Introduction to Microsoft Azure

Most of the companies are building Sass products and want to have their infrastructure on the cloud. There are various cloud platforms evolving each and every day. So Microsoft marked their footprint on the cloud with the help of Azure.

Azure is an cloud computing service developed by Microsoft for building, testing, deploying, managing apps and services through Microsoft managed data centers. It provides three kinds of services such as,

  • Software As a Service – Saas
  • Platform as a Service – Paas
  • Infrastructure as a Service – Iaas

In this blog, we will explore about Azure and finally we will compare their competitors.

History of Azure

Azure started with the project name called Project Red Dog. But after some time, they want to have a professional name for it and they went for Windows Azure. They released their first version on February 1, 2010.

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Later in 2014, they changed their name from Windows Azure to Microsoft Azure. They provided a good platform for all the .NET Services, SQL Database etc. And each and every day it’s getting bigger and better. As of now they marked their footprints around 55 regions worldwide.

Why Microsoft Azure?

There are various reasons for choosing Azure.

End-to-End Security

The security process was developed based upon the industry leading secure process which is known as Security Development Life cycle(SDL). So based upon this life cycle, Azure provides you more secure platform to store and access your data.

It’s the most trusted platform by U.S. Government Institutions. And also your data is safe in the cloud even if natural disaster occurs. So you know Azure is always on and it’s one of the most securest platform.

Analytics and Intelligence Capabilities

Based upon the data available in Azure, you should be able to get an statistics out of it. Let’s say you store your data in Azure SQL, it helps you to uncover the key insights based upon the data available to improve your business process.

There are various services available such as Application Insights, Data Explorer, Azure cognitive Service etc., based upon on these services you should be able to analyse your data.

Cost effective

Let’s take a scenario where you need to create a server of your own for your project. And you need to hire a team and manage and support the activities that should be done on the respective server. As the customer account increases, you need to buy an another server.

This would be tiring and since we have cloud now, why should we build our own server. All the maintenance and management of the server will be taken care of azure. You just have to Build and Deploy.

Work from Anywhere

Since Azure is an cloud platform, you should be able to use it anywhere and everywhere around the world. It just needs the credentials to login and you can create your resources at ease.

Once you create it, you can manage them directly in the Azure Portal, or else if you are smarter you can go for PowerShell scripts.

Easy to Implement

You can easily integrate your business models with Azure. It’s very simple to get started off with Azure, you just need some small amount of mouse clicks and you are good to go.

Azure follows pay as you go model so that you don’t have to worry about the pricing too much.

Hybrid Capabilities

Azure has the power of providing hybrid capabilities which is very unique when compared to other cloud providers. It provides you various hybrid connections, vm’s, virtual networks etc.,

Using these hybrid capabilities you can improve your business process and also you can improve your productivity.

Idenity & Access Management(IAM)

Azure provides IAM capabilities through Azure Active Directory such that the right information will be shown to the right users. They provide lot of user access policies which will be very useful to manage your Azure Environments.

Services provided by Azure

There are various services provided by Azure,

  • Compute – Virutal Machines, Service Fabric and Web API’s
  • Networking – There are various DNS servers, VPN’s available in Azure.
  • Storage – Storage Blobs, Queue, Date Lake Store and more.
  • Web & Mobile – You can develop and deploy web or mob apps at ease.
  • Database – Support for SQL and No-SQL database.
  • AI + Cognitive Services – Developing AI apps with cognitive service can be done very easily.
  • IoT – IoT Hub and IoT Edge makes a good pair for machine learning.
  • Security – Well defined User Access Policies
  • Developer Tools – You can make use Visual Studio, Visual Studio code, Powershell etc., to manage resources in Azure.
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Azure vs AWS vs Google Cloud

Whenever you learn a technology it’s very much important to analyse their competitors such that you can make your right choice.

Compute Engine

AWS provide Amazon EC which provides secure, resizable capacity in the cloud.

Azure provides us Virtual Machines with a full fledged solution where you can create either windows or linux vm’s. And also you can have end-end protection using Azure Active Directory Authentication.

Google Cloud Compute engine allows us to run virtual machines in their goggle innovative data centers. But they are not providing good authentication mechanism such as Azure.

Machine Learning

AWS provides 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.

Azure ML service provides to build, train and deploy ML models with ease. And also it have Azure Machine Learning Studio where you can easily build and deploy your machine learning models.

Google Cloud provide Cloud Machine Learning Engine such that you can build your models and deploy it in production. It also has an concept called AI- Hub where it will be very useful for large enterprise applications.


AWS offers a complete range of cloud services such as Amazon EBS, Amazon EFS, Amazon Glacier etc.,

Azure storage offers SQL and No-SQL data stores where you can store files in Blobs, Queues, Azure SQL Hyperscale, No-SQL etc.,

Google Cloud provides infrastructure as a service for storing and accessing your data.

Support for Hadoop Clusters

AWS has Amazon EMR which is supported for creating and managing the clusters of Amazon EC2 instances running on Hadoop.

Azure has HDInsight which can run open-source frameworks such as Apache, Hadoop, Spark etc.,

Google Cloud has Cloud Dataproc, which can be used to manage Hadoop and Spark environment.


AWS has Amazon Elastic Beanstalk which provides Platform-As-A-Service. Using the service, you can deploy and manage AWS applications.

Azure has various cloud services which can act as an Platform-As-A-Service.

Google Cloud has Google App Engine which provides you the Platform-As-A-Service.


AWS uses pay-as-you-go model such that user needs to pay only for what they use.

Azure is less expensive than Azure and Google Cloud and it also follows pay-as-you-go model.

Google cloud charges you every minute for the services available.


So which is best?

It totally depends upon your requirements. Based upon the requirements provided analyse these three platforms and come up with the right choice. But most of the services are available in Azure and it’s evolving faster than Google cloud or AWS.

In our next blog we will explore about Azure Cognitive Services.

Happy Coding!

Cheers! 🙂

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