Explain the features of Amazon Neptune

In this recipe, we will learn about Amazon Neptune. We will also learn about the features of Amazon Neptune.

Recipe Objective - Explain the features of Amazon Neptune?

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 its features of Amazon Neptune.

Features of Amazon Neptune

    • It provides Graph Queries with High Throughput and Low Latency

Amazon Neptune is a high-performance graph database engine designed specifically for Amazon. Neptune is a graph data storage and navigation system that uses a scale-up, in-memory optimised architecture to allow for fast query evaluation over large graphs. Users can use Gremlin or SPARQL with Neptune to run powerful queries that are simple to write and perform well.

    • It provides Database Computer Resources that Can Be Scaled Easily

Users can scale the compute and memory resources powering your production cluster up or down with a few clicks in the Amazon Web Services Management Console by creating new replica instances of the desired size or removing instances. Compute scaling operations usually take a few minutes to complete.

    • It provides Instance Monitoring and Repair

Users' Amazon Neptune database and its underlying EC2 instance are constantly monitored for health. The database and associated processes are automatically restarted if the instance that powers your database fails. Because Neptune recovery avoids the time-consuming replay of database redo logs, instance restart times are typically 30 seconds or less. The database buffer cache is also isolated from database processes, allowing it to survive a database restart.

    • It provides multi-AZ Deployments with reading Replicas

Amazon Neptune automates failover to one of up to 15 Neptune replicas users have created in any of three Availability Zones when an instance fails. If no Neptune replicas have been provisioned, Neptune will attempt to create a new database instance for users automatically in the event of a failure.

    • It provides backups that are automatic, continuous, incremental, and restore data to a specific point in time

The backup feature of Amazon Neptune allows for point-in-time recovery of your instance. This allows users to restore your database to any point in time up to the last five minutes of their retention period. The retention period for their automatic backups can be set to up to 35 days. Amazon S3, which is designed for 99.999999999 per cent durability, is used to store automated backups. Automatic, incremental, and continuous Neptune backups have no impact on database performance.

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I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

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