Amazon Neptune

High-performance graph analytics and serverless database for superior scalability and availability

Amazon Neptune Database

Neptune Database is a serverless graph database designed for superior scalability and availability. Neptune Database provides built-in security, continuous backups, and integrations with other AWS services. Neptune Global Database offers cross-Region data replication for low-latency reads and writes, disaster recovery, and scalability for globally distributed applications.

Amazon Neptune Analytics 

Neptune Analytics is an analytics database engine for quickly analyzing large volumes of graph data to get insights and find trends from data stored in Amazon S3 buckets or Neptune Database. Neptune Analytics uses built-in algorithms, vector search, and in-memory computing to run queries on data with tens of billions of relationships in seconds.

Benefits

Scale your graphs with unlimited vertices and edges and more than 100,000 queries per second for the most demanding applications. Storage scales up to 128 TiB per cluster and read scales up to 15 replicas per cluster.

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Find insights faster by analyzing graph datasets with tens of billions of relationships within seconds using built-in algorithms. Perform similarity searches on vectors stored along with your graph for gen AI apps.

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Secure your applications with features including ACID transactions, automated backups (with Neptune Database), snapshots, point-in-time recovery, encryption at transit and at rest, AWS Identity and Access Management (IAM) support, and AWS Key Management Service (AWS KMS).

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Provide high availability for your applications and make your data durable across three Availability Zones (AZs) in an AWS Region. Improve your disaster recovery posture and access local reads with Amazon Neptune Global Database.

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Use cases

Easily build profile graphs with a 360-degree view of your customers. Improve customer personalization, increase marketing relevancy, and advance your analytics.

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Build graph queries for near real-time fraud pattern detection by modeling relationships between people, places, and transactions to discover relationships that might not be obvious.

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Amazon Neptune ML uses graph neural networks (GNNs) to improve the accuracy of most predictions for graphs by over 50% when compared to predictions using nongraph methods.

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Proactively detect and investigate IT infrastructure using a layered security approach. Model assets to relationships to see how different dimensions of your IT environment interact.

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