Devops Uncomplicated: Hands-On Automation

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

Requirements

Basic IT Knowledge — Simple understanding of how servers and networks work (beginner level)

Computer with Internet Access — Windows, Mac, or Linux (for hands-on labs)

Free Cloud Accounts — AWS Free Tier and Azure Free Tier (we teach you how to set them up)

Willingness to Learn — The course starts from zero with Git, Linux, and cloud concepts

No programming experience required — You’ll learn everything you need step by step

Description

WHAT YOU’LL LEARN — HANDS-ON, NO FLUFFCloud, Hands-OnBuild VMs, networks, and storage on AWS and Azure (Labs 10–15, 38–44)Real AutomationWrite scripts to create multiple VMs in bulk (Labs 40, 44) and manipulate JSON using jq (Labs 41, 43)Professional TerraformProvision Kubernetes clusters (Labs 52–55) and VMs with variables and modules (Labs 48–51)Applied AnsibleDeploy containers, manage users and permissions (Labs 60–61)Complete CI/CDBuild a GitHub Actions pipeline with testing, deployment, and auto-destroy infrastructure (Labs 64–80)Kubernetes on AzureMaster HPA, Ingress, KEDA, and Probes (Labs 91–93)Elite MonitoringCreate Datadog dashboards for Kubernetes and EC2 with visual alerts (Labs 97–102)NEW MODULESAI for DevOps → ChatGPT, Claude, Gemini (in progress)Vibe Coding → For DevOps workflowsPython → For DevOps automationDIRECT, TO-THE-POINT METHODOLOGYEach lesson = 1 practical skillNo disconnected theory → everything has a real-world applicationReusable labs → apply scripts directly at workFrom zero to pro → start with Git/Linux and evolve into full DevOps projectsWHAT YOU’LL BUILDA multi-cloud environment (AWS + Azure) using CLI and Terraform (Sections 2, 6, 7)A CI/CD pipeline that tests, deploys, and auto-destroys infrastructure (Section 9)A Kubernetes cluster with metric-based autoscaling (Lab 91)A Datadog monitoring system with visual alerts (Lab 101)WHY THIS COURSE IS DIFFERENTFocus on troubleshooting (Labs 79, 100) — a key skill most courses skipAligned with the AZ-400 certification — many students are already certifiedReal-world tools → Terraform, Ansible, GitHub Actions, Helm, KEDA, DatadogWHO THIS COURSE IS FORDevOps beginners (we start with Git and Linux — Sections 1, 3, 4)Developers who want to understand and implement CI/CDSysadmins migrating to cloud environmentsFREQUENTLY ASKED QUESTIONSDo 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” exercisesReal Terraform + Kubernetes + Helm labsMulti-cloud automation with AWS and AzureA practical, automation-focused approach for real projectsEXCLUSIVE GUARANTEELifetime access to 103 fully hands-on lessonsUdemy certificate of completionAll scripts included — ready to use in your day-to-day workThis 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.

Overview

Section 1: Introduction

Lecture 1 Course Overview

Lecture 2 IMPORTANT: GitHub Repositories Used in This Course

Lecture 3 A Bit About the Instructor

Lecture 4 Prerequisites to Follow This Course

Lecture 5 IMPORTANT: Lab – Show Me the Code

Lecture 6 Lab: Setting Up the Environment

Section 2: First Steps – AWS and Azure

Lecture 7 Introduction to Cloud Computing (AWS and Azure) – Part 1

Lecture 8 Introduction to Cloud Computing (AWS and Azure) – Part 2

Lecture 9 Lab: IAM – Creating Users in AWS

Lecture 10 Lab: Creating the First EC2 (Virtual Machine) in AWS

Lecture 11 Lab: Creating a VPC (Virtual Private Cloud) in AWS

Lecture 12 Lab: Exploring the Azure Portal

Lecture 13 Lab: Creating a Virtual Machine in Azure

Lecture 14 Lab: Creating a Storage Account in Azure

Section 3: Linux for Automation

Lecture 15 Introduction to Linux for Automation – Part 1

Lecture 16 Introduction to Linux for Automation – Part 2

Lecture 17 Lab: Working with Directories in Linux

Lecture 18 Lab: Managing Files and Logs

Lecture 19 Lab: System Management

Lecture 20 Lab: Managing Permissions in Linux

Lecture 21 Lab: Installing and Managing Packages in Linux

Section 4: Essential Fundamentals – Git

Lecture 22 Lab: Configuring Git

Lecture 23 Lab: Creating a Repository and Setting Up the Connection

Lecture 24 Lab: First Commit and Updating the Readme

Lecture 25 Lab: Working with Branches and Pull Requests

Lecture 26 Lab: Working with Git Pull

Lecture 27 Lab: Automating Git Commands

Lecture 28 Lab: Working with Directories in Linux

Lecture 29 Lab: Handling Files and Logs

Lecture 30 Lab: System Management

Lecture 31 Lab: Managing Permissions in Linux

Lecture 32 Lab: Installing and Managing Packages in Linux

Section 5: Containers with Docker

Lecture 33 Lab: Installing Docker on Ubuntu

Lecture 34 Lab: Essential Docker Commands

Lecture 35 Lab: Creating the First Dockerfile

Lecture 36 Lab: Multistage Dockerfile – Professional Image

Lecture 37 Lab: Using Gitignore in Practice with Docker

Lecture 38 Lab: Pushing Images to Docker Hub

Lecture 39 Lab: Working with Docker Compose

Section 6: Scripting & Automation on AWS and Azure

Lecture 40 Lab: AZ CLI – Getting Started

Lecture 41 Lab: AZ CLI – Creating Virtual Machines

Lecture 42 Lab: AZ CLI – Script for Creating VMs in Bulk

Lecture 43 Lab: AZ CLI – Handling JSON with AWS and JQ

Lecture 44 Lab: AWS CLI – Getting Started with the Command Line (S3)

Lecture 45 Lab: AWS CLI – Creating AWS Instances and Handling JSON

Lecture 46 Lab: AWS CLI – Creating VMs in Bulk

Section 7: Automation with Terraform

Lecture 47 Lab: Creating the First Terraform – Resource Group

Lecture 48 Lab: Creating an Azure VM (Data and Variables) – Part 1

Lecture 49 Lab: Creating an Azure VM – Part 2

Lecture 50 Lab: Creating an Azure VM – Part 3

Lecture 51 Lab: Creating an Azure VM – Part 4

Lecture 52 Lab: Creating an AKS (Kubernetes) Cluster with Terraform

Lecture 53 Lab: Creating Kubernetes Variables and Main.tf

Lecture 54 Lab: Using Helm with Terraform – Installing Keda and Ingress

Lecture 55 Lab: Creating the Cluster and Validating Pods

Section 8: Configuration with Ansible

Lecture 56 Lab: Setting Up the Environment

Lecture 57 Lab: First Commands with Ansible

Lecture 58 Lab: Creating the First Playbook

Lecture 59 Lab: Deploying a Container Using Ansible

Lecture 60 Lab: Managing Users with Ansible

Lecture 61 Lab: Destroying the Environment

Section 9: CI/CD with GitHub Actions

Lecture 62 Lab: First Workflow (Pipeline)

Lecture 63 Lab: Creating Infrastructure for Docker Workflow

Lecture 64 Lab: Creating Workflow (Docker Build and Push)

Lecture 65 Lab: Creating Base Infrastructure for the Pipeline

Lecture 66 Lab: Configuring Secrets

Lecture 67 Lab: Configuring Terraform (Virtual Machine)

Lecture 68 Lab: Configuring Ansible Playbook

Lecture 69 Lab: Creating the Pipeline (Workflow) – Step Validate

Lecture 70 Lab: Creating the Pipeline (Workflow) – Step Deploy

Lecture 71 Lab: Creating the Pipeline (Workflow) – Step Infrastructure Tests

Lecture 72 Lab: Creating the Pipeline (Workflow) – Step Destroy

Lecture 73 Lab: Validating Infrastructure and Application

Lecture 74 Lab: Running the Pipeline (Workflow) (Preview Enabled)

Lecture 75 Lab: Destroying Infrastructure via Workflow

Section 10: Kubernetes Fundamentals on Azure (AKS)

Lecture 76 Lab: Creating a Kubernetes Cluster with Terraform

Lecture 77 Lab: Creating the First Pod

Lecture 78 Lab: Creating a ReplicaSet

Lecture 79 Lab: Creating a Deployment

Lecture 80 Lab: Exposing the Application with a LoadBalancer

Lecture 81 Lab: Working with Namespaces

Lecture 82 Lab: Configuring Requests and Limits

Lecture 83 Lab: Setting Up Health Checks

Lecture 84 Lab: Configuring HPA and Load Testing

Lecture 85 Lab: Implementing the Ingress Controller

Lecture 86 Lab: Implementing KEDA (Kubernetes Event-driven Autoscaling)

Section 11: Introduction to Datadog

Lecture 87 Lab: Navigating Through Datadog

Lecture 88 Lab: Installing the Datadog Agent on Kubernetes

Lecture 89 Lab: Installing the Datadog Agent on an EC2 Instance

Lecture 90 Lab: Instrumenting a Java Application in Datadog

Lecture 91 Lab: Troubleshooting with Datadog – Traces and Logs

Lecture 92 Lab: Creating Dashboards in Datadog

Lecture 93 Lab: Setting Up Monitors in Datadog

Lecture 94 Conclusion

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