Book Image

Serverless computing in Azure with .NET

Book Image

Serverless computing in Azure with .NET

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 (23 chapters)
Title Page
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we have implemented the integration of Azure Functions with the Microsoft Cognitive Services text sentiment analytics API. This integration allowed us to leverage the depth and breadth of Microsoft knowledge based on historical data, Machine Learning, and artificial intelligence to analyze the sentiment of input text.

We also expanded on the approach of using shared code across multiple Azure Functions to encapsulate functionality that is needed across the board, and we integrated the new results from a Twitter feed and document analysis with our Web UI dashboard.

During the previous four chapters, we walked through how to design and build a full text sentiment analytics application for analyzing text documents and tweets based on Azure serverless compute (Azure Functions), with a SQL Azure database as a data store and .NET Core Web application as a GUI.

As reflected in the architecture diagram in the beginning of this chapter, the application is comprised of multiple...