The Ultimate DevOps & Cloud Prerequisites Guide: Explained in 20 Minutes

DevOps and cloud computing are some of the most in-demand topics in the IT industry today. A common question for aspiring professionals is simply: "Where do I start?" Many who dive into advanced DevOps topics find themselves missing the foundational knowledge required to truly grasp the concepts.

This article serves as a comprehensive prerequisite guide. Think of it as a computer science crash course designed to get your basics right, making your future journey into DevOps and cloud technologies significantly smoother. We'll cover everything from setting up a lab environment to understanding Linux, networking, and the entire DevOps toolchain.

Demystifying the DevOps Toolchain: A Developer's Story

When you hear about DevOps, you're bombarded with a dizzying array of tools: Docker, Kubernetes, Ansible, Terraform, Git, GitHub, Jenkins, Prometheus, Grafana, and more. It can be overwhelming. Let's demystify these tools by following a story of an application's journey from a simple idea to a full-scale, automated production system.

The Spark of an Idea

It all begins with you and a world-changing idea: a website to book advance tickets to Mars. Like any developer, you skip the market research and jump straight into coding. Hours later, version one is ready. But how do you share it with the world?

Your code is running on your laptop, accessible only at http://localhost:8080. To make it public, you need to host it on a server that’s always on. You get a server (physical or a cloud VM), but you can't just copy the code. The server must be configured with the same programming language runtimes (like Python or Java) and the exact versions of all libraries your application depends on.

Once configured, your application is live on the server's IP address. You buy a domain name, point it to the IP, and tweet it out. Your site goes viral, and thousands of users are booking their trip to the Red Planet.

Your initial workflow is simple: 1. Develop: Write code on your local machine. 2. Build: Convert the text-based code into an executable or binary file using tools like Maven or Python setup tools. 3. Deploy: Move the executable to the production server and run it.

Scaling Collaboration with Git and GitHub

With success comes feature requests. You bring in friends to help develop the application. Now, multiple people are working on the same codebase, leading to conflicts and overwritten files. You need a way to collaborate effectively.

Enter Git. Git is a version control system that allows multiple developers to work on the same project simultaneously. Everyone installs Git, uses git pull to get the latest code from a central hub, makes their changes, and uses git push to send them back.

This central hub is often GitHub (or similar platforms like GitLab and Bitbucket). GitHub is a cloud-based platform that hosts Git repositories. It provides a web interface to manage projects, users, access levels, and documentation. With Git and GitHub, your development collaboration issues are solved.

Automating the Pipeline with CI/CD

While development is smoother, deploying is still a manual, risky process. With multiple developers contributing, you can't build on your individual laptops anymore. The build process moves to a dedicated build server. You also add a test environment to catch bugs before they reach production.

The new workflow looks like this: 1. Developers push code to GitHub. 2. Manually copy the code to a build server and build the executable. 3. Manually copy the executable to a test server and run tests. 4. Manually copy the executable to the production server and deploy.

This manual process is slow and error-prone. You decide to release new features only once a week, but this frustrates users and developers. You need to release features faster and more frequently.

This is where CI/CD (Continuous Integration/Continuous Delivery) tools come in. Tools like Jenkins, GitHub Actions, or GitLab CI/CD automate this entire pipeline. Now, every time code is pushed to GitHub, it's automatically: - Pulled to the build server. - Built into an executable. - Deployed to the test server and tested. - Deployed to the production server upon successful testing.

This automation allows your team to ship features and fixes faster, keeping users happy.

Ensuring Consistency with Containers

Your CI/CD pipeline is seamless, but one problem remains: dependencies. The application requires specific libraries and packages, and they must be configured identically on every server. A missed or incorrect version can cause the application to fail.

Containers solve this problem. Containers package an application and all its dependencies into a single, portable unit called an image. This image can run on any system without worrying about the underlying environment.

Docker is the leading technology for working with containers. A developer creates a Dockerfile that specifies all the dependencies. During the build step, this file is used to create a Docker image. This image can then be run as a container on any server with a simple docker run command. Containers also provide process isolation, allowing you to run multiple, separate instances of your application on the same server.

Managing Fleets of Containers with Kubernetes

Your user base is growing, and you need to run your application on multiple servers for high availability and load balancing. How do you manage containers across a fleet of servers? How do you scale up when traffic increases and scale down when it decreases? How do you automatically restart a container if it fails?

Container orchestration platforms like Kubernetes are the answer. Kubernetes allows you to declare how your containers should be deployed and ensures the system always matches that declared state. It can automatically scale your containers (and even the underlying servers), manage resources efficiently, and handle failures gracefully.

Defining Infrastructure as Code (IaC)

Your application is now a well-oiled machine. But what about the underlying infrastructure? Every time you add a new server, it must be configured identically to the others—same OS version, storage, networking, and pre-installed software like Docker. Doing this manually through a cloud provider's UI is time-consuming and prone to human error.

Tools like Terraform automate infrastructure provisioning. With Terraform, you define your entire infrastructure—VMs, storage, networks—in configuration files. This is known as Infrastructure as Code (IaC).

Here is a snippet of what a Terraform file looks like: ```terraform resource "awsinstance" "web" { ami = "ami-0c55b159cbfafe1f0" instancetype = "t2.micro"

tags = { Name = "WebAppServer" } } `` This code is stored in a Git repository, just like your application code. If you need to make a change, you update the code and runterraform apply`. Terraform ensures your infrastructure always matches the state defined in your code.

After provisioning servers with Terraform, you can use a tool like Ansible for configuration management. While Terraform is excellent for creating and destroying infrastructure, Ansible excels at post-provisioning tasks like installing and configuring software on those servers. Ansible uses "Playbooks" (written in YAML) to define these tasks, which are also stored as code.

Monitoring and Feedback

With everything running, you need to maintain it. You want to monitor server CPU and memory usage, identify performance bottlenecks, and take preventive action.

Prometheus is a powerful monitoring tool that collects metrics from your servers and stores them centrally. To make sense of this data, you use Grafana, which visualizes the metrics collected by Prometheus in beautiful charts and graphs.

This entire cycle—from idea to code, build, test, deploy, and monitor—is the essence of DevOps. It's a combination of culture, processes, and tools that enables organizations to deliver high-quality software consistently and rapidly.

Linux Crash Course

A solid understanding of Linux is non-negotiable in the DevOps world. Most servers you'll encounter run Linux, so getting comfortable with the command-line interface (CLI) is essential.

The Linux Shell

The shell is a text-based interface for interacting with the operating system. There are different kinds of shells, like sh, csh, zsh, and bash (Bourne Again Shell). bash is the most common and powerful. You can see which shell you're using with this command:

echo $SHELL

Basic Linux Commands

Here are some fundamental commands you'll use daily:

  • echo: Prints text to the screen. bash echo "Hello, World!"
  • ls: Lists the contents of a directory.
  • cd: Changes the current directory. bash cd /home/user/documents
  • pwd: Prints the present working directory.
  • mkdir: Creates a new directory. bash mkdir new_folder
  • mkdir -p: Creates a directory tree. bash mkdir -p /tmp/asia/india/bangalore
  • rm -r: Removes a directory and its contents.
  • cp -r: Copies a directory and its contents.

Working with Files

  • touch: Creates an empty file. bash touch new_file.txt
  • cat > filename: Writes content to a file. Press Ctrl+D to save and exit.
  • cat filename: Displays the content of a file.
  • cp: Copies a file. bash cp source.txt destination.txt
  • mv: Moves or renames a file. bash mv old_name.txt new_name.txt
  • rm: Removes a file.

The VI Text Editor

When working on remote servers, you'll often need to edit configuration files. vi (or its improved version, vim) is a powerful, ubiquitous text editor in Linux.

vi has two main modes: 1. Command Mode: The default mode when you open a file. Used for navigation, deleting, copying, and pasting. 2. Insert Mode: Used for writing and editing text. Press i to enter Insert Mode. Press Esc to return to Command Mode.

Essential vi Commands (in Command Mode): - Navigation: Arrow keys or h (left), j (down), k (up), l (right). - Delete Character: x - Delete Line: dd - Copy Line: yy - Paste: p - Save: :w - Quit: :q - Save and Quit: :wq - Quit without Saving: :q! - Search: /<search_term> and press n to find the next occurrence.

User Management and Permissions

  • whoami: Shows the current user.
  • id: Shows user and group information.
  • su <username>: Switches to another user.
  • sudo: The most critical command for system administration. Linux has a superuser called root with unrestricted access. Regular users have limited permissions. To perform administrative tasks (like installing software), a regular user with sudo privileges can prefix a command with sudo to execute it with root permissions. If you ever get a "Permission denied" error, you probably forgot to use sudo.

Downloading Files

You can download files from the internet directly from the CLI using these tools: - curl -O <URL>: Downloads a file and saves it with its original name. - wget -O <filename> <URL>: Downloads a file and saves it with a specified name.

Package Management in Linux

Package managers automate the process of installing, updating, and removing software. CentOS (and Red Hat) uses RPM-based package managers.

  • rpm: The Red Hat Package Manager. It installs .rpm files but doesn't handle dependencies automatically.
  • yum: A higher-level package manager that uses rpm underneath. yum automatically finds and installs all necessary dependencies from software repositories.

Common yum Commands: - yum install <package_name>: Installs a package and its dependencies. - yum remove <package_name>: Removes a package. - yum list <package_name>: Shows available and installed packages. - yum repolist: Lists all configured software repositories.

Repository configurations are stored in files under the /etc/yum.repos.d/ directory.

Managing Services in Linux

Software that runs in the background, like web or database servers, is managed as a service. Modern Linux systems use systemd to manage services. The primary command is systemctl.

Common systemctl Commands: - sudo systemctl start <service_name>: Starts a service. - sudo systemctl stop <service_name>: Stops a service. - sudo systemctl status <service_name>: Checks the status of a service. - sudo systemctl enable <service_name>: Configures a service to start automatically on boot. - sudo systemctl disable <service_name>: Disables a service from starting on boot.

You can create your own custom service by creating a unit file in /etc/systemd/system/. For example, to run a Python script as a service named my-app.service:

[Unit]
Description=My Custom Python Application

[Service]
ExecStart=/usr/bin/python /opt/code/my_app.py
Restart=always

[Install]
WantedBy=multi-user.target

After creating this file, run sudo systemctl daemon-reload to inform systemd of the new service.

Setting Up Your Own Lab Environment

While cloud-based labs are convenient, a personal learning environment on your laptop is invaluable for persistent changes and custom projects.

Virtualization with Hypervisors

Instead of installing numerous tools directly on your laptop, which can lead to conflicts and performance issues, use a virtual machine (VM). A VM is an isolated environment where you can experiment freely. If something breaks, you can delete it and start over.

Software that creates and runs VMs is called a hypervisor. - Type 1 Hypervisors: Run directly on hardware (e.g., VMware ESX). Used in enterprise data centers. - Type 2 Hypervisors: Run on top of an existing operating system (e.g., Oracle VirtualBox, VMware Workstation). Perfect for local labs.

Oracle VirtualBox is a free, open-source, and feature-rich choice that runs on Windows, macOS, and Linux.

Setting Up a VM with VirtualBox

  1. Download and Install VirtualBox.
  2. Get a Pre-built VM Image: Instead of installing an OS from scratch, download a pre-configured VM image from a site like osboxes.org. This saves a lot of time.
  3. Create a New VM: In VirtualBox, click "New," give it a name, and when prompted for a hard disk, select "Use an existing virtual hard disk file" and choose the image you downloaded.
  4. Configure and Start: Adjust CPU and memory settings if needed, then start the VM.

VirtualBox Networking Explained

Networking is often the trickiest part of setting up VMs. Here are the main options in VirtualBox:

  • NAT (Network Address Translation): The default setting. The VM can access the internet through the host machine, but it's isolated and cannot be reached from the outside or by other VMs. Each VM gets its own private network.
  • NAT Network: Similar to NAT, but all VMs attached to the same NAT Network are on the same private network and can communicate with each other. They can still access the internet, but cannot be reached from the outside.
  • Bridged Adapter: The VM appears as another physical device on your local network. It gets its own IP address from your router and is fully accessible by other devices on your network.
  • Host-Only Adapter: Creates a private network between the host machine and the VMs. The VMs can talk to each other and the host, but they cannot access the internet.

Pro-Tip: For a flexible setup, use two network adapters on your VMs. 1. Adapter 1 (Host-Only): For communication between VMs and the host. 2. Adapter 2 (NAT): For internet access.

Automating Lab Setup with Vagrant

Manually setting up multiple VMs is tedious. Vagrant is a tool that automates the creation and management of development environments. With a single command, vagrant up, Vagrant can download a VM image, create and configure the VM in VirtualBox, set up networking, and even run provisioning scripts.

All configuration is done in a single file called a Vagrantfile. Here's a simple example:

Vagrant.configure("2") do |config|
  config.vm.box = "centos/7"

  # Forward a port from the host to the guest
  config.vm.network "forwarded_port", guest: 80, host: 8080

  # Share a folder from the host to the guest
  config.vm.synced_folder "../data", "/vagrant_data"

  # Customize the provider (VirtualBox)
  config.vm.provider "virtualbox" do |vb|
    vb.memory = "1024"
    vb.cpus = "2"
  end

  # Run a shell script on boot
  config.vm.provision "shell", inline: <<-SHELL
    yum install -y httpd
    systemctl start httpd
    systemctl enable httpd
  SHELL
end

You can share this Vagrantfile with others, and they can spin up an identical environment with one command. It's an incredibly powerful tool for creating reproducible lab environments.