A Guide to the Developer Journey in Azure Stream Analytics

[Guest alexlzx]

As a developer, your journey with Azure Stream Analytics (ASA) can be divided into several stages, each with its own set of challenges and requirements. In this blog post, we'll walk you through the typical developer journey in ASA, from the initial setup to production deployment. Along the way, we'll explore the various development tools and best practices that will help you build a Stream Analytics job.

 

 

 

Stage 1: Configure Input and Output Sources

 

 

To define input and output sources for your Stream Analytics job, you can choose from various input sources such as Event Hubs, IoT Hub, and Blob storage, and output sources such as Blob Storage, Event Hubs, Power BI, etc. To verify if the sources are configured successfully, select Input Preview in the query editor to check if there’s any incoming data.

 

 

You can also configure input and output sources using the ASA extension for Visual Studio Code. For more information, refer to this guide.

 

 

 

Stage 2: Develop Query

 

 

Once you have defined your input and output sources, you can begin writing your streaming query using SQL-like language. ASA supports a broad range of built-in functions that allows you to process your data streams in real time. To ensure that your query is producing the expected results, select Test query.

 

 

To learn more about ASA build-in functions, see Built-in Functions (Azure Stream Analytics) | Microsoft Learn

 

 

 

Stage 3: Test Query Locally

 

 

To avoid errors in your Stream Analytics job, it is important to test your query logic beforehand. You can achieve this with the ASA extension for Visual Studio Code (VSCode), which allows you to test your query on your local machine and visualize your job's running topology. This way, you can identify possible performance issues and optimize your query accordingly.

 

• Select Open in VS Code in the query editor. Make sure you’ve installed the Azure Stream Analytics extension in your VSCode before opening the job in VS Code.
  

 

 

• Then open the query script in VS Code and select Simulate job to visualize job topology. Check out this guide to learn more about optimizing query performance.
  

 

 

 

 

Stage 4: Submit and Start Job

 

 

After you’ve finished editing your query, you can submit your Stream Analytics job to Azure and easily start it using either the Azure portal or the ASA extension for VSCode.

 

• Submit to Azure
  

 

 

• Start job in VSCode
  

 

 

• Start job in the Azure portal
  

 

 

 

 

Stage 5: Monitor Job

 

 

After starting your Stream Analytics job successfully, you can analyze its topology and monitor its running status using the Job diagram in the Azure portal.

 

• Select the Job diagram in the menu and identify any potential bottlenecks or performance problems within the streaming nodes.
  

 

 

 

Conclusion

 

 

By following the above stages, you can successfully set up and deploy your streaming queries in production, extract insights from your data, and enhance your applications. As you grow more familiar with the capabilities of Azure Stream Analytics, you can begin to explore more advanced features and techniques to create even more robust and efficient streaming applications.

 

Continue reading...