Prerequisites

Start here if you want the shortest path from beginner fundamentals to Kubernetes and platform work.

This hub is the front door to KubeDojo. It covers the terminal, containers, Kubernetes basics, declarative thinking, Git, and modern delivery practices.

Who This Is For

absolute beginners who need a real starting point
learners who want the shortest route into cloud-native systems
people who need enough fundamentals before choosing Linux, Kubernetes, cloud, platform, AI literacy, or AI/ML depth

Do Not Skip This If

you are still shaky on the terminal, files, SSH, or software installation
Kubernetes terminology still feels abstract
you are tempted to jump straight into advanced tracks because they look interesting

Most confusion later in the curriculum comes from skipping this layer too early.

Core Sections

Zero to Terminal — 11 modules

For absolute beginners. From “what is a computer?” to deploying your first server from the command line.

Module	Topic
0.1	What is a Computer?
0.2	What is a Terminal?
0.3	First Terminal Commands
0.4	Files and Directories
0.5	Editing Files
0.6	Git Basics
0.7	What is Networking?
0.8	Servers and SSH
0.9	Software and Packages
0.10	What is the Cloud?
0.11	Your First Server

Cloud Native 101 — 5 modules

Containers, Docker, Kubernetes, and the cloud-native ecosystem from first principles.

Module	Topic
1.1	What Are Containers?
1.2	Docker Fundamentals
1.3	What Is Kubernetes?
1.4	Cloud Native Ecosystem
1.5	Monolith to Microservices

Kubernetes Basics — 8 modules

The practical Kubernetes starter path with kubectl, Pods, Deployments, Services, config, and YAML.

Module	Topic
1.1	Your First Cluster
1.2	kubectl Basics
1.3	Pods - The Atomic Unit
1.4	Deployments - Managing Apps
1.5	Services - Stable Networking
1.6	ConfigMaps & Secrets
1.7	Namespaces & Labels
1.8	YAML for Kubernetes

Philosophy & Design — 4 modules

Why Kubernetes works the way it does, and which legacy patterns are not worth your time.

Module	Topic
1.1	Why Kubernetes Won
1.2	Declarative vs Imperative
1.3	What We Don’t Cover
1.4	Dead Ends - Technologies to Avoid

Git Deep Dive — 10 modules

The professional Git path. Internals, rebasing, recovery, scale, collaboration, and the bridge into GitOps.

Module	Topic
1	Git Internals
2	Advanced Merging
3	Interactive Rebasing
4	Undo and Recovery
5	Worktrees and Stashing
6	Troubleshooting and Search
7	Remotes and PRs
8	Sparse Checkout and LFS
9	Hooks and Rerere
10	Bridge to GitOps

Modern DevOps — 6 modules

IaC, GitOps, CI/CD, observability, platform engineering, and DevSecOps.

Module	Topic
1.1	Infrastructure as Code
1.2	GitOps
1.3	CI/CD Pipelines
1.4	Observability Fundamentals
1.5	Platform Engineering
1.6	Security Practices (DevSecOps)

Suggested Route

Zero to Terminal
   |
   +--> Linux track (if you want deeper systems knowledge)
   |
Cloud Native 101
   |
Kubernetes Basics
   |
Philosophy & Design
   |
Git Deep Dive
   |
Modern DevOps

Use Git Deep Dive after you are comfortable with basic Git from Zero to Terminal. It is not required before Cloud Native 101 or Kubernetes Basics, but it should be treated as a practical prerequisite before serious IaC, GitOps, CI/CD, and team workflows.

The practical rule is simple:

0.6 Git Basics
   |
Git Deep Dive
   |
Modern DevOps / IaC / GitOps / CI-CD

Git Is Not Optional For Modern Infrastructure Work

The sequence matters:

0.6 Git Basics in Zero to Terminal gives you the minimum viable workflow
Git Deep Dive turns Git into an operational tool rather than just a backup mechanism
only after that do Modern DevOps, IaC, and GitOps become much easier to use correctly

If a learner skips the Git path, they can still read later modules, but they will be missing one of the core skills behind modern infrastructure practice.

Common Next Routes

Prerequisites -> Linux if you want stronger systems depth before operations-heavy work
Prerequisites -> Kubernetes Certifications if you want external goals and hands-on pressure
Prerequisites -> Cloud if your main goal is hyperscaler fluency after cluster basics
Prerequisites -> AI if you want a true beginner-friendly path into AI literacy and disciplined AI use
Prerequisites -> AI/ML Engineering if you already know you want to build AI systems, run local models, or move toward MLOps
Prerequisites -> Modern DevOps should usually mean Zero to Terminal -> Git Deep Dive -> Modern DevOps, not a direct jump that skips Git maturity

Platform Engineering is usually not the immediate next stop for beginners. Most learners should reach it through Kubernetes, Linux, or Cloud first.

These are not inside the prerequisites/ section, but they are common next steps:

Path	Why It Matters
Linux	Go deeper into processes, networking, storage, security, and operations
AI	Build practical AI literacy and AI working habits before advanced engineering depth
AI/ML Engineering	Start the AI/ML track if your goal is LLMs, MLOps, or AI infrastructure

After Prerequisites

Ready to specialize? Choose your next track:

Goal	Next Step
Master Linux	Linux
Get certified	Kubernetes Certifications
Learn cloud providers	Cloud
Run Kubernetes on your own hardware	On-Premises Kubernetes
Go deeper into platform practices	Platform Engineering
Learn AI from zero	AI
Explore AI/ML systems	AI/ML Engineering