How do you.
And welcome back.
Now, in today's beginning, we will be discussing about Amazon at dinner.
So at the high limit overview you Amazon Athena is a service that allows us to analyze various log files
from a data source using standard sequence.
For example, let's say you have data or large amount of data in a stream.
What could I do on to analyze?
So what you can do, use it can basically write a single query and send it to a Tina.
Tina B Depending upon the sequel query, it will go ahead and analyze the database book and it will
give you the result.
So let us quickly understand some of the use cases where Tina can be useful.
So let's say that you have a big chunk of cloud drill log files that are available within your S3 bucket
and you want to quickly see on who has logged in to the cloud within the past ten days.
So in order to quickly analyze this specific use case or maybe you want to go ahead and analyze some
different use case within the cloud trail logs.
So generally for analysis, what you need, you need some kind of a solution like Splunk or the e-mail
key.
So maybe let's say you want to perform analyses.
So what you do, you basically will create the easy to extend on top of that, easy to instance, you
will deploy Splunk or some other log monitoring solution, and then you'll go ahead and add a data source
or import the cloud train logs to Splunk or Realtek.
And then you go ahead and begin analyzing your data in cloud trail.
So this is one.
The second way is let the data be in a book.
You go ahead and send the query to a pin on what exactly you want to analyze.
And depending upon your sequel query, Athena will go ahead and give you the results.
So this is the basics of it.
So let's go ahead and have a quick demo.
So currently within a stream bucket data, some data.
And so this is basically a cloud front related log.
So if you'll see you have a specific timestamp over here, you have the request that was made, you
have the USA and on the right hand side you also have various other information related to the operating
system, etc. and that is a big chunk of log feed that is currently available.
So all of this data is part of the test S3 bucket.
So what we want to we want to go ahead and analyze this data.
So for today's demo, I already have the connections that is configured.
So what we'll go ahead and do will go ahead and query data.
So even in the Athena console, you will see the diagram is very straight forward.
So you point to a data source to Athena start query and you can go ahead and analyze the results.
Great.
So let's go ahead and open up the query data here.
So currently you see we have a database and we also have a table called SCAF Logs which basically contains
other details on what might be expected within the log file like big time location methods, etc..
So what we need, we need to add a sequel query.
So I have a sample sequel query which goes ahead and analyzes the logs between this specific date from
this cyflogwr here.
So let's go ahead and click on right.
So it is currently running.
And if you'll see earlier, it basically gives us the result.
So basically what we're looking for, if you look into this set of log feeds, basically it contains
this operating system, details like Android, Windows Airways, etc..
So we're basically analyzing on how many counts of operating system are present in the set of log files.
That is part of the S3 bucket.
So this is how it really looks like.
Now, let's say that you want to also analyze the browser related information, which are the browsers
that users are logging in from.
So within you, if you're looking to the table, we also have one for browsers.
So let me go ahead and replace the OS by browser.
Let's quickly during.
It is analyzing the data.
The analysis is completed and if you will see you have Firefox browser count 794, opera is 85 and so
on.
So if you see whenever you have a detailed history book, a true sequel query, it becomes very simple
to go ahead and quickly identify your use case.
You no longer have to build the entire monitoring system like Splunk, LP, etc. to be able to query
the data that is part of a history book.
Many times the amount of data is pretty huge.
And with Athena, when you pointed to the S3 bucket, and if you write a simple query, it becomes very
easy to go ahead and analyze and get the appropriate result in a quick to win.
So this is a very high level overview about Amazon, Athena.