System design: Microservices Full Course - Learn Microservices in 4 Hours | Microservices Tutorial | Edureka

 May 1, 2021

Here is the link. 

( Microservices Architecture Training: https://www.edureka.co/microservices-...​ ) This Edureka Microservices Full Course video will help you learn Microservices from scratch with examples. This Microservices Tutorial is ideal for both beginners as well as professionals who want to master the Microservices Architecture. Below are the topics covered in this Microservices Tutorial for Beginners video: 00:00​ Agenda 1:39​ Introduction to Microservices 1:43​ Why Microservices? 8:18​ What is Microservice Architecture? 10:17​ Microservice Architecture 12:35​ Features of Microservice Architecture 13:46​ Advantages of Microservice Architecture 15:11​ Companies using Microservice 16:29​ Hands-On 20:52​ Microservice Integration 20:55​ Microservices with Spring Boot 21:24​ Challenges with Microservice Architecture 23:23​ Need for Spring Boot 24:09​ Use Case 26:07​Tools Required 40:41​ Microservices with Docker 41:18​ Use-Case 43:45​ Before and After Microservices 44:36​ Microservices Architecture 46:05​ What is Docker? 52:31​ Docker for Microservices 52:57​ Advantages of Docker in Microservices 56:29​ Implementation 1:11:59​ Microservices vs SOA 1:14:49​ SOA vs Microservice 1:15:53​ Microservices vs SOA Architecture 1:23:20​ Use-Case 1:29:41​ Microservices vs API 1:30:40​ 1:31:49​ Monolithic Architecture Example 1:32:42​ What are API's? 1:35:43​ Where are API's used in Microservices? 1:38:25​ Differences between Microservices and API 1:41:08​ Microservices Design Patterns 1:41:56​ Need for Design Patterns 1:44:10​ What are Design Patterns? 1:44:58​ Principles 1:49:25​ Microservices Design Patterns 1:49:27​ Aggregator 1:52:28​ API Gateway 1:55:00​ Chained Or Chain of Responsibility 1:57:11​ Asynchronous Messaging 1:59:07​ Databases 2:01:32​ Event Sourcing 2:02:38​ Branch 2:03:48​ CQRS 2:05:14​ Circuit Breaker 2:07:20​ Decomposition 2:09:57​ Microservices Tools 2:11:58​ Operating System 2:13:14​ Programming Language 2:15:48​ API Management and Testing 2:18:28​ Messaging 2:20:23​ Toolkits 2:22:08​ Architectural Frameworks 2:24:01​ Orchestration 2:26:37​ Monitoring 2:28:01​ Serverless 2:30:05​ Microservices Security 2:30:51​ Problems 2:34:47​ Best Practices 2:47:32​ Interview Questions #edureka​ #microservicesedureka​ #microservices​ #microservicearchitecture​ #microservicearchitecturetutorial​ #microservicesforbeginners​ #microservicesinandout​ #microservicesinterviewquestions​ #microserviceswithspringboot​ #microservicesdesign​ --------------------------------------------------------------------------------- Join Edureka’s Meetup community and never miss any event – YouTube Live, Webinars, Workshops, etc. https://bit.ly/2EfTXS1​ Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV​ SlideShare: https://www.slideshare.net/edurekaIN​ Instagram: https://www.instagram.com/edureka_lea...​ Facebook: https://www.facebook.com/edurekaIN/​ Twitter: https://twitter.com/edurekain​ LinkedIn: https://www.linkedin.com/company/edureka​

System design: AWS CloudFormation | My first 20 minutes study

May 1, 2021

Here is the link.

AWS CloudFormation gives you an easy way to model a collection of related AWS and third-party resources, provision them quickly and consistently, and manage them throughout their lifecycles, by treating infrastructure as code. A CloudFormation template describes your desired resources and their dependencies so you can launch and configure them together as a stack. You can use a template to create, update, and delete an entire stack as a single unit, as often as you need to, instead of managing resources individually. You can manage and provision stacks across multiple AWS accounts and AWS Regions.

Automate best practices

With CloudFormation, you can apply DevOps and GitOps best practices using widely adopted processes such as starting with a git repository and deploying through a CI/CD pipeline. You can also simplify auditing changes and trigger automated deployments with pipeline integrations such as GitHub Actions and AWS CodePipeline.

Scale your infrastructure worldwide

Manage resource scaling by sharing CloudFormation templates to be used across your organization, to meet safety, compliance, and configuration standards across all AWS accounts and regions. Templates and parameters enable easy scaling so you can share best practices and company policies. Additionally, CloudFormation StackSets enables you to create, update, or delete stacks across multiple AWS accounts and Regions, with a single operation.

Integrate with other AWS services

To further automate resource management across your organization, you can integrate CloudFormation with other AWS services, including AWS Identity and Access Management (IAM) for access control, AWS Config for compliance, and AWS Service Catalog for turnkey application distribution and additional governance controls. Integrations with CodePipeline and other builder tools let you implement the latest DevOps best practices and improve automation, testing, and controls.

Manage third-party and private resources

Model, provision, and manage third-party application resources (such as monitoring, team productivity, incident management, CI/CD, and version control applications) alongside your AWS resources. Use the open source CloudFormation CLI to build your own CloudFormation resource providers – native AWS types published as open source.

Extend CloudFormation with the community

The AWS CloudFormation GitHub organization offers open source projects that extend CloudFormation’s capabilities. The CloudFormation Registry and CloudFormation CLI let you define and create resource providers to automate the creation of resources safely and systematically. Using CloudFormation GitHub projects, you can do things like check CloudFormation templates for policy compliance (using cfn-guard), or validate use of best practices (using cfn-lint).

System design: Amazon Athena | My first 20 minutes study

 May 1, 2021

Here is the link. 

Movable Ink uses AWS to query seven years’ worth of historical data and get results in moments, with the flexibility to explore data for deeper insights. Movable Ink provides real-time personalization of marketing emails based on a wide range of user, device, and contextual data, driving higher response rates and better customer experiences. The company uses the Amazon Athena serverless query service to analyze data stored in Amazon S3, gaining insights to improve results for customers’ marketing campaigns.

Using Data to Drive Email Marketing That Works Movable Ink’s Intelligent Content Platform supports real-time personalization of email campaigns using up-to-date information from websites, social-media platforms, and APIs, as well as contextual data about device type, weather, recent user activity, and more. Additionally, it enables customers to analyze data about their users to make better marketing decisions. This process incorporates large and unpredictable amounts of data from a wide variety of sources, making the scale, elasticity, and connected nature of cloud services a logical fit. To meet these challenges, Movable Ink migrated its entire production environment to Amazon Web Services (AWS) in 2015, taking advantage of multiple regions and availability zones to provide redundancy, resilience, and scalability. In addition to the data and content used in personalized email messages, Movable Ink captures data on user behavior after users receive those messages, such as whether they opened the email, what items they clicked on, and what they browsed and purchased on websites as a result. 

This data is used by Movable Ink in its client-billing systems, in reporting results to clients, and as the basis for building new services such as recommendation engines. Movable Ink uses Amazon Elastic MapReduce (Amazon EMR) clusters to capture data about user actions and push it to Amazon Simple Storage Service (Amazon S3). Movable Ink has been collecting data on user actions since 2011, and this database grows by up to 100 GB per day. To reduce time to insight, optimize costs, and increase flexibility for its analytics, the company recently adopted the serverless Amazon Athena query service. Amazon Athena enables interactive querying of large-scale data sets in Amazon S3 using standard SQL. It eliminates the need for complex extract, transform, and load (ETL) jobs to prepare data for analysis, and delivers most results within seconds. Transforming Discovery with Serverless Querying Before adopting Amazon Athena, Movable Ink was using Apache Hive for querying user-activity data. However, this solution lacked the necessary performance and added cost and management complexity. Movable Ink had to keep an Amazon EMR cluster running to load data into local databases when queries were performed. Since the company began using Amazon Athena, it has realized both cost savings and improved performance for analytics related to user actions. “One of the big attractions of Amazon Athena is that it’s serverless and purely consumption-based,” says Matt Chesler, director of DevOps at Movable Ink. “We only pay when we’re actually querying the data, and we don’t have to keep a cluster running all the time. Using Amazon Athena, we’re able to query seven years’ worth of data—adding up to hundreds of terabytes— get results at least 50 percent faster, and save nearly $15,000 per month.” Better Performance at Lower Cost Movable Ink has optimized its approach to Athena to achieve the best cost-to-performance ratio. “For data we use often, we have invested in optimizations such as repartitioning and changing data from row based to columnar to further reduce costs.” Movable Ink has also built a microservice that caches data so users running similar queries do not generate a call to Amazon Athena with each request. In addition, Movable Ink has taken advantage of API access built into Amazon Athena to provide programmatic access to data. “Previously, we would run a query against Hive and load it into a local database instance, which was a fragile and slow process,” says Chesler. “With Amazon Athena API access, we can run the query directly against Amazon S3. Besides avoiding the need to create and manage local database instances, the big benefit of this is that our users can modify the query on the fly to explore data and get deeper insights.”

Benefits 

• Improved performance by 50% compared to persistent cluster solution 
• Reduced costs by $15,000 per month 
• Provided analysts with the flexibility to adapt queries on the fly for richer insights 
• Adopted pay-as-you-go querying to optimize costs 
• Eliminated the need to manage persistent database instances 

AWS Services Used 

• Amazon Athena 
• Amazon Elastic MapReduce 
• Amazon S3 

System design: Amazon EMR | My first 20 minutes study

May 1, 2021

Here is the article. 

Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. Amazon EMR makes it easy to set up, operate, and scale your big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters. With EMR you can run petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. You can run workloads on Amazon EC2 instances, on Amazon Elastic Kubernetes Service (EKS) clusters, or on-premises using EMR on AWS Outposts.

Easy to use

You can use EMR Studio, an integrated development environment (IDE), to easily develop, visualize, and debug data engineering and data science applications written in R, Python, Scala, and PySpark. EMR Studio uses AWS Single Sign-On and allows you to log in directly with your corporate credentials. It provides fully managed Jupyter Notebooks and collaboration with peers using code repositories such as GitHub and BitBucket.

Low cost

EMR pricing is simple and predictable: You pay a per-instance rate for every second used, with a one-minute minimum charge. You can launch a 10-node EMR cluster for as little as $0.15 per hour. You can save 50-80% on the cost of the instances by selecting Amazon EC2 Spot for transient workloads and Reserved Instances for long-running workloads. You can also use Savings Plans.

Elastic

Unlike the rigid infrastructure of on-premises clusters, EMR decouples compute and storage, giving you the ability to scale each independently and take advantage of the tiered storage of Amazon S3. With EMR, you can provision one, hundreds, or thousands of compute instances or containers to process data at any scale. The number of instances can be increased or decreased automatically using Auto Scaling (which manages cluster sizes based on utilization) and you only pay for what you use.

Reliable

Spend less time tuning and monitoring your cluster. EMR is tuned for the cloud and constantly monitors your cluster — retrying failed tasks and automatically replacing poorly performing instances. Clusters are highly available and automatically failover in the event of a node failure. EMR provides the latest stable open source software releases, so you don’t have to manage updates and bug fixes, which leads to fewer issues and less effort to maintain your environment.

Secure

EMR automatically configures EC2 firewall settings, controlling network access to instances and launches clusters in an Amazon Virtual Private Cloud (VPC). Server-side encryption or client-side encryption can be used with the AWS Key Management Service or your own customer-managed keys. EMR makes it easy to enable other encryption options, like in-transit and at-rest encryption, and strong authentication with Kerberos. You can use AWS Lake Formation or Apache Ranger to apply fine-grained data access controls for databases, tables, and columns.

Flexible

You have complete control over your EMR clusters and your individual EMR jobs. You can launch EMR clusters with custom Amazon Linux AMIs and easily configure the clusters using scripts to install additional third party software packages. EMR enables you to reconfigure applications on running clusters on the fly without the need to relaunch clusters. Also, you can customize the execution environment for individual jobs by specifying the libraries and runtime dependencies in a Docker container and submit them with your job.

System design: AWS Glue | My 20 minutes study

May 1, 2021

Here is the article. 

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS Glue provides all of the capabilities needed for data integration so that you can start analyzing your data and putting it to use in minutes instead of months.

Data integration is the process of preparing and combining data for analytics, machine learning, and application development. It involves multiple tasks, such as discovering and extracting data from various sources; enriching, cleaning, normalizing, and combining data; and loading and organizing data in databases, data warehouses, and data lakes. These tasks are often handled by different types of users that each use different products.

AWS Glue provides both visual and code-based interfaces to make data integration easier. Users can easily find and access data using the AWS Glue Data Catalog. Data engineers and ETL (extract, transform, and load) developers can visually create, run, and monitor ETL workflows with a few clicks in AWS Glue Studio. Data analysts and data scientists can use AWS Glue DataBrew to visually enrich, clean, and normalize data without writing code. With AWS Glue Elastic Views, application developers can use familiar Structured Query Language (SQL) to combine and replicate data across different data stores.

Faster data integration

Different groups across your organization can use AWS Glue to work together on data integration tasks, including extraction, cleaning, normalization, combining, loading, and running scalable ETL workflows. This way, you reduce the time it takes to analyze your data and put it to use from months to minutes.

Automate your data integration at scale

AWS Glue automates much of the effort required for data integration. AWS Glue crawls your data sources, identifies data formats, and suggests schemas to store your data. It automatically generates the code to run your data transformations and loading processes. You can use AWS Glue to easily run and manage thousands of ETL jobs or to combine and replicate data across multiple data stores using SQL.

No servers to manage

AWS Glue runs in a serverless environment. There is no infrastructure to manage, and AWS Glue provisions, configures, and scales the resources required to run your data integration jobs. You pay only for the resources your jobs use while running.

System design: Amazon Elastic Block Store (Amazon EBS) | Slack case study

May 1, 2021

Here is the article. 

Why Amazon Web Services

Crowley says Slack turned to Amazon Web Services out of experience and because it was the best choice for the company going forward. Tiny Speck—the original company name for what became Slack Technologies—used AWS in 2009 when it was the only viable offering for public cloud services.

“Given their expertise and pains running a more traditional environment when Flickr was developed, Slack’s founders realized it was a no brainer to use AWS,” says Crowley. “During the development of Slack, the feeling was that AWS was good to us and would continually improve with more and better features. There was no need to leave.”

Slack has a relatively simple IT architecture that is based on a broad range of AWS services, including i2.xlarge Amazon Elastic Compute Cloud (Amazon EC2) instances for basic compute tasks; Amazon Simple Storage Service (Amazon S3) for users’ file uploads and static assets; and Elastic Load Balancing to balance workloads across Amazon EC2 instances. Slack uses Amazon Elastic Block Store (Amazon EBS) for nightly backups of MySQL instances running on Amazon EC2 i2s instances; the Amazon EBS volumes are attached to the instances and used as temporary storage before being sent to Amazon S3. Slack replaced hundreds of terabytes of Amazon EBS gp2 volumes with lower-cost Amazon EBS sc1 volumes for database backups. Since the switch, the company continues to grow the data on its user base.

For security, Slack uses Amazon Virtual Private Cloud (Amazon VPC) to control security groups and firewall rules and AWS Identity and Access Management (IAM) to control user credentials and roles. The company uses Amazon CloudTrail for monitoring logs related to Amazon EC2 instances, and Amazon Route 53 for DNS management.

Along with the AWS services, Slack is using the Redis data structure server, the Apache Solr search tool, the Squid caching proxy, and a MySQL database.

The Benefits

Using AWS as its IT infrastructure has helped Slack achieve an astonishing growth rate and a multibillion-dollar valuation with a platform that supports speed of innovation and responsiveness, reliability, and security features to ensure the confidentiality of customer information.

Crowley says AWS gives fast-growing companies like Slack the ability to minimize their involvement with daily IT management. That lets them focus on pushing innovative products and services to market quickly. “We have a lot of metrics and programs that tell us about available capacity for new customer teams to join and existing customers to grow their Slack usage,” he says. “With traditional IT, it would take weeks or months to contend with hardware lead times to add more capacity. Using AWS, we can look at user metrics weekly or daily and react with new capacity in 30 seconds.”

The ease of provisioning resources in the AWS cloud allows Slack to practice disaster recovery scenarios, which is essential for assuring existing and prospective customers that their information will always be there, when and where they need it. “One of the real strengths of AWS is that we can do a lot of re-provisioning of our infrastructure, making sure that we can recover quickly and competently in the event that something goes down,” Crowley says. “Having the ability to quickly grab twice as many of a certain class of instances is great. It gives us the ability to regularly practice our disaster-recovery scenarios.” Slack is also saving money with the lower-cost Amazon EBS sc1 volumes, providing the requisite speed at approximately one-fourth the cost. And, with the Amazon EBS sc1 volumes, the price is so low that Slack can leave them running continuously without having to build and operate special volume management tooling.

A large part of the appeal of Slack is that it replaces disparate communications tools with a single, unified platform. But that puts an increased burden on Slack to ensure that its customers' information is safe, and that Slack can deliver the kind of enterprise reliability and high availability to support the service-level agreements expected of robust enterprise applications.

“As a company, our business is integral to our customers’ daily lives,” Crowley says. “So in our customers’ eyes, our security controls and ability to deliver a reliable service become incredibly important, and it’s a responsibility we take incredibly seriously.”

He says AWS immediately addresses customers’ security concerns because AWS publishes service organization control (SOC) reports, which are based on third-party examinations evaluating how AWS achieves compliance controls and objectives. “The fact that we can rely on the AWS security posture to boost our own security is really important for our business. AWS does a much better job at security than we could ever do running a cage in a data center,” Crowley says. ”Hosting Slack in AWS makes our customers more confident that Slack is safe, secure, and always on.”

 

System design: Amazon Elastic Block Store | My first 30 minutes study

May 1, 2021

Here is the link. 

Amazon Elastic Block Store (EBS) is an easy to use, high-performance, block-storage service designed for use with Amazon Elastic Compute Cloud (EC2) for both throughput and transaction intensive workloads at any scale. A broad range of workloads, such as relational and non-relational databases, enterprise applications, containerized applications, big data analytics engines, file systems, and media workflows are widely deployed on Amazon EBS.

You can choose from six different volume types to balance optimal price and performance. You can achieve single-digit-millisecond latency for high-performance database workloads such as SAP HANA or gigabyte per second throughput for large, sequential workloads such as Hadoop. You can change volume types, tune performance, or increase volume size without disrupting your critical applications, so you have cost-effective storage when you need it.

Designed for mission-critical systems, EBS volumes are replicated within an Availability Zone (AZ) and can easily scale to petabytes of data. Also, you can use EBS Snapshots with automated lifecycle policies to back up your volumes in Amazon S3, while ensuring geographic protection of your data and business continuity.

Performance for any workload

EBS volumes are performant for your most demanding workloads, including mission-critical applications such as SAP, Oracle, and Microsoft products. SSD-backed options include a volume designed for high performance applications and a general-purpose volume that offers strong price/performance for most workloads. Customers who want to drive higher performance can attach their EBS volumes to Amazon EC2 R5b instances to get up to 60 Gbps bandwidth and 260K IOPS (input/output operations per second) of performance, the fastest block storage performance on EC2. For large, sequential workloads such as big data analytics engines, log processing, and data warehousing, customers can use HDD-backed volumes . Use Fast Snapshot Restore (FSR) to instantly receive full performance when creating an EBS volume from a snapshot.

Highly available and durable

Amazon EBS architecture offers reliability for mission-critical applications. EBS volumes are designed to protect against failures by replicating within the Availability Zone (AZ), offering 99.999% availability. EBS offers a high-durability volume (io2) for customers that need 99.999% durability, especially for their business-critical applications. All other EBS volumes are designed to deliver 99.8% - 99.9% durability. For simple and robust backup, use EBS Snapshots with Amazon Data Lifecycle Manager (DLM) policies to automate snapshot management. 

Cost-effective

EBS offers six different volumes at various price points and performance benchmarks, enabling you to optimize costs and invest in a precise level of storage for your application needs. Options range from highly-cost-effective, dollar-per-gigabyte volumes to high-performance volumes with high IOPS and high throughput designed for mission-critical workloads. With up to a 20% lower price point per GB than gp2, gp3 volumes provide you with high-performance SSD storage and the ability to provision more IOPS without adding more storage capacity. Additionally, EBS offers backups using EBS Snapshots that are incremental and save on storage costs by not duplicating data.

Easy to Use

Amazon EBS volumes are easy to create, use, encrypt, and protect. Elastic Volumes capability allows you to increase storage, tune performance up and down, and change volume types without any disruption to your workloads. EBS Snapshots allow you to easily take backups of your volumes for geographic protection of your data. Data Lifecycle Manager (DLM) is an easy-to-use tool for automating snapshot management without any additional overhead or cost.

Virtually unlimited scale

Amazon EBS enables you to increase storage without any disruption to your critical workloads, build applications that require as little as a single GB of storage, or scale up to petabytes of data — all in just a few clicks. Snapshots can be used to quickly restore new volumes across a region's Availability Zones, enabling rapid scale.

Secure

EBS is built to be secure for data compliance. Newly-created EBS volumes can be encrypted by default with a single setting in your account. EBS volumes support encryption of data at rest, data in transit, and all volume backups. EBS encryption is supported by all volume types, includes built-in key management infrastructure, and has zero impact on performance.

System design: Amazon click stream analysis | My first 30 minutes study

May 1, 2021

Here is the link. 

 This Quick Start builds a clickstream analytics solution on Amazon Web Services (AWS) in about 30 minutes. It integrates AWS services such as Amazon Kinesis Data Firehose, Amazon Simple Storage Service (Amazon S3), Amazon Elasticsearch Service (Amazon ES), Amazon Redshift, and Amazon QuickSight. The clickstream analytics solution provides:

• Streaming data ingestion, which can process millions of website clicks (clickstream data) a day from global websites.
• Near real-time visualizations of web usage metrics such as events per hour, visitor count, and referrers.
  
• Ability to build a recommendation engine with Amazon Redshift application programming interfaces (APIs).
• Ability to publish your website clickstream data to Amazon S3, Amazon Redshift, and Amazon ES.
• Analysis and visualizations of your clickstream data by using Kibana (an open-source tool that's included with Amazon ES) and Amazon QuickSight.

 

This Quick Start is for users who want to get started with AWS-native components for clickstream analytics on AWS. Once this foundational layer is in place, you can use it to ingest, analyze, and generate business insights from your websites’ clickstream data.

What you'll build 

Use this Quick Start to automatically set up the following environment on AWS:

• A highly available architecture that spans two Availability Zones.*
• A virtual private cloud (VPC) configured with public and private subnets according to AWS best practices, to provide you with your own virtual network on AWS.*
• In the public subnets:
  
  • Managed network address translation (NAT) gateways to allow outbound internet access for resources in the private subnets.*
    
  • A Linux bastion host in an Auto Scaling group to allow inbound Secure Shell (SSH) access to Amazon Elastic Compute Cloud (Amazon EC2) instances in public and private subnets.*
  • A publicly accessible Amazon Redshift cluster for data aggregation, analysis, transformation, and creation of new clickstream datasets.
• In the private subnets, two web server instances running Apache in an Auto Scaling group with Amazon Kinesis Agent installed.
  
• AWS Identity and Access Management (IAM) security groups (stateful firewall) at the EC2 instance level.
• An Application Load Balancer (ALB) to balance traffic between the two web servers. A separate target group is created for SSH access to the backend instances via the ALB, as an alternative to using the bastion host.
• Publicly accessible Amazon ES with Elasticsearch version 6.3 (default) for indexing and searching functionality on the clickstream data.
• Three Kinesis Data Firehose delivery streams to push clickstream data to the destinations: Amazon S3, Amazon Redshift, and Amazon ES.
• An Amazon S3 bucket for the Kinesis Data Firehose delivery stream.
• Integration with other Amazon services such as Amazon S3, Amazon Kinesis Data Firehose, Amazon ES with Kibana, and Amazon QuickSight.
• IAM roles to provide permissions to access AWS resources. Examples include permitting Amazon ES to access VPC resources, and allowing Amazon Kinesis Data Firehose to access Amazon S3, Amazon Redshift, and Amazon ES.
• Amazon Simple Notification Service (Amazon SNS) to notify you about automatic scaling operations and rollback of AWS CloudFormation stack creation.

System design: Amazon Route53 | My 30 minutes study

April 30, 2021

Here is the link.  

I like to spend 30 minutes to learn Amazon Route53. 

Amazon Route 53 Resolver DNS Firewall 

Secure your Amazon VPC against DNS-level attacks. 

Amazon Route 53 is a highly available and scalable cloud Domain Name System (DNS) web service. It is designed to give developers and businesses an extremely reliable and cost effective way to route end users to Internet applications by translating names like www.example.com into the numeric IP addresses like 192.0.2.1 that computers use to connect to each other. Amazon Route 53 is fully compliant with IPv6 as well.

Amazon Route 53 effectively connects user requests to infrastructure running in AWS – such as Amazon EC2 instances, Elastic Load Balancing load balancers, or Amazon S3 buckets – and can also be used to route users to infrastructure outside of AWS. You can use Amazon Route 53 to configure DNS health checks to route traffic to healthy endpoints or to independently monitor the health of your application and its endpoints. Amazon Route 53 Traffic Flow makes it easy for you to manage traffic globally through a variety of routing types, including Latency Based Routing, Geo DNS, Geoproximity, and Weighted Round Robin—all of which can be combined with DNS Failover in order to enable a variety of low-latency, fault-tolerant architectures. Using Amazon Route 53 Traffic Flow’s simple visual editor, you can easily manage how your end-users are routed to your application’s endpoints—whether in a single AWS region or distributed around the globe. Amazon Route 53 also offers Domain Name Registration – you can purchase and manage domain names such as example.com and Amazon Route 53 will automatically configure DNS settings for your domains.

Highly available and reliable

Amazon Route 53 is built using AWS’s highly available and reliable infrastructure. The distributed nature of our DNS servers helps ensure a consistent ability to route your end users to your application. Features such as Amazon Route 53 Traffic Flow help you improve reliability with easy configuration of failover to re-route your users to an alternate location if your primary application endpoint becomes unavailable. Amazon Route 53 is designed to provide the level of dependability required by important applications. Amazon Route 53 is backed by the Amazon Route 53 Service Level Agreement.

Flexible

Amazon Route 53 Traffic Flow routes traffic based on multiple criteria, such as endpoint health, geographic location, and latency. You can configure multiple traffic policies and decide which policies are active at any given time. You can create and edit traffic policies using the simple visual editor in the Route 53 console, AWS SDKs, or the Route 53 API. Traffic Flow’s versioning feature maintains a history of changes to your traffic policies, so you can easily roll back to a previous version using the console or API.

Simple

With self-service sign-up, Amazon Route 53 can start to answer your DNS queries within minutes. You can configure your DNS settings with the AWS Management Console or our easy-to-use API. You can also programmatically integrate the Amazon Route 53 API into your overall web application. For instance, you can use Amazon Route 53’s API to create a new DNS record whenever you create a new EC2 instance. Amazon Route 53 Traffic Flow makes it easy to set up sophisticated routing logic for your applications by using the simple visual policy editor.

Fast

Using a global anycast network of DNS servers around the world, Amazon Route 53 is designed to automatically route your users to the optimal location depending on network conditions. As a result, the service offers low query latency for your end users, as well as low update latency for your DNS record management needs. Amazon Route 53 Traffic Flow lets you further improve your customers’ experience by running your application in multiple locations around the world and using traffic policies to ensure your end users are routed to the closest healthy endpoint for your application.

Cost-effective

Amazon Route 53 passes on the benefits of AWS’s scale to you.  You pay only for the resources you use, such as the number of queries that the service answers for each of your domains, hosted zones for managing domains through the service, and optional features such as traffic policies and health 

Designed for use with other Amazon Web Services

Amazon Route 53 is designed to work well with other AWS features and offerings. You can use Amazon Route 53 to map domain names to your Amazon EC2 instances, Amazon S3 buckets, Amazon CloudFront distributions, and other AWS resources. By using the AWS Identity and Access Management (IAM) service with Amazon Route 53, you get fine grained control over who can update your DNS data. You can use Amazon Route 53 to map your zone apex (example.com versus www.example.com) to your Elastic Load Balancing instance, Amazon CloudFront distribution, AWS Elastic Beanstalk environment, API Gateway, VPC endpoint, or Amazon S3 website bucket using a feature called Alias record.

Secure

By integrating Amazon Route 53 with AWS Identity and Access Management (IAM), you can grant unique credentials and manage permissions for every user within your AWS account and specify who has access to which parts of the Amazon Route 53 service. When you enable Amazon Route 53 Resolver DNS firewall, you can configure it to inspect outbound DNS requests against a list of known malicious domains.

Scalable

Route 53 is designed to automatically scale to handle very large query volumes without any intervention from you.

Simplify the hybrid cloud

Amazon Route 53 Resolver provides recursive DNS for your Amazon VPC and on-premises networks over AWS Direct Connect or AWS Managed VPN.