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Integrating serverless architecture : using Azure Functions, Cosmos DB, and SignalR Service
Vemula R., Apress, New York, NY, 2019. 456 pp. Type: Book (978-1-484244-88-3)
Date Reviewed: Aug 2 2021

Integrating serverless architecture covers how to build the Twitter bot application from scratch via a step-by-step method, including the basic installation software needed to achieve fundamental serverless computing. As the term “serverless computing” suggests, anyone might wonder how computing is achieved without a server. This is briefly discussed in the first chapter as “the modernization of core software engineering practices like agile development, customer-focused testing,” and continuous integration/validation as a basic cloud called “serverless computing.” Vemula explains: “The term serverless doesn’t mean zero servers; it means full abstraction with server management,” as “cloud computing provides reliable, scalable, secure, cost-effective, [and] sophisticated services to help businesses quickly adapt, upgrade, and modernize [the current application programming interface (API)].” The book’s main goal is to present “the serverless architecture by leveraging Azure functions” and other technologies such as Cosmos DB and SignalR Service.

The chapters (except chapter 1) discuss Azure functions, Cosmos DB, and SignalR Service from Azure. In chapter 2, the author presents basic Azure functions, how to create a Twitter bot account, setting up a Twitter bot framework, and how to retrieve popular tweets based on some keywords by using the Tweetinvi library. Finally, after setting up the basics needed, the book covers deploying the Twitter bot API on the cloud. Chapter 3 then explores the data mechanisms needed by connecting tweet, hashtag, and user collections to Cosmos DB, which is a “globally distributed multi-model database that supports ... documents, graphs, key value, and column store,” as well as different APIs such as SQL, MongoDB, Table, Gremlin, and Cassandra, in order to perform data operations among various data models. Having designed the Twitter bot using Cosmos DB, which very much consists of distributed data, it is important to make it more reliable and efficient. This aspect is achieved “by bringing in more servers (horizontal scaling) or by adding more [central processing units (CPUs)/random-access memory (RAM)] power to existing servers.” This is discussed in detail, along with disaster management by decoupling the systems from essentials and nonessentials by tweet scan logic. This is achieved via the Tweet Scheduler Function, Tweet Bot Function, and Tweet Notification Function; setting up these functions is discussed in chapter 4.

Chapter 5 deals with the security issues of software applications, for example, (i) securing applications “such as managing user identities through authentication and authorization protocols”; and (ii) “enhancing server security” by securing configuration at a centralized Azure key vault.

Chapter 6 discusses integrating the Twitter bot application with the open-source ASP.NET web application in order to configure and manage a Twitter bot web app and have a “callback [uniform resource locator, URL] to support sign-in to the Azure environment.” In light of current business productivity and responsiveness, critical decisions must be achieved in real time, that is, by integrating the Azure SignalR Service library.

Finally, the book concludes with the design and implementation of “continuous integration and continuous delivery pipeline for the Twitter bot application [through] Azure DevOps.” Thus, the content of the book clearly explains how to build a customer-centric Twitter bot application and develop and improve the content of the business, from a software application perspective, as a serverless architecture.

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Reviewer:  J. Arul Review #: CR147323 (2112-0284)
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