Easiest way to build a streaming application for beginners

[alexlzx]

If you don't know where to start building your first streaming application, check out this GitHub repository for easy deployment of a complete streaming application. Just follow the instructions on README and run a few commands in PowerShell, it will deploy all the resources required for a streaming application with an auto-generated data stream. This is an effortless way to build and explore what a streaming application can do with real-life streaming data.

What are the benefits of using this deployment method? 

1. Easy to deploy. It saves you time reading documentation, preparing input data, and setting up a Stream Analytic job in the Azure portal.  
2. Test a Stream Analytic job with an auto-generated data stream. The streaming application generates a data stream for you to test and explore different stream analytic scenarios.  
3. Set the number of events generated per minute. You can increase the input rate for generating data stream and see how a Stream Analytic job handles a large amount of data.

For example, if you want to extract GET and POST requests from a website clickstream, follow these steps to build a clickstream analyzer. (Please note that you must have an Azure subscription and install Git and Azure PowerShell module to begin with) 

1. Open PowerShell and clone this GitHub repo 

   git clone https://github.com/Azure/azure-stream-analytics.git 

2. Go to the directory 

   cd .\azure-stream-analytics\BuildApplications\ 

3. Sign in to Azure 

   Connect-AzAccount 

4. Run this command with your Azure subscription ID

   .\CreateJob.ps1 -job ClickStream-Filter -eventsPerMinute 11 -subscriptionid $subscriptionId 

Congratulations! You have deployed a clickstream analyzer in Azure. This application uses the following architecture for deployment. 

Now you can view your Stream Analytic job in the Azure portal and see how it processes the website clickstream. Select Test query in the query editor to see the output results. You can use the code examples in the comments for other stream analytic scenarios. 

For more detail about building a streaming application, visit GitHub repository.