relationships. Neo4j AuraDB preserves these connections, storing data in nodes with relationships intact. This makes your data smarter, more contextual, and easier to work with. AuraDB evolves with your changing business needs, eliminating rigid schemas and painful JOINs.

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Capabilities

Build Your Critical Applications With Confidence

  • Match Patterns in Data and Relationships

    Find complex or recursive patterns without JOINs using expressive Cypher query language and graph algorithms.

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  • Explore Data Insights

    Find hidden connections and relationships using the Visualization tool and built-in Dashboards in the Aura console. Minimize manual query work and generate visual insights faster with AI-powered dashboard creation and GenAI Copilot.

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  • Index-Free Adjacency

    Navigate the graph without index lookups or JOINS, keeping processing time proportional to data size, making traversals lightning fast with index-free adjacency.

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  • Analytical and Transactional Workloads

    Support diverse workloads to simplify data architecture. Automatically split analytical queries to run concurrently across multiple threads on multi-core CPUs for stronger results.

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  • Enrich Predictions and Inferences

    Discover more insights using graph data science and machine-learning algorithms, such as pathfinding, centrality algorithms, community detection algorithms, and network embeddings.

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  • 99.95% Uptime SLA

    High availability, reliability, and minimal downtime. Scale instances for data and compute needs, efficiently managing datasets and memory-intensive workloads.

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  • Zero Administration

    Build apps without having to manage infrastructure. Get automatic service upgrades, security updates, and new features without interruption.

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  • Minimize Downtime and Data Loss

    Restore databases with full or incremental backups across multiple zones, point-in-time recovery, and fully encrypted snapshots verified for data consistency.

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  • Multi-Cloud Freedom

    Available on Amazon Web Services, Microsoft Azure, and Google Cloud in over 60 global regions.

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  • End-to-End Encryption

    Data is encrypted at rest with customer-managed keys. Network traffic, even within service infrastructure, is encrypted via Transport Layer Security (TLS).

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  • Fine-Grained Access Control

    Protect data from unauthorized access with role-based and property-based access control (PBAC), enabling selective graph traversals and precise read/write access for different parts of the graph.

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  • Network Security

    Segregate network traffic and resources to prevent unauthorized access. You also reduce the risk of security breaches with network isolation and private endpoints.

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  • Comply With Industry Regulations

    Adhere to industry compliance standards for data security and privacy regulations, such as ISO 27001, ISO 20243, GDPR, CCPA, SOC2 Type II, SOC3, and HIPAA.

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Free Guide
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Build and work with your first knowledge graph

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Powerful Use Cases

Optimize your projects with the Graph Database as a foundation.

Generative AI

Artificial Intelligence (AI) is poised to drive the next wave of technological disruption across nearly every industry. Just like previous technology revolutions in web and mobile, however, there will be...

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Knowledge Graphs

Access deep, dynamic context by connecting your data in knowledge graphs. You can quickly design, implement, and evolve your knowledge graph with Neo4j.

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Fraud Detection & Analytics

Real-time analysis of data relationships is essential to uncovering fraud rings and other sophisticated scams before fraudsters and criminals cause lasting.

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Healthcare & Life Sciences

Pharmaceutical companies, chemical manufacturers and biotech companies are using Neo4j to analyze data in ways not previously possible without graphs.

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Real-Time Recommendations

Graph-powered recommendation engines help companies personalize products, content and services by leveraging a multitude of connections in real time.

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Supply Chain Management

Graph technology is essential to optimize the flow of goods, uncover vulnerabilities and boost overall supply chain resilience. Discover how Transparency-One, Caterpillar and others use graph technology to ensure business...

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Explore All Use Cases

Loved by Devs. Deployed Worldwide.

1,700+ organizations build on Neo4j for data breakthroughs.

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Data Insights & Results

  • “Neo4j AuraDB aligns well with Dun & Bradstreet’s cloud strategy and helps us to deliver the deep knowledge our clients need to take intelligent actions based on timely compliance decisions.”

    Matthew Voss, Identity Insights Leader

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  • “Neo4j AuraDB helps us visualize our data and the opportunities in one place, so we can focus our efforts on streamlining our service delivery and operations.”

    Luke Slowen, Enterprise Architect

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  • “The schema flexibility and analytical capabilities of Neo4j allow us to see relationships between manufacturing batches that simply would not be possible with other tools.”

    Eric Wespi, Data Scientist

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  • “Our developer team is able to calculate the risk score of every asset, defined as any piece of software, server, services, website, source code, and more, in just four minutes. These are complex traversals ... we’re able to do them extremely quickly.”

    Zach Probst, Staff Software Engineer

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  • "We’re excited by Neo4j’s latest enhancements to their graph database solutions, all of which have the potential to take our data and processing and analytics to the next level and achieve unprecedented levels of insight and decision-making.”

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Get Started Today!

AuraDB Free

$0

Learn and Explore Graphs

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AuraDB Professional

$65
/GB/month
(minimum 1GB cluster)

Build Production-Ready Apps

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AuraDB Business Critical

$146
/GB/month
(minimum 2GB cluster)

Scale Apps for Enterprise Use

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AuraDB Virtual Dedicated Cloud

Deploy applications, using all capabilities of AuraDB Business Critical, but in a dedicated environment

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Works Seamlessly With Your Tech Stack

Neo4j integrates with leading technology providers

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Tools and Tips to Build Intelligent Apps

Get started graphing with AuraDB.

AuraDB Frequently Asked Questions

  • How does AuraDB differ from traditional relational databases?

    AuraDB is a purpose-built, high-performance native graph database engine that stores and navigates relationships between data elements directly. Relational databases, use JOINs to access related data. In contrast, AuraDB handles relationships natively using pointers, eliminating the associated performance costs. Learn more

    AuraDB also supports ACID transactional properties, ensuring that all data changes are consistent and reliable. This guarantees data integrity even in multi-user, highly concurrent environments.

    This robust architecture enables Neo4j customers to build reliable transactional and analytical apps with confidence in their data’s consistency and integrity. Learn more about Neo4j AuraDB's database internals.

  • What programming languages and drivers does AuraDB support?

    AuraDB empowers developers with high query performance, a flexible data model, and broad language support, making migration easy. AuraDB supports a wide range of programming languages, with official drivers for .NET, Java, JavaScript, Go, and Python.

    Additionally, community contributors provide drivers for many other languages, including PHP, Ruby, R, Erlang, Clojure, C/C++, and more. Learn more about available drivers.

  • What security and regulatory certification standards does Neo4j Aura meet?

    Aura complies with key security and regulatory standards, including CCPA, ISO 27001, ISO 20243, SOC2 Type II, SOC3, HIPAA and GDPR.

    Visit Neo4j's Trust Center at https://trust.neo4j.com/ for comprehensive security and compliance information.

  • What migration and interoperability tools does Neo4j provide?

    Neo4j supports various approaches for migration and interoperability. It offers standard APIs like GraphQL, which work across different database models. Neo4j also supports the ISO standard graph query language GQL, with mature implementations like Cypher and OpenCypher, and provides a plugin for Apache Gremlin.

    Neo4j includes Sql2Cypher, a tool that converts SQL to Cypher, often simplifying and improving query readability for those migrating from SQL. Additionally, Neo4j provides SQL access through BI connectors.

  • How does Neo4j help developers optimize query performance?

    Neo4j’s “EXPLAIN and PROFILE” options in the Cypher query language help architects and developers identify where indexes can improve query performance. Neo4j doesn’t need JOINs to access related/connected data. This means Neo4j doesn’t need JOIN indexes, materialized views, or other components that relational databases use to work around the fact that they don’t store relationships directly.

    In short, Neo4j uses indexes as a way to find the Node(s) (entities or discrete objects of a domain) that are the starting point for a graph query. It doesn’t need additional indexes, materialized views, or other components to speed up queries of related data.

  • What data integration and quality tools are available in Neo4j AuraDB?

    AuraDB has multiple data integration and data quality tools built into the database, including:

    • Cypher query language and Graph Data Science algorithm for data transformation, aggregation, preparation, and feature generation.
    • Python client for direct integration with Pandas data frames, enabling data scientists to leverage Python libraries for data preparation and transformation.
    • APOC library with stored procedures for data aggregation, formatting, cleaning, and engineering.
    • Connectors (Spark, Kafka, Data Warehouse) for data movement and transformation.
    • Data Importer for no-code graph data modeling and loading.