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- [Alana] In the beginning of the week,

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I referenced a situation
where a team of engineers

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wanted to practice continuous delivery,

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on top of the continuous
integration system

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they already had in place.

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At this point, we've spent
most of the week learning

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how to get code deployed.

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However, we've still been

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performing these deployments manually,

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which doesn't really solve the
scenario I presented earlier.

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The goal of that team of engineers

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was to automate their build, test,

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and then deploy that code
to a staging environment

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before it gets released, all automated.

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So, let's figure out how to
do that with the information

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and services we know, so far.

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So, here I am in the
AWS Management Console,

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and I've already set up a sample pipeline

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that looks like what the
team already has in place.

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For this, I use AWS CodePipeline to model

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and configure the different stages

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of the team software-release process.

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Since they're already practicing
continuous integration,

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it's likely that the
team had a source phase

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and a test phase.

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As you can see, I've already set up

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the source stage to use AWS CodeCommit,

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and a test stage to run
unit tests on their site.

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Now, we just need to
configure a deploy stage

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to automate the deployment all
the way from the repository

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to their staging environment.

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Okay.

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So, to make this quick,

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I've already created an application

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and a deployment group for CodeDeploy.

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So, I'll go ahead and edit the pipeline.

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And I'm going to add in a deployment.

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I'll click Add stage in the pipeline,

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after the test stage, and call it Staging,

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and then click Add stage again.

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From here, I'll go ahead
and click Add action group.

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And this is where I'll
specify what kind of action

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I want to take here, and which service

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I'm going to use to do it.

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I'll give it a name, call it something,

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like DeployToStaging.

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I'll select the service I want to use,

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which, in this case, will be CodeDeploy.

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And then, I'll then specify
the input artifact that flows

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through the pipeline.

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If you remember, in an earlier video,

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where we walked you through CodeDeploy,

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we use the service by itself.

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When we used it as a standalone service,

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we had to specify the
location of the artifact

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and an S3 bucket that we created.

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When you create a pipeline
with CodePipeline,

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it creates an artifact bucket by default,

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and uses this bucket to store
input and output artifacts,

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and also transfers them from
stage to stage on your behalf.

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In fact, we can go into the S3
console and see this bucket.

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I'll go ahead and search for this bucket,

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with CodePipeline in the title.

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And then, I'll choose the
one for the Region I'm in.

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In this case, it'll be us-west-2,

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and I'll click on it.

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I can select which pipeline I want, here.

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And, in this case,

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I'm using the CDpipeline in this demo.

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And then, I can view the files in it.

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As you can see here, there is a folder

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that has our source artifacts,

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which CodePipeline will
transfer from stage to stage.

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All right.

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So, let's go back to the
CodePipeline console,

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where we're back to creating
a DeployToStaging action.

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If we use CodeDeploy with
CodePipeline, as we're doing here,

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we'll simply specify the
artifact that's in the bucket,

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and it takes care of the rest.

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I'll select the details
about the application name,

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and deployment group.

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And then, I'll click Done.

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All right.

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From here, I'll click
Save, and then Save again.

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Okay.

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And then we'll kick off
the pipeline once more.

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So, I'll go into my terminal, here.

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I'll find my application,

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which is index.html, here.

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I'll make a change to the file,

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such as changing the font-size.

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(typing)

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Save the file.

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(typing)

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And then, I will git add the file,

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(typing)

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git commit with a message, such
as, "changed the font size,"

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(typing)

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and git push.

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Okay.

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So, once we've updated the file,

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we can go back to our pipeline, here.

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And I will give it a bit to run.

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All right.

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So, it looks like
everything's succeeded here,

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and we've automated much
of the process already.

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So, everything is good to go.

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Now, if we wanted the pipeline
that released to production,

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we could simply add another
stage here, to DeployToProd.

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I've already created a
pipeline that adds on to this,

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that does just that.

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So, I'll go ahead and find that pipeline,

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click on it,

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and we can view the entire thing.

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Here is my source, test,

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the DeployToStaging, and
then the DeployToProd.

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But, notice in the middle
of these two deploy stages,

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I have a manual approval action.

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This is the point where I
want the pipeline to stop

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so that someone can manually
approve or reject this action.

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If everything looks
good, it can be released.

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Otherwise, we'll need
to troubleshoot and fix.

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This entire pipeline here is
continuous delivery in action.

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It automates the deployment
to the staging environments,

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and includes a manual
step here to DeployToProd.

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So, with this pipeline in place,

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the team is now practicing
continuous delivery.