Azure Batch - Demo
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This course will show practical applications of key Azure features to meet the programming and configuration challenges introduced by long-running tasks.

We'll start with Azure Batches and how you can use them to create large-scale, parallel, and high-performance apps in the Azure cloud. Then we'll go over Azure Queues and how they can add resiliency to your web applications. Next, you'll look at Webhooks and how they can address events in your cloud apps. Finally, we'll show you WebJobs and how they can deal with continuous processing tasks.

By the end of this course, you should be able to understand and apply these four Azure features to solve some of the challenges you face with long-running tasks, especially in high-performance computing applications.

Learning Objectives

  • Create large-scale, parallel, and high-performance apps by using Azure Batches
  • Build resilient apps by using Azure Queues
  • Implement code to address application events by using Azure Webhooks
  • Address continuous processing tasks by using Azure WebJobs

Intended Audience

  • People pursuing the Microsoft AZ-203 certification
  • IT professionals, web developers, DevOps administrators


  • Basic understanding of cloud concepts
  • Familiarity with web programming
  • Exposure to Azure configuration (Portal, CLI, or PowerShell)

We’d love to get your feedback on this course, so please give it a rating when you’re finished.


In this demonstration we will implement some compute workloads targeted at media processing in Azure, using Azure Batch and Azure Storage. I have created a batch account and storage account ahead of time. You can download Batch Explorer from a link in the portal. I'm using some stock audio files as the data input. The example uses FFmpeg to convert audio files between formats, usually to save space. 

Let's look at the properties of the Azure Batch job. You can see the command line task preparation which will run on the job nodes. You can limit the job resources using constraints, like the maximum retry count and time. 

Here is the Azure template for this job. Now, let's go the Batch Explorer and put in some settings for the pool and the job. We also need to select the location for the input and output files. We'll submit the job, and use Batch Explorer to look at the progress. You can keep an eye on the job at the top right of the screen. You can monitor metrics, like core minutes, and node states. Later on, I'll show you the resource consumption from the Azure portal. 

Now that the job is complete, we can look at the output files which have been converted from .wav to .mp3 format. Back at the Azure portal, I will show you the constraints and the resource consumption we saw earlier. And finally, here is an overview of the successful job. And that's it for our demonstration of Azure Batch.

About the Author

Derrick is a content contributor and trainer for Microsoft cloud technologies like Azure, Office 365 and Dynamics 365. He works across North America and Europe to help companies and organizations with these technology shifts. Before that he has worn many hats but prefers to wear them one at a time.

When he is not night walking during his travels, you can find him on a bicycle path or performing guitar solos to an imaginary audience in his basement.