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 lesson, 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
Prerequisites
- SQL experience (recommended)
- Microsoft Azure account recommended (sign up for free trial at https://azure.microsoft.com/free if you don’t have an account)
This Lesson Includes
- 50 minutes of high-definition video
- Many hands-on demos
Resources
The github repository for this lesson is at https://github.com/cloudacademy/azure-stream-analytics.