Key uses of Microsoft Azure

Brass Contributor

Microsoft Azure is a platform that provides cloud computing services like storage, computation, security, and many other solutions. Azure is designed for creating and managing apps through Microsoft’s data centers on the internet without having to install and manage hardware or server software.


Furthermore, Azure is flexible in terms of using multiple languages, and tools to create customized applications, and frameworks, and allows you to scale applications up with unlimited servers and storage.


Key uses of Microsoft Azure:


  1. Host and Build Web and Mobile Apps- Azure App Service enables to host and build web applications in the programming language without managing infrastructure. It builds apps faster with a one-of-a-kind cloud service that can be used to create enterprise-ready web and mobile apps quickly and easily and locate them on a scalable and reliable cloud infrastructure.
  2. Expansion in developer productivity- To measure developer productivity is challenging. Microsoft has created a new holistic way of measuring developer productivity which considers additional standards like developer well-being & community, investigations of low-code, no-code, work at the intersection of code and AI & ML. Hence, it helps to work better, faster, smarter, and more securely.
  3. Hybrid cloud consistency- A consistent hybrid cloud model permits users to work with both analytical data and operational. These services are provided on-premises and in the cloud for data warehousing, data analysis, and data visualization. Microsoft Azure hybrid cloud provides tools that ensure secure access to all data in a seamless and efficient way. Azure data services combine with Microsoft SQL Server to create data consistency and quality. Make insights and analytics more accessible- Migrating to a limitless analytics platform, Microsoft Azure helps organizations engage directly with their data. Using Power BI makes it easier to explore analytics and generate & share reports that lead to enterprise data warehousing and big data analytics, thus, leaving expensive and inelastic on-premises data warehouses behind.
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