The Ins and Outs of Azure Stream Analytics – Real-Time Event Processing

Yesterday at TechEd Europe 2014, Microsoft announced the preview of Azure Stream Analytics. This post will give you the ins and outs of this new service.

What is Azure Stream Analytics?

Azure Stream Analytics is a cost effective event processing engine that helps uncover real-time insights from devices, sensors, infrastructure, applications, and data. Deployed in the Azure cloud, Stream Analytics has elastic scale where resources are efficiently allocated and paid for as requested. Developers are given a rapid development experience where they describe their desired transformations in SQL-like syntax. Some unique aspects about Stream Analytics are:

  • Low cost: Stream Analytics is architected for multi-tenancy meaning you only pay for what you use and not for idle resources.  Unlike other solutions, small streaming jobs will be cost effective.
  • Faster developer productivity: Stream Analytics allow developers to use a SQL-like syntax that can speed up development time from thousands of lines of code down to a few lines.  The system will abstract the complexities of the parallelization, distributed computing, and error handling away from the developers.
  • Elasticity of the cloud: Stream Analytics is built as a managed service in Azure.  This means customers can spin up or down any number of resources on demand.  Customers will not have to setup costly hardware or install and maintain software.

Similar to the recent announcement Microsoft made in making Apache Storm available in Azure HDInsight, Stream Analytics is a stream processing engine that is integrated with a scalable event queuing system like Azure Event Hubs. By making both Storm and Stream Analytics available, Microsoft is giving customers options to deploy their real-time event processing engine of choice.

What can it do?

Stream Analytics will enable various scenarios including Internet of Things (IoT) such as real-time fleet management or gaining insights from devices like mobile phones and connected cars. Specific scenarios that customers are doing with real-time event processing include:

  • Real-time ingestion, processing and archiving of data: Customers will use Stream Analytics to ingest a continuous stream of data and do in-flight processing like scrubbing PII information, adding geo-tagging, and doing IP lookups before being sent to a data store.
  • Real-time Analytics: Customers will use Stream Analytics to provide real-time dashboarding where customers can see trends that happen immediately when they occur.
  • Connected devices (Internet of Things): Customers will use Stream Analytics to get real-time information from their connected devices like machines, buildings, or cars so that relevant action can be done. This can include scheduling a repair technician, pushing down software updates or to perform a specific automated action.

How do I get started?

For Microsoft customers, we are offering Azure Stream Analytics as a public preview.  To get started, customers will need to have an Azure subscription or a free trial to Azure. With this in hand, you should be able to get Azure Stream Analytics up and running in minutes. Start by reading this getting started guide.

For more information on Azure Stream Analytics: