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Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565296
Length 382 pages
Edition 1st Edition
Tools
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Authors (4):
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Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
Roman Storchak Roman Storchak
Author Profile Icon Roman Storchak
Roman Storchak
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
Author Profile Icon Dmitry Foshin
Dmitry Foshin
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Getting Started with ADF 2. Chapter 2: Orchestration and Control Flow FREE CHAPTER 3. Chapter 3: Setting Up a Cloud Data Warehouse 4. Chapter 4: Working with Azure Data Lake 5. Chapter 5: Working with Big Data – HDInsight and Databricks 6. Chapter 6: Integration with MS SSIS 7. Chapter 7: Data Migration – Azure Data Factory and Other Cloud Services 8. Chapter 8: Working with Azure Services Integration 9. Chapter 9: Managing Deployment Processes with Azure DevOps 10. Chapter 10: Monitoring and Troubleshooting Data Pipelines 11. Other Books You May Enjoy

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...

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