- Benefits:
- Supports open graphics APIs.
Amazon Neptune offers open graph APIs for Gremlin and SPARQL, providing high performance for both graph models and their query languages. The service allows you to choose either the Property Graph model and its open-source query language, Apache TinkerPop Gremlin; or the W3C-standard Resource Description Framework (RDF) model and its standard query language, SPARQL.
- High performance and scalability.
Amazon Neptune is a purpose-built, high-performance graph database. This database is optimized for processing graph queries. Neptune supports up to 15 low-latency read replicas across three Availability Zones to scale read capacity and run over 100,000 graph queries per second. You can easily scale your deployment up and down and use smaller or larger instances as your needs evolve.
- High availability and resilience.
Amazon Neptune is highly available, resilient, and ACID (atomicity, consistency, isolation, resiliency) compliant. Neptune is designed to deliver greater than 99.99% availability. With fault-tolerant storage and automatic recovery, the service is built for the cloud and allows you to replicate six copies of data across three Availability Zones. Neptune continuously backs up data to Amazon S3 and transparently recovers from physical storage failures. High availability provides instance failover that typically takes less than 30 seconds.
Amazon Neptune provides multiple levels of database security, including network isolation using Amazon VPC, support for IAM authentication for endpoint access, encrypted HTTPS client connections, and encryption at rest using keys created and controlled through AWS Key Management Service (KMS). In an encrypted Neptune instance, the underlying storage data is encrypted, as are backups, replicas, and automated snapshots within the same cluster.
With Amazon Neptune, you no longer have to worry about database administration tasks such as hardware provisioning, software patching, installation, configuration, or backups. Neptune automatically and continuously monitors and backs up databases to Amazon S3, enabling granular point-in-time recoveries. Database performance can be monitored with Amazon CloudWatch.
– Use cases:
Amazon Neptune helps you build knowledge graph applications. A knowledge graph lets you store information in a graph template and use graph queries to easily navigate highly connected data sets. Neptune supports open-source APIs and open standards to enable you to quickly leverage existing information resources to create knowledge graphs and host them in a fully managed service. For example, if a user is interested in the Mona Lisa, you can help them discover other works of art by Leonardo da Vinci or located in the Louvre. Using a knowledge graph lets you add topical information to product catalogs, create and query complex regulatory rule models, or model general information such as Wikidata.
You can use Neptune to create identity graphs for any identity resolution solution, including device and social network graphs, personalization and recommendations, and pattern detection. Using a graph database for an identity graph allows you to easily link identifiers and update profiles. It also enables ultra-low-latency queries, enabling faster updates and up-to-date profile data for targeting, personalization, data analysis, and ad allocation.
With Amazon Neptune, you can use relationships to process financial and purchasing transactions in near real-time to easily detect fraud patterns. Neptune offers a fully managed service to run fast graph queries to detect if a potential buyer is using the same email address and credit card as a known fraudster. If you're building a retail fraud detection application, Neptune can help you create graph queries to easily detect relationship patterns, such as multiple people associated with a personal email address or multiple people sharing the same IP address but residing at different physical addresses.
- Recommendation mechanisms.
Amazon Neptune allows you to store relationships between information—such as customer interests, friends, and purchase history—in a graph and quickly query that graph to make relevant and personalized recommendations. For example, with Neptune, you can use a highly available graph database to make product recommendations to a user based on products purchased by others who follow the same sport and have a similar purchase history. Additionally, you can identify people with a mutual friend who haven't met yet and make a friend recommendation.
Amazon Neptune can quickly and easily process large sets of user profiles and interactions to build social networking applications. Neptune enables highly interactive and high-performance graph queries to bring social features to applications. For example, if you're building a social feed in an application, you can use Neptune to provide results that prioritize showing users the latest updates from their family, friends whose updates users like, and friends who live near users.
You can use Amazon Neptune to store a graph of your network and use graph queries to answer questions like how many hosts are running a specific application. Neptune can store and process billions of events to manage and secure your network. If you detect an event that is an anomaly, you can use Neptune to quickly understand how it might affect your network by running a graph pattern using the event attributes. You can query Neptune to find other hosts or devices that may be compromised. For example, if you detect a malicious file on one host, Neptune can help you find connections between the hosts that distributed the malicious file and allow you to trace that file back to the original host where it was downloaded.
Amazon Neptune helps you build applications that store and navigate life sciences information and easily process sensitive data by encrypting data at rest. For example, you can use Neptune to store models of diseases and genetic interactions, as well as search for graph patterns in protein pathways to find other genes that may be associated with a disease. You can model chemical compounds as a graph and search for patterns in molecular structures. Additionally, Neptune helps you integrate information to solve life sciences and healthcare research challenges. You can use Neptune to create and store data across different systems and organize publications that are searchable by topic to quickly find relevant information.
