You know patch a processing thing right where you gather all the stats then it goes to another system where a cron job runs every five minutes which you set the vitals and then raises the alert maybe the patient would meet with a fatality meanwhile right so in this case like an ICU case where there is you know as soon as you detect that the white ulcer you know the white you know metric so the ball of the of the patient are going here immediately so that's one the next is fraud detection in today's world.

Today we are all doing online transactions you know we enter our credit card details in multiple that sides right so what is a fraudulent website somehow you know takes a great cut information from you and starts doing transactions on your credit card on your behalf it's basically spending your money to buy things for themselves so in finance of cyber security it is very important to you know to take these frauds real-time or near real you know having all this data gathered overnight and external is the fraud has happened the money spent and now you have to reconcile somehow you have to go for your insurance weekend so essentially in the cyber security space or in the finance phase fraud detection is a use case which demands real-time you know analytics of financial transactions.

So to say the next is you know you have an application that you have deployed on the cloud let us say your bait website that you've deployed on the cloud how do you know whether it's working right now right how you know in the last five minutes or last two minutes how many other pages did itself how many times you fail so these are again things you know if you do know if you don't act immediately on you know your your applications health then it might cause widespread impact on your business.

So again application health monitoring which monitors how many errors you are serving to your end-users how many you know page load errors 500 errors you know all of that needs to be real-time the next is nowadays know it's all about every business is moving towards data-driven decision making and one of the aspects in that is you know let us say you do not rule out roll out a new feature on your website and then you know people start talking about that feature in your product on social media you want to know quickly you want to mass soon as the feature rose up what is the user sentiment based on social media what are they meeting about what are they you know talking about on Facebook so again there if you if you do the traditional batch analytics thing then you know you will come to know maybe a day or two later and by that time you could have impacted maybe 90% of your users they might have loved it which is all good but if they disliked it you could not prevent it because your system was not geared to analyze data as soon as it was produced the next days you know Google Maps you know.

I want to go from here to you know I want to go go to some place Google map you know use the Google Maps uses live traffic management right so it is looking at traffic data as it is produced and based on that suggesting which routes are tests in terms of time you know which routes a less congested so that's another use case where Google in this case Google Maps in this case has to act on the traffic information real time and then you know suggest better routes the next is high frequency stock trading so let us say there is a system in which you know whose responsibility is to in automatically using an algorithm invest money into stocks buy stocks and sell stocks so that at the end of the day it has made a margin it has made a profit for its users such a system you know has to act quickly because stocks raise stock prices you know raise and fall within seconds.

If the system is like that you know only at historical data and could only act tomorrow today the company might be doing very well but tomorrow by the time it analyzes that date and decides to buy that stalk it might be too late right and the stock would just be falling the next day the last hand you know something that we are all going to experience in the next 10 15 years is self-driving cars so self-driving car is not a real-time data analytics person's like real-time data processing self-driving car has to look at you know whether it has to accelerate brake slightly turn left turn completely turn left turn right all of that right so it has to be able to act on data as soon as it gets it or it catches it so these are few the use cases that I wanted to impress on you guys where real-time data analytics is now not only you know a good thing to know it's now an essential thing to have in your analytics toolkit okay.

So moving on what I want to talk next about this we are talking about all this real-time data getting produce type data getting produced and you having the ability to process it real-time but how can you model this data in your minds so that you can understand how to do analytics with it this is what this slide is going to cover this what we are trying to discuss here so one approach you know one way to look at data as it is getting generated is essentially as a stream of events right or a stream of data you know so data real-time data or data getting produced at different points in time can be looked at as a stream of events it's like a stream a river flowing right in front of you standing here and there's a river flowing right across you right and what the river essentially has as it has events where each event in itself knows the timestamp of when that event occurred and it actually and it also has the actual data inside it.

So the model we are thinking office we are standing and there's a river flowing right a crosses and it's a river of events and those events essentially have in them the time when they occurred and the actual date of the event so let us keep this modeling of data in our head so that we can see how we can tie this with analytics or if we model the data this way how analytics becomes right easier to do in real life okay all right so now moving on what I want to do next talk about this what is the types of real-time data analytics that gets done generally what are the different ways in which real-time data gets analyzed so one common use case is for you to aggregate stats from these send events let us say in the case of the example that we discussed in the case of a website that you have to put on the cloud I being the architect of the system care about.

How many page load errors did it serve in the last five seconds right this is very important for me because only this can tell me whether my system is right now doing good or not right the the other use case where aggregated statistics from recent events you know can be you know is needed is let us say you work in an e-commerce company or in any company and you know it sells different types of products and you see you as you okay tell me how much revenue each category of products that we sell like apparel like you know Footwear like you know maybe boots like you know whatever else how much revenue each of these categories is generating every minute and I want it as latest as the last minute.

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