Introduction to Amazon Neptune and its use cases

In this recipe, we will learn about Amazon Neptune. We will also learn about the use cases of Amazon NEptune.

Recipe Objective - Introduction to Amazon Neptune and its use cases?

The Amazon Neptune is a widely used service and is defined as a fully managed graph database service that makes it simple to create and run applications that work with large, interconnected datasets. Amazon Neptune is powered by a purpose-built, high-performance graph database engine that can store billions of relationships and query them in milliseconds. Amazon Neptune supports the popular graph models Property Graph and W3C's RDF, as well as their query languages Apache TinkerPop Gremlin and SPARQL, making it simple to create queries that efficiently navigate highly connected datasets. Recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security are just a few of the graph use cases that Neptune powers. With read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across Availability Zones, Amazon Neptune is highly available. With support for HTTPS encrypted client connections and encryption at rest, Neptune is safe. Users no longer have to worry about database management tasks like hardware provisioning, software patching, setup, configuration, or backups because Neptune is fully managed. Users don't have to worry about database management tasks like hardware provisioning, software patching, setup, configuration, or backups with Amazon Neptune. Neptune monitors and backs up its database to Amazon S3 in real-time, allowing for granular point-in-time recovery. Amazon CloudWatch can be used to track database performance.

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Benefits of Amazon Neptune

  • Both Gremlin and SPARQL have open graph APIs, and Amazon Neptune provides high performance for both graph models and query languages. It allows users to choose between the Property Graph model and Apache TinkerPop Gremlin, an open source query language, and the W3C standard Resource Description Framework (RDF) model and SPARQL, a standard query language and thus it supports Open graph APIs. Amazon Neptune is a high-performance graph database designed specifically for Amazon. It is designed to handle graph queries. To scale read capacity and execute more than 100,000 graph queries per second, Neptune supports up to 15 low latency read replicas spread across three Availability Zones. As users' needs change, users can easily scale their database deployment from smaller to larger instance types and thus it offers high performance and scalability. Amazon Neptune is highly available, long-lasting, and compliant with the ACID (Atomicity, Consistency, Isolation, and Durability) standards. Neptune is designed to have a 99.99 per cent availability rate. It has fault-tolerant and self-healing cloud storage with six copies of users' data replicated across three Availability Zones. Neptune automatically backs up users' data to Amazon S3 and recovers from physical storage failures in real-time. Instance failover in High Availability typically takes less than 30 seconds and thus it offers high availability and durability. For the user's database, Amazon Neptune provides multiple levels of security, including network isolation via Amazon VPC, support for IAM authentication for endpoint access, HTTPS encrypted client connections, and encryption at rest via Amazon Key Management Service keys users create and control (KMS). Data in the underlying storage, as well as automated backups, snapshots, and replicas in the same cluster, are all encrypted on an encrypted Neptune instance and thus offer security.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Neptune and the Use cases of Amazon Neptune.

Use cases of Amazon Neptune

    • It has a use case in social networking

To create social networking applications, Amazon Neptune can quickly and easily process large sets of user-profiles and interactions. Neptune allows users to integrate social features into your apps by allowing highly interactive graph queries with high throughput. If users are creating a social feed for their app, for example, users can use Neptune to prioritise showing their users the most recent updates from their family, friends whose updates they like,' and friends who live close to them.

    • It has a use case for Recommendation engine

Amazon Neptune lets users store relationships between data points like customer interests, friends, and purchase history in a graph and query it quickly to generate personalised and relevant recommendations. For example, users can use Neptune to make product recommendations to a user based on which products have been purchased by others who follow the same sport and have similar purchase histories. Alternatively, users can find people who share a friend but haven't met yet and make a friendship recommendation.

    • It has a use case for fraud detection

Users can use Amazon Neptune to process financial and purchase transactions in near real-time, making it easy to spot fraud patterns. Neptune offers a fully managed service that runs fast graph queries to see if a potential buyer is using the same email address and credit card as a previously reported fraud case. If users are working on a retail fraud detection app, Neptune can help them create graph queries to quickly spot patterns like multiple people using the same personal email address or multiple people using the same IP address but living at different physical addresses.

    • It has a use case for knowledge graphs

Amazon Neptune aids in the development of knowledge graph applications. A knowledge graph allows users to store data in a graph model and use graph queries to help users navigate large, interconnected datasets. Neptune supports open source and opens standard APIs, allowing users to quickly build knowledge graphs and host them on a fully managed service by leveraging existing information resources. If a user is interested in The Mona Lisa, for example, users can direct them to other works by Leonardo da Vinci or other works of art in The Louvre.

    • It has a use case in Life sciences

Amazon Neptune enables users to create life sciences applications that store and navigate data, as well as process sensitive data with ease using encryption at rest. Neptune, for example, can be used to store disease models and gene interactions, as well as to search for graph patterns within protein pathways to discover other genes that may be linked to a disease. Chemical compounds can be represented as graphs, and patterns in molecular structures can be searched for. Neptune can also assist you in integrating data to solve problems in healthcare and life sciences research. Neptune can be used to create and store data across multiple systems, as well as to organise research publications by topic to find relevant information quickly.

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