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Azure Serverless Computing Cookbook

Azure Serverless Computing Cookbook

By : Praveen Kumar Sreeram
5 (1)
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Azure Serverless Computing Cookbook

Azure Serverless Computing Cookbook

5 (1)
By: Praveen Kumar Sreeram

Overview of this book

This third edition of Azure Serverless Computing Cookbook guides you through the development of a basic back-end web API that performs simple operations, helping you understand how to persist data in Azure Storage services. You'll cover the integration of Azure Functions with other cloud services, such as notifications (SendGrid and Twilio), Cognitive Services (computer vision), and Logic Apps, to build simple workflow-based applications. With the help of this book, you'll be able to leverage Visual Studio tools to develop, build, test, and deploy Azure functions quickly. It also covers a variety of tools and methods for testing the functionality of Azure functions locally in the developer's workstation and in the cloud environment. Once you're familiar with the core features, you'll explore advanced concepts such as durable functions, starting with a "hello world" example, and learn about the scalable bulk upload use case, which uses durable function patterns, function chaining, and fan-out/fan-in. By the end of this Azure book, you'll have gained the knowledge and practical experience needed to be able to create and deploy Azure applications on serverless architectures efficiently.
Table of Contents (14 chapters)
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13
Index

Integrating Azure Functions with Data Factory pipelines

In many enterprise applications, the need to work with data is definitely there, especially when there are a variety of heterogeneous data sources. In such cases, we need to identify tools that help us to extract the raw data, transform it, and then load the processed data into other persistent media to generate reports.

Azure assists organizations in carrying out the preceding scenarios by using a service called Azure Data Factory (ADF).

Azure Data Factory is another cloud-native serverless solution from Microsoft Azure. ADF can be used as an Extract, Transform, and Load (ETL) tool to process the data from various data sources, transform it, and load the processed data into a wide variety of data destinations. Before we start working with the recipe, I would recommend that you learn more about Azure Data Factory and its concepts at https://docs.microsoft.com/azure/data-factory/introduction.

When we have complex processing...

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