So now, let's talk about the insights type
of CloudWatch products.
And we have, the first one we have
is the CloudWatch container insights.
So this is as the name indicates, the way to collect,
aggregate, and summarize metrics and logs
from your containers.
And it's available for containers that you run
on Amazon ECS, on Amazon EKS,
on Kubernetes platform directly on EC2
or on Fargate, both for ECS and EKS.
So the idea is that thanks
to the CloudWatch container insights,
we can very easily extract the metrics and the logs
out of our containers into a very detailed
and granular dashboard from within CloudWatch.
The other thing you need to know
is that if you are using CloudWatch container insights
on Kubernetes, would it be for Amazon EKS
or Kubernetes running on EC2?
The way CloudWatch insights work is that it's using
a containerized version of the CloudWatch agents
to discover containers.
So that's the first thing you need to remember is this one.
The second one is Lambda insights.
So you're guessing what it would be about.
It is about monitoring and troubleshooting solution
for your serverless applications running on AWS Lambda.
So again, it's going to collect, aggregate,
and summarize system level metric,
including CPU time, memory, disk, and network,
and also information around your cold starts,
and your Lambda worker shutdowns.
And it's provided for your Lambda function
as a Lambda layer.
You don't really know this.
But the idea is that it runs next to your Lambda.
And it's going to create, again, a specified dashboard
called Lambda insights to monitor
the performance of your Lambda functions.
And this is to be used when you need
really, really detailed monitoring
into your serverless applications running on AWS Lambda.
Then, we have contributor insights.
So this one is to analyze logs and create time series
that display contributed data.
So for example, you wanna see the top end contributor
or the total number of unique contributors and their usage.
So we'll go quickly into an example
so you understand what this means.
So this helps you find, for example, top talkers
on your network and understand
who is impacting system performance.
So you can run it on any logs generated by AWS.
For example, VPC logs, or DNS logs, and so on.
And let's take an example.
You can identify bad hosts.
So we are going to look at the network logs, the VPC logs.
And we're going to find the heaviest network users
or we can find, for example, if you look at DNS,
the URLs that generate the most errors.
So let's have a look at how to identify
the heaviest network users.
So we look at the VPC flow log, which is the log
of all the network request made within your VPC,
which is then passed to CloudWatch logs,
and then analyzed by the CloudWatch contributor insights.
And out of it, we can find the top 10 IP addresses
that are generating traffic on RVPC
and understand if they are good actors or bad actors.
So anytime, you see a top 10 contributors or whatever,
this is the CloudWatch contributor insight.
So you can look through logs and find
who are the top whatever.
So you can build rules from scratch
or you can also leverage simple rules that AWS has created.
And behind the scene, again, it leverages CloudWatch logs.
And to finish, there are also built in rules
that you can use to analyze metrics
coming from other AWS services.
Finally, we have CloudWatch application insights,
which is to provide an automated dashboard
that will show potential problems
with monitored applications to help isolate ongoing issues.
So your applications can be running on Amazon EC2.
We select technologies only, for example, Java, or.net,
or Microsoft IIS web server, or specific databases.
And then, it will be linked to other AWS resources
such as EBS, RDS, ELB, ASG, Lambda, SQS, DynamoDB,
S3 buckets, maybe your ECS cluster, your EKS cluster,
SNS topics, or your API gateway.
And so in case there is an issue with your application,
automatically, CloudWatch applications insights
is going to put together an automated dashboard
that shows you the potential issue with some services.
And to do this, automated dashboard is going to use
a SageMaker machine learning service internally.
So it's really going to give you enhanced visibility
into your application health for you to reduce the time
it takes you to troubleshoot and repair your application.
So this is really cool because, well, if your application
is using other services or other resources of AWS,
automatically, if it's a problem originating
from one of them, it will surface
in the applications insights dashboard.
And all the alerts and findings will be sent
to Amazon EventBridge and SSM OpsCenter
so that you can be alerted in case there is an issue
detected with your application.
So to summarize, you only need to know these services
at a high level.
So CloudWatch container insights is for your metrics
and logs coming from ECS, EKS,
Kubernetes on EC2, and Fargate.
And in case you are using Kubernetes,
you need an agent to run.
For CloudWatch Lambda insights, this is detailed metrics
to troubleshoot your serverless applications
running on AWS Lambda.
For CloudWatch contributors insights,
this is to find the top end contributors
through your CloudWatch logs.
For CloudWatch application insights,
this is to create automatic dashboards
to troubleshoot your applications
and the related AWS services that your application is using.
So that's it for this lecture,
you should be good for the exam.
I hope you liked it.
And I will see you in the next lecture.