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Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build ETL, Hybrid ETL, and ELT pipelines using ADF, Synapse Analytics, Fabric and Databricks

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781803246598
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Tonya Chernyshova Tonya Chernyshova
Author Profile Icon Tonya Chernyshova
Tonya Chernyshova
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
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Dmitry Foshin
Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
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Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started with ADF 2. Orchestration and Control Flow FREE CHAPTER 3. Setting Up Synapse Analytics 4. Working with Data Lake and Spark Pools 5. Working with Big Data and Databricks 6. Data Migration – Azure Data Factory and Other Cloud Services 7. Extending Azure Data Factory with Logic Apps and Azure Functions 8. Microsoft Fabric and Power BI, Azure ML, and Cognitive Services 9. Managing Deployment Processes with Azure DevOps 10. Monitoring and Troubleshooting Data Pipelines 11. Working with Azure Data Explorer 12. The Best Practices of Working with ADF 13. Other Books You May Enjoy
14. Index

Rerunning activities

When our data transfers fail for one reason or another, we frequently need to rerun affected pipelines. This ensures that appropriate data movement is performed, albeit delayed. If our design is complex, or if the pipeline is 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 Azure 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 pipelines in debug mode. We will...

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