Introduction to Azure Stream Analytics

Azure Stream Analytics (ASA) is Microsoft’s service for real-time data analytics. Some examples include stock trading analysis, fraud detection, embedded sensor analysis, and web clickstream analytics. Although these tasks could be performed in batch jobs once a day, they are much more valuable if they run in real time. For example, if you can detect credit card fraud immediately after it happens, then you are much more likely to prevent the credit card from being misused again.

Although you could run streaming analytics using Apache Spark or Storm on an HDInsight cluster, it’s much easier to use ASA. First, Stream Analytics manages all of the underlying resources. You only have to create a job, not manage a cluster. Second, ASA uses Stream Analytics Query Language, which is a variant of T-SQL. That means anyone who knows SQL will have a fairly easy time learning how to write jobs for Stream Analytics. That’s not the case with Spark or Storm.

In this course, you will follow hands-on examples to configure inputs, outputs, and queries in ASA jobs. This includes ingesting data from Event Hubs and writing results to Data Lake Store. You will also learn how to scale, monitor, and troubleshoot analytics jobs.

Learning Objectives

  • Create and run a Stream Analytics job
  • Use time windows to process streaming data
  • Scale a Stream Analytics job
  • Monitor and troubleshoot errors in Stream Analytics jobs

Intended Audience

  • Anyone interested in Azure’s big data analytics services


This Course Includes

  • 50 minutes of high-definition video
  • Many hands-on demos


The github repository for this course is at


Welcome to the “Introduction to Azure Stream Analytics” course. My name’s Guy Hummel and I’ll be showing you how to get started with Microsoft’s service for real-time data analytics. I’m a Research Lead at Cloud Academy and I have over 10 years of experience with cloud technologies. If you have any questions, feel free to connect with me on LinkedIn and send me a message, or send an email to


This course is intended for anyone who’s interested in Azure’s big data analytics services.


To get the most from this course, it would be helpful to have some familiarity with writing queries using SQL, although it’s not a requirement. This course is full of hands-on examples, so I recommend that you try performing these tasks yourself on your own Azure account. If you don’t already have one, then you can create a free trial account.


To save you the trouble of typing in the URLs and commands shown in this course, I’ve put them in a file in a github repository. You can find a link to the repository at the bottom of the course overview below this video.


We’ll start with an overview of how Stream Analytics is typically used. Then I’ll show you how to create and run a job.


Next, we’ll look at time windows, an important concept when dealing with streaming data.


After that, I’ll show you how to run a more complex processing job, using other Azure services for input and output.


Then we’ll look at how to monitor your Stream Analytics jobs.


Next, I’ll explain how to scale your jobs, especially if they’re parallelizable.


Finally, we’ll go over the most common problems when running jobs and how to troubleshoot them.


By the end of this course, you should be able to create and run a Stream Analytics job; use time windows to process streaming data; scale a Stream Analytics job; and monitor and troubleshoot errors in Stream Analytics jobs.


Now, if you’re ready to learn how to get the most out of Azure Stream Analytics, then let’s get started.

About the Author
Learning Paths

Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).