Here you are.
And welcome back.
Now, in today's video, we will be discussing about the Amazon Kindness Service.
Now, do note that depending upon the type of data that you're dealing with in your organization, the
appropriate service that can be used will also change accordingly.
So it always has various services, like they have a skewers, you have EMR, you have kindnesses,
etc., and each one has its own importance.
When we discuss about streaming data, then Amazon kinases is one of the appropriate services that can
be used.
So first, before we go in and discuss about kinases, it is important for us to understand what exactly
this streaming data is all about.
So at a high level overview, streaming data is the continuous flow of data that is generated by various
sources.
So let's dig in example there.
Let's say that you have sensors across the city which will go ahead and measure something and it will
send it to central sources.
So these sensors will continuously send some data or continuously stream some data to the central source.
So this type of data that is continuously being sent by various sensors across multiple places.
This type of data can be referred to as a streaming data.
Or let's take a few more example.
One of the easiest example, if you would be stock market, is its own data.
So for example, if you have observed stock market, you have continuous stream of data that is flowing
and continuous buys and order that is flowing in through the stock market timings.
So this type of data is also referred to as streaming data over here.
So when we discuss about streaming data, this service in either place that is recommended to deal with
it is kinases.
Now, let's look into the challenges that organization faces when they're primarily working with streaming
data.
For example, when you are dealing with streaming data, you required a adequate system that can handle
such a big load.
So ideally, the streaming data processing requires two primary layers.
One is the storage layer, and second is the processing layer.
And when we discuss about storage layer, one of the few features that is really recommended in search
layer is strong consistency, record ordering, replayable reads.
And when we discuss about the processing layer, this processing layer is primarily responsible for
consuming the data from the storage layer and running some computation on top of that data and many
other tasks.
So these are the two important layers that are involved in streaming data.
Now, as an organization, you can either implement data, various open source tools as well as speed
tools that are available that can work with streaming data.
Now again, when you're working with open source as well as be tools, one of the primary challenges
that is involved is you have to take care of a lot of things, including hosting of applications.
If application is running slow, you have to be limited performance, high availability, clustering,
etc..
So in order to overcome all of this, you can make use of Amazon kinases.
So Amazon kinases makes it easy to collect, process and analyze real time streaming data so you can
get timely insights and react quickly on new information.
So at a very high level, all of you, whenever you have streaming data, you can easily make use of
kinases and everything from security performance, high availability, etc. is taken care of by the
kinases itself.
Now when we discuss about kinases again similar to SKU skewers, there are three entities that are involved.
One is producer, second is streams to it, and third is consumer.
So produce that isn't a thing.
But any entity that is producing the information like for example, we had discussed about sensors.
So sensors are nothing but the producer, which produces a lot of information.
And where will they send the information to?
They will send the information to some kind of streams to you can consider it to be Amazon kinases.
So the information from all of these producers will be sent to kinases and from kinases.
You can connect multiple consumers that can train some type of analytics on top of it and produce some
relevant results.
So before we conclude, let's go ahead and look into how exactly this might look like.
So currently I am in the car and this is console.
And if you see I have one data stream that is available.
So this is basically the data stream where the producers can send their data in to know once you have
their data, all of these graphs will also be filled.
But current data groups are not available primarily because this is the newest team that we have created.
Now, if you look over here, you also have Amazon kinases.
And what this agent does, it is basically a Java.
So for the application that allows you to sync the data to this specific kinesis stream.
Now when we discuss a.
All of these producers, this producer needs a way to send the streaming data to kinases.
So for those producers, there are multiple ways in which their data can be sent.
One of them is through the kinases, agent or warrior.
And the second thing is consumers, as we were discussing, consumer is on the right hand side.
So once their data is being sent to the kinases, what exactly needs to be done for that specific data?
So this is where the consumer really comes into the picture.
Now, again, one of the question that comes is, what is the role of consumers?
So let's take a look.
So if you look into this first example of financial institution, tracks the changes in stock market
in real time and adjust its portfolio accordingly.
So if you have kinases, what can be done?
The realtime analytic of a stock market can be sent to kinases and then you will have a set of consumers.
This set of consumers will run some analytics on top of this data.
And based on this analytics, the consumers will go ahead and just the portfolio of their financial
institution accordingly.
For example, let's see, a stock market is going down.
Then the set of consumers might have a logic to sell their stocks.
So this is one of the simple logics that can be implemented.
I mean, it would be much more complicated.
But we are looking into the simplistic scenario where the producers kinases and the consumers can be
integrated to form a specific use case.
So if you're looking to the consumers, there can be various consumers like kinases, data analytics,
which allows you to process the data in real time.
So it allows you to perform some analytics on top of the data that is stored on kinases.
Great.
Now, before we conclude, let me also quickly show you how easy it is to upload some data to Guinness
is doing so from this year.
I have a simple command which basically goes ahead and adds it according to the Guinness Esteem.
So if you look into the stream name, it is called as a demo steam.
And the steam that we have here is also demo steam.
So what exactly this does, it basically goes ahead and adds a data coalesce demo data in a Guinness
stream.
So if I go ahead and press Android here, you see we got the appropriate little.
So basically this data of demo data has been successfully uploaded to kinases.
So overall, sending data to kinases is extremely simple.
Once their data has arrived at Guinness is you can make use of various consumers to perform analytics
on top of that data and achieve that desirable result.
So this is the very high level overview about the Amazon Kinases service.