Explain the features of Amazon Route 53

In this recipe, we will learn about Amazon Route 53 We will also learn about the features of Amazon Route 53.

Recipe Objective - Explain the features of Amazon Route 53?

The Amazon Route 53 is widely used and defined as a service that is a highly available and scalable cloud Domain Name System (DNS) web service. Amazon Route 53 is designed to give the developers and businesses an extremely reliable and the cost-effective way to route end users to Internet applications by translating names like www.example.com into numeric IP addresses like 192.0.2.1 which computers use to connect to each other. Amazon Route 53 is a fully compliant service with IPv6 as well. Amazon Route 53 links user requests to AWS infrastructure, such as the Amazon EC2 instances, Elastic Load Balancing load balancers, or Amazon S3 buckets, and may also be used to route users to infrastructure that is not hosted by AWS. Users can also use the Amazon Route 53 to establish the DNS health checks, then utilise Route 53 Application Recovery Controller to continually monitor their applications' capacity to recover from failures and regulate application recovery. Amazon Route 53 Traffic Flow enables users to manage traffic worldwide by utilising a range of routing types, such as Latency Based Routing, Geo DNS, Geoproximity, and Weighted Round Robin—all of which may be used with DNS Failover to provide a variety of low-latency, fault-tolerant designs. Users can quickly configure how their end-users are routed to their application's endpoints with the Amazon Route 53 Traffic Flow's intuitive visual editor, whether they are in a single AWS region or dispersed across the world. Amazon Route 53 also provides Domain Name Registration, which allows users to buy and administer domain names like example.com, and Amazon Route 53 will automatically create DNS settings for their domains.

ETL Orchestration on AWS using Glue and Step Functions

Benefits of Amazon Route 53

  • The Amazon Route 53 is built on Amazon Web Services' highly available and dependable infrastructure and its DNS servers are spread, which helps to provide a constant ability to route users end customers to their application. Amazon Route 53 Traffic Flow and routing management, for example, can help users increase dependability by rerouting their customers to an alternate destination if the user's original application endpoint becomes unavailable. Amazon Route 53 is intended to provide the reliability demanded by critical applications and thus is highly available and reliable. Amazon Route 53 on Amazon Traffic Flow directs the traffic depending on a variety of factors, including endpoint health, geographic location, and latency and Users may set up various traffic regulations and choose which ones to use at any given moment. Users may build and change traffic policies via the Route 53 interface, AWS SDKs, or the Route 53 API using the easy visual editor So, the versioning function in Traffic Flow keeps track of changes to user's traffic policies, allowing users to quickly roll back to a prior version through the interface or API and thus it provides flexibility. Amazon Route 53 intends to complement other AWS technologies and offerings. Amazon Route 53 may be used to map domain names to Amazon EC2 instances, Amazon S3 buckets, Amazon CloudFront distributions, and other AWS services and also Users can fine-tune who may change their DNS data by combining the AWS Identity and Access Management (IAM) service with the Amazon Route 53. Using a feature called Alias record, Users may utilise Amazon Route 53 to link their zone apex (example.com versus www.example.com) to their Elastic Load Balancing instance, Amazon CloudFront distribution, AWS Elastic Beanstalk environment, API Gateway, VPC endpoint, or the Amazon S3 website bucket.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Route 53 and the Features of Amazon Route 53.

Features of Amazon Route 53

    • It uses a Global Network

Amazon Route 53 uses a global network of DNS servers present at a series of worldwide locations to offer users a high availability and increased performance. Route 53 uses more than 50+ countries as its location served in all continents except Antarctica.

    • It provides API's

Amazon Route 53 provides a simple set of APIs which make it easy to create and manage DNS records for user's domains. Users can call these directly that is all this functionality can also be accessed via the main AWS Management Console. The API CreateHostedZone is used for creating a new hosted zone to contain the user's DNS data. After creating a Hosted Zone, users receive four name servers to which users can delegate their domain. Also, the API ChangeResourceRecordSets is used and defined as the API which populates and edits the DNS resource records in a hosted zone.

    • It provides DNS

Amazon Route 53 provides DNS and is an “authoritative DNS” system that is an authoritative DNS system provides an update mechanism that developers use to manage their public DNS names. Further, it then answers DNS queries, translating domain names into IP addresses so that computers can communicate with each other. The name for the user's service (Route 53) comes from the fact that the DNS servers respond to the queries on port 53 and provide answers that further route end users to the user's applications on the Internet.

    • It provides various Functionality

Amazon Route 53 has a simple web-services interface that lets users get started in minutes. Also, users DNS records are organized into “hosted zones” that users configure with the Route 53’s API. The user's hosted zone will be initially populated with the basic set of DNS records, including four virtual name servers that will answer queries for the user's domain. Further, users can add, delete or change the records in this set using the AWS Management Console or by calling ChangeResourceRecordSetAPI.

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

Build Streaming Data Pipeline using Azure Stream Analytics
In this Azure Data Engineering Project, you will learn how to build a real-time streaming platform using Azure Stream Analytics, Azure Event Hub, and Azure SQL database.

GCP Project to Learn using BigQuery for Exploring Data
Learn using GCP BigQuery for exploring and preparing data for analysis and transformation of your datasets.

Analyse Yelp Dataset with Spark & Parquet Format on Azure Databricks
In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.

Getting Started with Pyspark on AWS EMR and Athena
In this AWS Big Data Project, you will learn to perform Spark Transformations using a real-time currency ticker API and load the processed data to Athena using Glue Crawler.

Learn Efficient Multi-Source Data Processing with Talend ETL
In this Talend ETL Project , you will create a multi-source ETL Pipeline to load data from multiple sources such as MySQL Database, Azure Database, and API to Snowflake cloud using Talend Jobs.

Build a Scalable Event Based GCP Data Pipeline using DataFlow
In this GCP project, you will learn to build and deploy a fully-managed(serverless) event-driven data pipeline on GCP using services like Cloud Composer, Google Cloud Storage (GCS), Pub-Sub, Cloud Functions, BigQuery, BigTable

Build a real-time Streaming Data Pipeline using Flink and Kinesis
In this big data project on AWS, you will learn how to run an Apache Flink Python application for a real-time streaming platform using Amazon Kinesis.

Deploying auto-reply Twitter handle with Kafka, Spark and LSTM
Deploy an Auto-Reply Twitter Handle that replies to query-related tweets with a trackable ticket ID generated based on the query category predicted using LSTM deep learning model.

PySpark Project-Build a Data Pipeline using Hive and Cassandra
In this PySpark ETL Project, you will learn to build a data pipeline and perform ETL operations by integrating PySpark with Hive and Cassandra

Retail Analytics Project Example using Sqoop, HDFS, and Hive
This Project gives a detailed explanation of How Data Analytics can be used in the Retail Industry, using technologies like Sqoop, HDFS, and Hive.