Welcome back to we're starting module six. Wherever talking about pipeline deployment. This is our second to last
Let's just take a look here in less than 6.1. Just the concepts ideas we're gonna be talking about in this
We're looking at tools and activities. Look at that continuous deployment in the cloud. Some of the tools that are available.
Well, what we've been looking at Jenkins on some local tools, but kind of shifted That just kind of give some of the tools that they're out there that will take a look at infrastructure of the code. If you're not familiar with that, it's an interesting concept of instead of manually building your environment or editing the environment or doing anything production
use this that I see to actually deploy,
and and run the production or even your development environment.
And take a look again specific specifically at def SEC ops maturity model from a loss for deployment.
And then do give a little introduction to kubernetes and containers we talked about a couple of times. I just want to delve into that a little bit more
so for a learning objective discussed the tools activities that deploying
again the cloud based tools infrastructures a code critique some of the I C off offerings like puppet, danceable. Just compare them and then look at deployment maturity and again follow up with kubernetes.
So we talk about deploy. What it means is deploying the artifacts. So containers if you're doing micro services
but our VM images, if you're doing virtualized instances,
he recommends busy from the duty enterprise reference design using containers over VM and images. This that's the way the industry's moving. But if you're doing the M images, you would need the hyper visor.
And the idea is that the VM is a full OS stack or it has the full operating system, all the libraries, everything I needed. So that's why it takes a little bit longer, too.
To start up versus If you're running a container, which is has minimum libraries, it's ah, it's very well suited from micro services architecture because you have reduced consumption, reduced resource that you need, and they could spin up much quicker in seconds versus minutes compared to ah, virtual machine.
So some of the deployment activities it's triggered after a successful gate exit from delivery. So we all of our tests we set up, they all pass. It's ready to go way we have it. We have a package that that's a sui expected it. So we're gonna deployed into them operations.
The artifacts have delivered from repository into production, whether it's getting containers of the virtual machines. And then, if you do in infrastructures, a code it now executed to provisioned the environment before the applications deployed
and then after it, once it's deployed were from our security perspective to be other tests as well. But we're gonna do some verification scans to make sure what we tested in the development and what was pushed. The production is the same, and no vulnerabilities are nothing new was introduced
again, just kind of following along on a little infinity path. Here we can see where we are. We're in the deploy phase, so we're going to be doing that mentioned verification scan and infrastructure as a code.
We talked about the concepts for this module
and next word. Delve in a little bit, too, with continuous deployment in the cloud and looking at some of the options there