Due to the increasing amount of data worldwise, most organisations today are adopting cloud technology. Researchers of major cloud providers Amazon, Google and Microsoft are striving hard to achieve new heights in this technology. Most of the consumers and businesses are using the cloud because it is convenient, scalable, adaptable and secured in nature.
Amazon AWS is the oldest player in the cloud services market and is also one of the leading ones. Although these cloud computing platforms offer almost similar properties, there are certain differences which are helping them to keep hold of their positions. In this article, we have compared the leading cloud computing vendors, Amazon AWS, Microsoft Azure and Google Cloud Platform.
1| Compute Engine
Amazon AWS offers Amazon EC2 which provides secure, resizable compute capacity in the cloud. This service interface allows you to obtain and configure capacity with minimal friction. It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment.
Microsoft Azure compute provides the virtual machines and a full-fledged identity solution where you can provision Linux and Windows VMs as well as gain managed end-point protection and Active Directory support.
Meanwhile, Google Cloud Compute Engine delivers virtual machines running in Google’s innovative data centres and worldwide fibre network.
2| Machine Learning
All three providers are strong players in case of machine learning technology. Amazon AWS launched ML services such as AWS Rekognition for image recognition and Polly for text-to-speech deep learning, Amazon SageMaker for the build, train and deploy machine learning models, etc.
Microsoft Azure ML service provides to build, train and deploy ML models with ease. According to British Petroleum, with the help of Azure ML, they are able to build more finely tuned, accurate models in dramatically less time, helping them better gauge available hydrocarbon reserves.
While Google Cloud Platform provides AI and ML products such as Cloud Machine Learning Engine where a developer can easily build and run superior machine learning models in production. Also, in AI platform, GCP provides AI Hub for enterprise-grade sharing capabilities, including end-to-end AI pipelines and out-of-the-box algorithms, AI building blocks for developers to add sight, language, conversation, and structured data to their applications, etc.
Storage is one of the important features in cloud platforms. AWS Cloud Storage offers a complete range of cloud storage services to support both application and archival compliance requirements. The cloud storage products include Amazon EBS, Amazon EFS, Amazon FSx for Lustre, Amazon Glacier, etc.
Azure Storage offers a massively scalable object store for data objects, a file system service for the cloud, a messaging store for reliable messaging, and a NoSQL store. It includes data services such as Azure Blob, Files, Queues and Tables.
While Google Cloud Storage is an infrastructure-as-a-service for storing and accessing data. It is a unified object storage for enterprises.
4| Support For Hadoop Clusters
In AWS, Amazon EMR (Elastic Map Reduce) supports for creating and managing fully-configured elastic clusters of Amazon EC2 instances running Hadoop and other applications in the Hadoop ecosystem. It also includes EMRFS which is a connector that allows Hadoop to use Amazon S3 as a storage layer.
Azure provides HDInsight which can easily run popular open-source frameworks including Apache Hadoop, Spark, and Kafka.
GCP includes Cloud Dataproc, which is a managed Hadoop and Spark environment. A developer can use Cloud Dataproc to run most of the existing jobs with minimal alteration.
Amazon Elastic Beanstalk is the Platform-as-a-Service for AWS. With AWS Elastic Beanstalk, you can quickly deploy and manage applications in the AWS Cloud.
Cloud Services provides as the Platform-as-a-Service for Microsoft Azure.
Google App Engine is GCP’s platform as a service (PaaS) where Google handles most of the management of the resources.
Amazon offers “pay as you go” approach which means a user will pay only for the individual services s/he uses without any long term licensing.
Microsoft Azure is less expensive than AWS and charges on a minute basis.
GCP too charges on a minute basis and there are no up-front costs or termination fees.
Cloud Service Providers are portraying a major role in the uplifting of the organisations. Organisations are utilising their flexibility and elasticity for a number of uses cases such as connecting online and offline retail, ML in fintech for fraud detection, etc. However, choosing a cloud over the other entirely depends upon the needs of an individual or the organisation’s workload.
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box. Contact: firstname.lastname@example.org