Here, we will study, How some Top Global Companies are using AWS for their Business Expansion and Operation:
Netflix Uses Close to 1,000 Amazon Kinesis Shards in Parallel to Process Billions of Traffic Flows.
Netflix uses AWS for nearly all its computing and storage needs, including databases, analytics, recommendation engines, video transcoding, and more — hundreds of functions that in total use more than 100,000 server instances on AWS.
Netflix is the online platform for video streaming with low latency and delay. In 2009, they moved to AWS cloud to incorporate the content delivery throughout the globe. They preferred AWS because they wanted to be more focused on updating, saving and managing instances over the cloud. Netflix is using dozens of EC2 instances running across 3 AWS regions. There are hundreds of micro services running and serving 1 billion hours of content serving per month. They use Amazon S3 for chopping the video content into 5 seconds parts, package it, and then deploy to the content delivery networks.
Netflix is one of the world’s largest online media streaming providers, delivering almost 7 billion hours of videos to nearly 50 million customers in 60 countries per quarter. The company is planning to use AWS Lambda to build rule-based, self-managing infrastructure and replace inefficient processes to reduce the rate of errors and save valuable time
Slack provides a messaging platform that integrates with and unifies a wide range of communications services such as Twitter, Dropbox, Google Docs, Jira, GitHub, MailChimp, Trello, and Stripe. Privately-held Slack is on Fortune Magazine’s “Unicorn List” of startup firms worth $1 billion or more, with a $2.8 billion valuation supported by a five percent weekly user growth rate and major brand-name customers including Adobe, Samsung, Intuit, NASA, Dow Jones, eBay, and Expedia.
Slack has very simple IT architecture that is based on AWS services. They are using Amazon Elastic Compute Cloud (EC2), Amazon Simple Storage Service (S3) for file uploads and sharing static assets, Elastic Load Balancer to balance the load across servers.
To protect the network on cloud and firewall rules with security groups, they are using Amazon Virtual Private Cloud (VPC). For the protection of user credentials and accounts, they are utilizing Amazon Identity and Access Management (IAM) to control user credentials. Along with these services, they are using Redis data structure server, Apache Solr search tool, the Squid caching proxy, and a MySQL database.
Benefits of AWS
- Reviews user metrics daily
- Reacts to usage rates in a matter of seconds by provisioning additional capacity
- Easily practices disaster-recovery scenarios
Coursera is the world’s largest provider of massive open online classes (MOOC), with more than 150 university partners from 29 countries and more than 25 million registered students. Since its launch in 2012, the company’s educational offering has expanded from a handful of courses to some 2,000 offerings in 160 specializations, including business, computer science, and the humanities.
To host its website and support its rapidly expanding business, Coursera relies heavily on Amazon Web Services (AWS). Until recently, the company focused on setting up its backend services and AWS infrastructure. Now, it needed to streamline its front-end processes as well.
They used Amazon Elastic Container Services (ECS) to move easily to micro services-based architecture. They are using Amazon EMR, Redshift, Cassandra, Amazon RDS and many third party tool to set up their infrastructure on the cloud. Using ECS, each job is produced as a container and ESC schedule it across the instance cluster of EC2. Amazon ECS also helps them handling all the cluster management. Using AWS Cloud, they achieved the usability of resources, speed and agility in their processes , scalable capacity and operational efficiencies.
Benefits of AWS
- Reduced build times by 83%
- Runs 300–500 builds a day
- Runs multiple jobs concurrently
- Able to scale continuously for build processing
- Creates build environments
Vodafone Foundation Australia
One of 27 Vodafone Foundations worldwide, Vodafone Foundation Australia has donated more than AU$23 million (US$17 million) in charitable contributions, and has partnered with the Garvan Institute of Medical Research, a leading medical research organization that focuses on cancer and other diseases.
The application runs in an AWS architecture comprised of Amazon Cognito, which is used to save DreamLab application user data and manage identification in the AWS cloud. Amazon Simple Queue Service (Amazon SQS) is used to queue and transmit data sets of publically available DNA information between various AWS services and the application, Amazon CloudWatch provides monitoring and alerts if any of the cloud resources or applications are experiencing sub-optimal performance.
Consumers are also seeing constant improvements to the application as the development team uses feedback and the agility of the AWS architecture to fine tune its functionality. Burnet describes working with AWS as “fantastic” and indicates that the potential for DreamLab means its use of AWS is likely to expand in the future.
At the center is San Francisco-based Coinbase, a digital wallet and exchange platform where over 20 million merchants and consumers have traded more than $150 billion in cryptocurrencies since its founding in 2012.
Coinbase needs to provide a seamless experience for consumers while taking steps to secure the environment in which they operate. For this, the company relies on artificial intelligence (AI) using machine learning tools from Amazon Web Services (AWS).
Using Amazon SageMaker, a tool to easily build, train and deploy machine learning models, engineers at Coinbase developed a machine learning-driven system that recognizes mismatches and anomalies in sources of user identification, allowing them to quickly take action against potential sources of fraud.
Restricted access to data in a highly secure environment makes doing machine learning that much harder. Coinbase overcomes this challenge by allowing machine learning engineers access to data logs only via code that’s been thoroughly reviewed and committed into Amazon Elastic Container Registry — machine learning engineers can’t actually log into the production servers and run code that hasn’t been reviewed.
… and the list keeps going on, Humanly, It’s not possible to mention and describe every company which works on AWS in the world, Here is the link of their official website of case-studies by AWS.