Building Automated Machine Images with HashiCorp Packer

Master HashiCorp Packer to automate your machine images across multiple platforms -- a critical skill to enabling automation in your environments.

This is the most extensive and thorough course on Udemy for learning how to use HashiCorp Packer in your organization. It is the only course that teaches you all about using HCL2 to create your Packer templates, the new standard moving forward.

It doesn't matter if you have never used Packer before or have been writing Packer templates in the older JSON style. You'll step away from this course ready to tackle any Packer build.

This course includes in-depth lectures on all of the core HashiCorp Packer components and tons of included labs. You also get access to hands-on labs that you can perform in your own environment, as we believe taking a hands-on approach is the best way to learn. Each lesson includes a mindmap of the topics, a PDF of the slides, and multiple labs to demonstrate the topics at hand.

Beyond getting comfortable with HashiCorp Packer, we're taking it one step further and showing you how to integrate HashiCorp Packer with other automation tools, such as HashiCorp Terraform, HashiCorp Vault, and GitLab CI/CD. We'll demonstrate building images on multiple public cloud platforms, and we'll even show you how to build VMware images using Packer.

Topics Covered

• HashiCorp Packer intro and the problems it solves
• Use cases and examples
• Writing Packer Templates using HCL2 (the new standard for Packer 1.7+)
• Different Builders
• Using Variables
• Provisioners
• Post-Processors
• Code Organization
• Integrations such as automation pipelines, secrets management, Terraform, and more

You will get lifetime access to a wealth of content plus all of the labs for your own use. Don't forget that this course also comes with Udemy's 30-day guarantee.

About HashiCorp Packer

Packer is an open-source tool for creating identical machine images for multiple platforms from a single source configuration. Packer is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel.