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Azure Authors: Elizabeth White, Liz McMillan, Yeshim Deniz, Greg Schulz, Todd Matters

Related Topics: Azure Cloud on Ulitzer, Microsoft Developer, Internet of Things Journal

Blog Post

Real-time Analysis of the 'Internet of Things' on Azure | @ThingsExpo [#IoT]

Azure Stream analytics has all the well known advantages of Azure Cloud; fully managed; real-time stream computation

Real-time Analysis of IoT on Microsoft Azure with Stream Analytics

Simply put, it is about collection of data from various devices (different types, different protocols, different complexity, etc) in real-time and aggregating and processing this incoming stream of data to provide useful information. This can enormously improve efficency and make the business stand out. Presently businesses may be implementing custom solutions for addressing such data handling and analysing scenarios.

This is going to get more streamlined, easy and cost-effective using the recently introduced Azure Stream Analytics. Azure Stream analytics has all the well known advantages of Azure Cloud; fully managed; real-time stream computation; highly resilient; and easy to implement and easy to get started. It can, not only help larger corporations who want to dissociate themselves from custom solutions but also help small businesses who cannot afford a custom solution.

All it takes are a few click to author a streaming job using a simplified SQL-like language and monitor the outcome. According to Microsoft, stream rates of kB/sec to gb/sec are accomodated. There are no custom codes to write as most of it is declarative (language).

An associated product, the Azure Event Hub to which Stream Analytics connect to feed all the collected data from varied devices. The streaming nature of data is compromised neither during this 'injesting' process nor during the computational phase of the analytics.

The advertised key capabilities are the following:
  • Ease of use -declarative query model, customer insulation from computautional complexity
  • Scalability -handle millions of events/sec
  • Reliable, repeatable and quick recovery-guarantees zero data loss
  • Low Latency -Optimized for sub-second latency with an adoptive pull-based model
  • Reference data- treated very much like the incomimg stream
The following scenarios of usage are mentioned:
  • Financial Services; Personalized stock trading and alerts
  • Real-time fraud detection
  • Identity protecction real-time
  • Web click stream analytics
  • Telemetry log analysis
  • Event archival for future reference
Here is a schematic of the stream analytic process (with blog author superposed lines in yellow).



Read more here (as this post is a condensed version):
http://azure.microsoft.com/en-us/documentation/articles/stream-analytics-introduction/

Read the original blog entry...

More Stories By Jayaram Krishnaswamy

Jayaram Krishnaswamy is a technical writer, mostly writing articles that are related to the web and databases. He is the author of SQL Server Integration Services published by Packt Publishers in the UK. His book, 'Learn SQL Server Reporting Services 2008' was also published by Packt Publishers Inc, Birmingham. 3. "Microsoft SQL Azure Enterprise Application Development" (Dec 2010) was published by Packt Publishing Inc. 4. "Microsoft Visual Studio LightSwitch Business Application Development [Paperback] "(2011) was published by Packt Publishing Inc. 5. "Learning SQL Server Reporting Services 2012 [Paperback]" (June 2013) was Published by Packt Publishing Inc. Visit his blogs at: http://hodentek.blogspot.com http://hodentekHelp.blogspot.com http://hodnetekMSSS.blogspot.com http://hodnetekMobile.blogspot.com He writes articles on several topics to many sites.