HashiCorp Packer - Build automated machine images

Build pre-configured machine images like AMIs, VMDK.VMX files, OVF exports. Automate image build with CI / CD pipeline
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HashiCorp Packer - Build automated machine images
905
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2.5 hours
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Nov 2020
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$19.99
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Why take this course?

🌟 Master Cloud Infrastructure Automation with HashiCorp Packer! 🌟

Course Title: [πŸ—οΈ] HashiCorp Packer - Build Automated Machine Images

Headline: Learn to create consistent and scalable machine images for AWS, VMware, Azure, and more with HashiCorp Packer!

Unlock the Power of Automation with HashiCorp Packer!

Are you ready to revolutionize your cloud infrastructure setup? With our comprehensive online course on HashiCorp Packer, you'll learn how to automate the process of building and maintaining machine images. Say goodbye to manual and error-prone processes, and hello to efficiency, consistency, and scalability!

Why Take This Course?

  • πŸ”„ Automated Image Creation: Build automated machine images for various platforms from a single configuration file. This means you can create images for AWS (AMIs), VMware (VMDK.VMX files), OVF exports, and more with just a few clicks.

  • 🏭 Parallel Processing: Discover how to create images in parallel, drastically reducing the time it takes to prepare your infrastructure.

  • Provisioning with Chef or Puppet: Utilize powerful provisioning tools like Chef or Puppet within Packer to ensure your images are perfectly configured before they ever hit your environment.

  • CI/CD Integration: Seamlessly integrate Packer with Continuous delivery (CI/CD) tools to automate the entire lifecycle of your machine images, from creation to deployment.

  • Real-World Application: Learn how to launch instances using your Packer-built images, test and verify them, and ensure they align with your infrastructure needs.

Course Highlights:

  • πŸ“š HCL Mastery: Dive deep into HashiCorp Configuration Language (HCL) to understand variables, outputs, and other details that make your Packer scripts powerful and flexible.

  • Diverse Provisioners & Builders: Explore different provisioners like Ansible, Chef, and Puppet, and learn how to select the appropriate builder for your project - whether it's a local shell or a remote Docker builder.

  • πŸ› οΈ Understanding of Ansible Builders: Get to grips with the difference between Ansible local and remote builders, and understand which scenario is best suited for each.

  • Docker Image Creation: Learn how to create a Docker container image using Packer's shell local and remote builder, giving you the versatility to build images in a containerized environment.

  • Windows AMI Mastery: Conquer the challenge of creating a Windows Amazon Machine Image (AMI) that meets all your requirements.

  • Vagrant Integration: Create and test Vagrant box images with ease, ensuring your local development environment is always in sync with production.

  • πŸ› οΈ Deep Dive into Post-Processors: Work with post-processors and other components of HashiCorp Packer to enhance your image building workflow.

Who Is This Course For?

  • DevOps engineers, SysAdmins, Cloud Architects, and anyone interested in automating the creation and deployment of machine images.

  • Developers looking to streamline their development and testing environments using Packer with Vagrant.

  • IT professionals aiming to reduce manual labor and increase scalability within their cloud infrastructure.

What Will You Achieve?

By completing this course, you'll be equipped to:

  • Automate the creation of consistent, scalable, and secure machine images across various platforms.

  • Integrate Packer with CI/CD pipelines for a more efficient deployment process.

  • Save time and resources by automating repetitive tasks associated with building and maintaining machine images.

Enroll Now to Elevate Your Cloud Infrastructure Skills with HashiCorp Packer! πŸš€

Don't miss out on the opportunity to become a master in infrastructure automation. Join us today and transform your approach to cloud infrastructure management! πŸ–₯οΈπŸš€

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3540840
udemy ID
02/10/2020
course created date
20/11/2020
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