What is Stream Analytics
Stream analytics or Streaming analytics typically means making analytically informed decisions in milliseconds, while examining many thousands of events per second, generated from many millions of devices which can also be enriched by many other disparate sources of data.
Stream analytics is important for institutions and individuals alike. We need to know what is happening now and not miss out on anything important. An event with a particular machine at my productions plant or someone breaking and entering my home is of importance to me now and not later as this helps me in immediately initiating remedial actions based on events.
IoT is a typical use case for Stream Analytics as we have millions of things generating many million events which need to analyze on the fly and make informed choices either automatically or by human intervention.
A Streaming analytics platform has the following features:-
- Data or events are analyzed in almost real time.
- They may be routine monitoring, counting, alerting and reporting of data.
- They may also include this filtered data or enriched data to be fed into complex decision-making systems for training and predictive analytics.
- Every incoming event is distinctly processed.
- Events may be stored for future usage.
- Immediate actions are possible after processing of events, albeit simple actions like sending alerts, emails, streaming etc.
Advantages of Streaming Analytics:-
- Business value of data diminishes with age. With streaming analytics, an immediate action based on data is possible.
- Immediate threats to life, infrastructure is drastically reduced.
- Predictive maintenance to cut future losses.
Conclusion: We have options from all three major IoT platform providers AWS, Azure and Google to do stream Analytics. A detailed study on each of this platform will be published as the whitepaper, watch our resource page for this whitepaper click here.