So now we need to talk about all the use cases of what you can do with the pipeline.
And as you can see there is a lot of different services we can use.
So there is this page called best practices in use cases and I do recommend you use it.
And they do give you some examples around how to use good pipeline.
For example keep battling with as three could commit and could deploy it or it could by playing with
third parties such as actual providers just get up and Jenkins could star could build and test code.
Yes.
Let's take beanstalk it lambda and cloud formation so there is a lot of different use cases for code
by plane.
And I do recommend you just read through them and understand how they work and what they're trying to
do at a very high level.
So if we go back to you could buy right now and edit our pipeline.
I just want to show you all the various actions we can have in there and just talk to them about at
a high level so that we can understand exactly what we could do with cut pipeline so all possible use
cases.
So the first one is manual approval.
We've seen this.
This is when we want to have some kind of review before we deploy it into production could build is
when we build or test our code.
So when we test our code we don't produce any artifacts but when we build our code we can produce artifacts
and pass them on to the next stage for example code pipeline could deflate.
Jenkins we'll see this in this entire section.
This is a way to integrate with a third party build service.
Now for the deployment options we have cloud formations so it is possible to deploy an entire cloud
formation templates using code pipeline.
And so it is quite common for people because code confirmation is infrastructure as code to store that
code into could commit to update the cloud formation templates include commit maybe validate it using
could build.
And then finally deploy it's using code pipeline and the cloud formation integration.
So it is definitely a very common pattern.
Could deploy we've seen this at great length.
So we'll be good Elastic Beanstalk if we wanted to deploy our application on an elastic band stock target
and have automated rolling upgrades for example on its service catalog if we wanted to deploy stuff
to service catalog which is in the end of the day a cloud formation template.
And we'll see service kind of log in this course later on and access skills easy yes if we wanted to
deploy a docker containers.
So yes we can do it simply or using blue green deployments and for blue green deployments we'll use
could deploy in the back in the backend or deploy to Amazon as three and we've seen this in this course
where we first for example can upload a zip archive artifact into an S3 Buckets in our account in another
region for example we could invoke an eight US the function and I'll be talking about this in greater
detail in the next lecture.
In terms of sources we can pull code from code commits easy R for docker containers as three for a zip
archives and github for code.
So we would use could commit as three and get hub when it comes to integrating code and building it.
Maybe we'll use ACR if we want to have a continuous delivery pipeline that has EPR as the source and
easy s as the deployment targets.
So that is definitely something that's possible.
By the way if you do specify multiple sources every time the pipeline is run each source will be refreshed
and the code will be will be pulled again now for testing.
We can test with code builders in this in this section we can have device farm where we test our applications
on many actual physical device and this is called database device farm.
So this is good when we want to do some kind of load testing and so on.
Jenkins also has a testing feature blaze meter and ghost UI inspector and run scope API monitoring.
These are more external services but they allow you to run different kinds of testing such as load testing
UI testing monitoring and so on.
So you have a lot of different possibilities with code pipeline obviously and this is definitely a very
handy use case.
So one thing we should notice is that there's a risk lined up and we learned that we can run anything
we want and I'll be talking about it in the very next lecture but so here we have all the possible options
for a code pipeline.
I do recommend you have a look through the use cases and I'll have the link obviously in the resources
the use cases for you to understand how to use good by planning with difference.
I tend to use cases for example with E.
Yes for continued delivery of container based applications to the cloud.
And I will see you in the next lecture.