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Serverless computing in Azure with .NET

Serverless computing in Azure with .NET

By : Rosenbaum
4.3 (3)
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Serverless computing in Azure with .NET

Serverless computing in Azure with .NET

4.3 (3)
By: Rosenbaum

Overview of this book

Serverless architecture allows you to build and run applications and services without having to manage the infrastructure. Many companies have started adopting serverless architecture for their applications to save cost and improve scalability. This book will be your companion in designing Serverless architecture for your applications using the .NET runtime, with Microsoft Azure as the cloud service provider. You will begin by understanding the concepts of Serverless architecture, its advantages and disadvantages. You will then set up the Azure environment and build a basic application using a sample text sentiment evaluation function. From here, you will be shown how to run services in a Serverless environment. We will cover the integration with other Azure and 3rd party services such as Azure Service Bus, as well as configuring dependencies on NuGet libraries, among other topics. After this, you will learn about debugging and testing your Azure functions, and then automating deployment from source control. Securing your application and monitoring its health will follow from there, and then in the final part of the book, you will learn how to Design for High Availability, Disaster Recovery and Scale, as well as how to take advantage of the cloud pay-as-you-go model to design cost-effective services. We will finish off with explaining how azure functions scale up against AWS Lambda, Azure Web Jobs, and Azure Batch compare to other types of compute-on-demand services. Whether you’ve been working with Azure for a while, or you’re just getting started, by the end of the book you will have all the information you need to set up and deploy applications to the Azure Serverless Computing environment.
Table of Contents (16 chapters)
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Azure Batch

Azure Batch is a compute on-demand service best suited for running intrinsically parallel, high-performance computing (HPC) workloads.

Intrinsically parallel workloads are the workloads that can easily split into multiple tasks performed simultaneously on many servers. Common examples include financial risk modelling, image processing, or genetic sequence analysis.

These workloads typically require very long processing times or large amounts of compute power. Hence, they can benefit tremendously from HPC, where parallel processing by multiple servers is used to run the application more efficiently and quickly.

With Azure Batch, you define the compute resources you need to execute your compute workloads without the need to provision, configure, and manage the infrastructure and networking. With Batch, you need to specify the OS, the node (instance) size, the target...

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