Stream Analytics allow you to analyse your data in real-time. The really interesting feature of this engine is the capability to operate your data throw the time and create windows of time.
One really interesting windows function is the Sliding one.
Why ?Because it allow you for each event to create a windows of time with a give size before this event. It's really interesting for sensor data where the data are raw and can be duplicate among many sensors.
On this case we will be interested for a RFID sensor that catch each tag going throw a gate,even a low speed micro controller speed up to 16 Hhz which mean you got a refresh every 62 nanoseconde. So imagine a truck full of good tagged with RFID will spend 10 second to move throw the door, the information will be send 160 millions time throw the stream of data,so instead of scale up component and have a strong power on a micro-controller just for 10 seconds we can just send this data throw the cloud and have the Event-hub scale up automatically and the stream-analytic analyse them.
So to achieve that we will be looking to set a windows function of let say 2 min ( maximum time the truck may spend to go throw the gate).
We want to discard a tag that already went throw this gate in the last 2 min.
Conclusion : We can see it's easy to compute event using the Stream Analytics.In the case of the RFID Warehouse management system that scan in an out it's not cost-efficient to operate and purchase a strong server just for couple run a day,instead leverage the cloud power will be way more interesting, offer high scalability and speed,this point is valid for many other system that need Ad-hoc power.
One really interesting windows function is the Sliding one.
Why ?Because it allow you for each event to create a windows of time with a give size before this event. It's really interesting for sensor data where the data are raw and can be duplicate among many sensors.
On this case we will be interested for a RFID sensor that catch each tag going throw a gate,even a low speed micro controller speed up to 16 Hhz which mean you got a refresh every 62 nanoseconde. So imagine a truck full of good tagged with RFID will spend 10 second to move throw the door, the information will be send 160 millions time throw the stream of data,so instead of scale up component and have a strong power on a micro-controller just for 10 seconds we can just send this data throw the cloud and have the Event-hub scale up automatically and the stream-analytic analyse them.
So to achieve that we will be looking to set a windows function of let say 2 min ( maximum time the truck may spend to go throw the gate).
We want to discard a tag that already went throw this gate in the last 2 min.
The code on stream-analytics will be :
select gate_uid,card_uid,max(Cast([date] as datetime)) as max_datetime into outputHadoop
from input
group by gate_uid,card_uid,SlidingWindow(minute,2)
having count(*)=1;
So we grouping data by windows of 2 min sending to the output only event that not happend in the last 2 minute ( same tag at the same gate).
Conclusion : We can see it's easy to compute event using the Stream Analytics.In the case of the RFID Warehouse management system that scan in an out it's not cost-efficient to operate and purchase a strong server just for couple run a day,instead leverage the cloud power will be way more interesting, offer high scalability and speed,this point is valid for many other system that need Ad-hoc power.
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