Book Image

Azure Data Factory Cookbook

By : Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton
Book Image

Azure Data Factory Cookbook

By: Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton

Overview of this book

Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you’ll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. 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 as the main ETL and orchestration tool for your data warehouse or data platform projects.
Table of Contents (12 chapters)

Rerunning activities

When our data transfers fail for one reason or another, we frequently need to rerun affected pipelines. This ensures the appropriate data movement is performed, albeit delayed. If our design is complex, or if we are moving large volumes of data, it is useful to be able to repeat the run from the point of failure, to minimize the time lost in the failed pipeline.

In this section, we will look at two features of Data Factory that help us to troubleshoot our pipelines and rerun them with maximum efficiency. The first feature is breakpoints, which allow us to execute a pipeline up to an activity of our choice. The second feature is rerunning from the point of failure, which helps to minimize the time lost due to a failed execution.

Getting ready

Preparing your environment for this recipe is identical to the preparation required for the previous recipe in this chapter, Investigating failures – running in debug mode. We will be using the same Azure Data...