DevOps Uncomplicated: Hands-On Automation | Udemy


DevOps Uncomplicated: Hands-On Automation | Udemy [Update 09/2025]
English | Size: 5.9 GB
Genre: eLearning

Master Docker, Scripting, Terraform, Ansible, Kubernetes, CI/CD on Azure, and Datadog — From Zero to Advanced with Hands

What you’ll learn
Automate infrastructure on AWS and Azure using Terraform (VMs, networks, Kubernetes)
Build professional CI/CD pipelines with GitHub Actions (tests, deploy, auto-destroy)
Manage Kubernetes clusters on Azure (AKS) with HPA, Ingress, and KEDA
Monitor cloud environments with Datadog (metrics, logs, dashboards)
Write Ansible playbooks for automated server configuration
Work with containers (Docker multistage, Docker Compose, Docker Hub)
Set up complete DevOps environments (Git, Linux, IaC, CI/CD)
Prepare for certifications like AZ-400 DevOps
Example: Conduct a case study to manage a project from concept to completion

WHAT YOU’LL LEARN — HANDS-ON, NO FLUFF

Cloud, Hands-On

Build VMs, networks, and storage on AWS and Azure (Labs 10–15, 38–44)

Real Automation

Write scripts to create multiple VMs in bulk (Labs 40, 44) and manipulate JSON using jq (Labs 41, 43)

Professional Terraform

Provision Kubernetes clusters (Labs 52–55) and VMs with variables and modules (Labs 48–51)

Applied Ansible

Deploy containers, manage users and permissions (Labs 60–61)

Complete CI/CD

Build a GitHub Actions pipeline with testingdeployment, and auto-destroy infrastructure (Labs 64–80)

Kubernetes on Azure

Master HPAIngressKEDA, and Probes (Labs 91–93)

Elite Monitoring

Create Datadog dashboards for Kubernetes and EC2 with visual alerts (Labs 97–102)

NEW MODULES

  • AI for DevOps → ChatGPT, Claude, Gemini (in progress)
  • Vibe Coding → For DevOps workflows
  • Python → For DevOps automation

DIRECT, TO-THE-POINT METHODOLOGY

  • Each lesson = 1 practical skill
  • No disconnected theory → everything has a real-world application
  • Reusable labs → apply scripts directly at work
  • From zero to pro → start with Git/Linux and evolve into full DevOps projects

WHAT YOU’LL BUILD

  • multi-cloud environment (AWS + Azure) using CLI and Terraform (Sections 2, 6, 7)
  • CI/CD pipeline that testsdeploys, and auto-destroys infrastructure (Section 9)
  • Kubernetes cluster with metric-based autoscaling (Lab 91)
  • Datadog monitoring system with visual alerts (Lab 101)

WHY THIS COURSE IS DIFFERENT

  • Focus on troubleshooting (Labs 79, 100) — a key skill most courses skip
  • Aligned with the AZ-400 certification — many students are already certified
  • Real-world tools → TerraformAnsibleGitHub ActionsHelmKEDADatadog

WHO THIS COURSE IS FOR

  • DevOps beginners (we start with Git and Linux — Sections 1, 3, 4)
  • Developers who want to understand and implement CI/CD
  • Sysadmins migrating to cloud environments

FREQUENTLY ASKED QUESTIONS

Do I need experience?
No. You’ll learn step by step, starting from Git and Linux all the way to advanced DevOps projects.

Can I apply this at work?
Absolutely. The labs use real-world tools and free accounts on AWS and Azure.

What makes this course different?

  • No pointless “hello world” exercises
  • Real Terraform + Kubernetes + Helm labs
  • Multi-cloud automation with AWS and Azure
  • practical, automation-focused approach for real projects

EXCLUSIVE GUARANTEE

  • Lifetime access to 103 fully hands-on lessons
  • Udemy certificate of completion
  • All scripts included — ready to use in your day-to-day work

This course makes limited use of artificial intelligence tools for language adjustments and improvements in clarity. All course content, labs, and explanations were created and reviewed by the instructor.

Who this course is for:

  • Infrastructure and support professionals looking to transition into DevOps, or anyone curious about DevOps but unsure where to start
  • Python for DevOps enthusiasts seeking practical applications
  • Java developers wanting to integrate DevOps practices
  • Support analysts aiming to advance into automation and cloud environments
  • Professionals interested in Artificial Intelligence applied to DevOps
  • Example: Beginner Python developers curious about data science
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