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Monitoring Azure Data Factory Pipeline Performance

In this hands-on lab scenario, you are a data engineer for Awesome Company. Recently the company has been implementing a number of Azure Data Factory pipelines to move and transform data for a variety of purposes. As their usage grows it's your duty to monitor for failed jobs and unexpected resource consumption. Performing the actions of this hands-on lab will help you become familiar with monitoring an Azure Data Factory.

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Path Info

Clock icon Intermediate
Clock icon 45m
Clock icon Jun 04, 2021

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Table of Contents

  1. Challenge

    Create a Pipeline Trigger

    1. Set a trigger on the ProductArchivePipeline according to your timezone. Have it run at an interval, such as every three minutes.
  2. Challenge

    Stream Logs to a Storage Account

    1. Create a storage account, then configure the Azure Data Factory to store its platform logs and metrics there.
  3. Challenge

    Create Pipeline Alerts

    1. Create two alerts, one for successful pipeline runs and one for failed ones.
    2. After confirming successful runs, create an interruption in the pipeline that will cause it to fail. Then verify the alerts are working properly.
  4. Challenge

    View Real-Time Performance Data

    1. Use the Azure portal to view real-time CPU and Memory performance.

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