Building Automated Machine Images with HashiCorp Packer

This lecture will briefly introduce the Introduction to HashiCorp Packer section.

Why is Packer so popular and continues to gain momentum in the market right now? We'll discuss this and much more in this lecture as we define the problem that Packer solves for us.

What is Packer and what can it do for me? Learn where Packer fits in the HashiCorp ecosystem, what functionality Packer provides us, and the primary use cases and benefits of using Packer.

While Packer is pretty simple, there are multiple components that we need to be aware of to properly create the artifacts we're looking for in our cloud or on-premises environment. Learn more about all the different components here.

Ok, you're sold on Packer and want to start using it, but how do you get it installed? Well, you're in luck because that's what this lecture is all about.

Did somebody say demo? Yep, our first demo where we'll install Packer to a brand new machine. But stay tuned, because we'll just continue to build out the lab as we continue through the course.

Learn how to interact with Packer using its only available interface, the Command Line. We'll break it all down for you so you're ready to jump right in and put Packer to use in your own environment.

More Labs....this time we'll show you all that cool CLI goodness we just learned about on our machine that we just installed Packer on.

By now you're getting to know Packer pretty well, and you can even interact with it using the CLI, but how does all this magic work? We'll break it down for you to showcase the workflow with a great example.

Oh snap, another lab? And we're still in the Intro section? Sweet! So...remember that awesome workflow that we just showcased a minute ago? Yea....the AWS one? Well, let's jump into the lab and actually do it and show you the workflow IRL <--- that means in real life...that's how the kool kids write it I'm told. In our case, maybe it means In a Real Lab :)

Nice, you made it to the end of this section. Think about all the awesome stuff you just learned in this section and it's just the Intro section. If you're not excited to continue on, go build all this manually and come back to us when you're tired of clicking around int he console :)

Welcome to this section, where we'll talk about how to write Packer templates. We'll focus on HCL in this course but we'll mention JSON as well.

In this section, we'll focus on the HCL syntax for your Packer templates.

In this lab, we will utilize the packer hcl2_upgrade command to convert JSON to HCL2 templates.

This lecture will ensure students understand how to work with the individual Packer components when writing templates in the HCL format.

This lab will walk you through updating a Packer HCL Template. It uses the amazon-ebs source to create a custom image in the us-west-2 region of AWS.

Welcome to the Builders section, where you'll learn about what Builders are and how to use them.

This demo will walk students through the creation of an AWS AMI using the amazon-ebs builder.

This demo will walk you through building a new image on the Azure cloud platform.

This demo will walk you through building a new image on the Google Cloud Platform.

This demo will walk you through building a new image on the VMware platform.

This lab will walk you through updating your Packer Template to build images across multiple regions within AWS.

This lab will walk you through updating your Packer Template to build different images for each operating system.

This lab will walk you through updating your Packer Template to build images across AWS and Azure.

This quick demo will show you how to target specific builds when using a Packer template with multiple images.

Welcome to the Packer Variables section, where students will learn what variables are and how to use them throughout Packer templates.

Cool, we know what variables are and why we want to use them, but how do we get started? Well, variables must be declared somewhere, so let's start with that....

Alright, let's start using variables throughout our Packer templates so they can be reusable and more flexible for our needs.

Did you know you can use your local system's environment variables to set values or query for more information. That's what we'll walk through in this lecture.

Packer user variables allow your templates to be further configured with variables from the command-line, environment variables, Vault, or files. This lets you parameterize your templates so that you can keep secret tokens, environment-specific data, and other types of information out of your templates. This maximizes the portability of the template.

Welcome to the Packer provisioners section, where students will learn all about provisioners and how to use them in Packer templates.

This lecture will walk students through some basic examples of the different types of provisioners that are available to use in Packer.

Beyond just the provisioner itself, there are many different useful features that can help you write more efficient and capable Packer templates. Let's check them out.

This lab will walk you through adding a provisioner to your Packer HCL Template. Provisioners use built-in and third-party software to install and configure the machine image after booting.

The lecture will dive a bit deeper on using post-processors in your Packer templates.

This lab will walk you through adding a post-processor to your Packer HCL Template. Post-processors run after the image is built by the builder and provisioned by the provisioner(s). Post-processors are optional, and they can be used to upload artifacts, re-package, or more.

When using Packer in your organization, it's important to think about how to organize your code. This will help make Packer simple to use and drive efficiency.

Packer HCL Templates can be specified in a single file.pkr.hcl or in multiple files within a single folder. To assist with code readability it may be beneficial to break out a large pkr.hcl file into separate files.

When performing image builds it might be favorable to only target a particular build by type or cloud target. Packer provides this ability via the -only and -exclude parameters.

Let's be honest, getting it all right the first time can be pretty tough. But have no fear, Gabe is here to show you how to effectively debug your Packer templates in this hands-on lab.

The breakpoint provisioner will pause until the user presses "enter" to resume the build. This is intended for debugging purposes, and allows you to halt at a particular part of the provisioning process.

The Ansible Packer provisioner runs Ansible playbooks. It dynamically creates an Ansible inventory file configured to use SSH, runs an SSH server, executes ansible-playbook , and marshals Ansible plays through the SSH server to the machine being provisioned by Packer.

While Packer excels at building automated machine images, Terraform excels at deploying those images which is why Packer and Terraform are typically found to be complementary for infrastructure as code lifecycles.

In this demo, we'll showcase how you can easily integrate Packer with HashiCorp Vault to retrieve static credentials from the KV V2 secrets engine.

In this demo, we'll take a look at how you can use HashiCorp Vault's AWS Secrets Engine to obtain dynamic AWS credentials for your Packer build.

This demo will showcase how to integrate Packer with GitLab CI/CD and have your Packer builds automatically triggered by simply committing your new Packer template to a repo.

Bonus Section