Book Image

Azure Data Factory Cookbook - Second Edition

By : Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton
4 (1)
Book Image

Azure Data Factory Cookbook - Second Edition

4 (1)
By: Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton

Overview of this book

This new edition of the Azure Data Factory book, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.
Table of Contents (15 chapters)
13
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14
Index

Managing Deployment Processes with Azure DevOps

Azure DevOps offers a comprehensive set of development collaboration, continuous integration, and continuous delivery tools. With Azure Repos, you can collaborate on code development using free public and private Git repositories, pull requests, and code reviews. Meanwhile, Azure Pipelines enables you to implement a streamlined build, test, and development pipeline for any application.

In this chapter, we will delve into setting up CI and CD for data analytics solutions in Azure Data Factory (ADF) using Azure DevOps.

Continuous Integration (CI) is a practice where code changes are regularly integrated into a shared repository, ensuring that each change is automatically built, tested, and validated.

Continuous Deployment (CD) is the automatic deployment of changes to production or staging environments after they pass CI tests.

By implementing CI/CD in the context of data analytics, you can streamline the development process...