DevOps Handbook

Comprehensive DevOps handbook covering methodologies, best practices, tooling guides, automation patterns, and real-world implementation notes — from CI/CD and infrastructure-as-code to cloud-native architectures, observability, and security.

How we treat other topics (e.g. networking): This handbook is about DevOps. Where we mention networking, security, or other domains, we keep it short and give enough context so you can move forward without leaving. If you want to go deeper, we point you to the right deep-dive repo (e.g. Networks-Deep-Dive for networking). Same idea applies for other deep dives — optional, not required to progress.

Structure

• Methodologies/ — DevOps culture, practices, workflows, and team collaboration patterns
• CiCd/ — Continuous Integration and Continuous Delivery pipelines, practices, and tools
• IAC/ — Infrastructure as Code: Terraform, CloudFormation, Pulumi, and best practices
• Automation/ — Automation patterns, scripting, orchestration, and workflow automation
• Cloud-Native/ — Cloud-native architectures, patterns, and platform engineering
• Observability/ — Monitoring, logging, tracing, SLO/SLI; one folder per tool (Prometheus, Grafana, OpenTelemetry, etc.)
• Security/ — Security practices, compliance, secrets management, and threat mitigation
• Operating-Systems/ — Fundamentals/ (OS-agnostic theory), Linux/, Windows/, Unix/, MacOS/: deep coverage of OS topics (process, memory, I/O, shell, services, storage, virtualization, etc.); add new topics as needed
• Languages/ — General-purpose, config/query, niche/security (Assembly, Nim, VBA, R, Erlang, Zig, Delphi, COBOL, Objective-C, OCaml, F#, Ada, Julia, Dart, D, Haskell, Clojure, Crystal, VB.NET, Tcl, Fortran), and Web3 (Solidity, Vyper, Move, Cairo): C/C++, Python, Go, Java, JavaScript, Rust, TypeScript, Ruby, Groovy, C#, Scala, Perl, Kotlin, PHP, Lua, Swift, Elixir, LaTeX, Shell, HCL, JSON, YAML; Shell in Operating-Systems, HCL in IAC, JSON/YAML across CiCd/IAC/Cloud-Native; database query languages (e.g. SQL, PromQL) in Databases-Deep-Dive; one folder per language; add topic files as needed
• Assets/ — Images and diagrams (with credit in the topic files). See Assets/README.md for structure and where to store images per section.

Visual guides and images

We use diagrams and images where they help (stored under Assets/<Section>/ and credited in each topic). For extra visual learning alongside the handbook:

• GeeksforGeeks — Operating System Tutorial — OS basics, kernel, process states, system call, deadlock, memory, file systems, disk scheduling, and more. Many articles include diagrams; we use and credit them in Operating-Systems topics (e.g. kernel, process and PCB, request flow).
• ByteByteGo — Computer Fundamentals — Process vs thread, deadlock, paging vs segmentation, memory and storage, boot process. Great for Operating Systems and fundamentals.
• ByteByteGo — DevOps and CI/CD — CI/CD pipelines, Docker, Kubernetes, deployment strategies, logging/tracing/metrics, Terraform, and more. Use as visual reference for CiCd, Cloud-Native, IAC, and Observability as we add content.
• Tecmint — RAID series — Introduction to RAID, RAID levels, and Linux mdadm tutorials (RAID 0/1/5/6/10, grow array, recover/rebuild). Linked as further reading in Operating-Systems RAID topics.

Images from these sources can be downloaded into the right Assets/ subfolder and linked from topic files (with credit). This keeps the handbook engaging and consistent with our content guidelines.

How to use

1. Start with Methodologies/ for DevOps culture and foundational practices
2. Use Operating-Systems/ for OS fundamentals (theory first in Fundamentals/), then Linux, Windows, Unix, or macOS implementation and commands
3. Use Languages/ when you need to read or write code (Python for automation, Go for K8s/Terraform, etc.)
4. Jump into CiCd/ or IAC/ based on your immediate needs
5. Use Automation/ for workflow and orchestration patterns
6. Reference Observability/ and Security/ for production operations
7. Keep checklists and commands handy for day-to-day operations

Where to go deeper

When a topic here touches another domain, we give enough context to continue in this handbook. If you want full-depth coverage, see these repositories:

Topic	Repository	What’s there
Networking	Networks-Deep-Dive	Physical layer to cloud-native: foundations, transport, routing, security, observability, labs.
Containers	Containerization-Deep-Dive	Runtimes (Docker, Podman), orchestration (Kubernetes, Swarm, OpenShift), managed (GKE, EKS, AKS).
Databases & object storage	Databases-Deep-Dive	Relational, NoSQL, analytical, vector; engines and managed services. For why S3, GCS, or similar object stores are used and how they fit in, see this repo.
System design	System-Design-Concepts	Patterns, components, trade-offs; fundamentals, caching, messaging, case studies.
DSA	Datastructures-and-Algorithms	Data structures and algorithms notes and solutions (GFG, Leetcode).
Commands	Commands-and-Cheatsheets	Essential commands and cheatsheets (languages, DevOps & cloud, databases).

Contributing — content guidelines

When adding content:

• Flow in each topic: Text first → then image (if it fits the context) → code block only if something more is needed (e.g. thought flow, step-by-step).
• Images are first priority when they fit: store under Assets/<Section>/, reference in the topic right after the text they illustrate, and credit the original source (e.g. "Image: ByteByteGo – CI/CD Pipeline").
• If there’s no suitable image, use a code block for visual understanding (diagram, flow, or fundamentals).
• Content must be standalone (beginner-to-advanced) with references at the end of each topic. Be factually correct; verify when unsure.
• External links: Use only GitHub repository URLs for other repos. For a database or object store (e.g. S3, GCS) that has only a placeholder in Databases-Deep-Dive, link to that repo path (e.g. .../Databases-Deep-Dive/tree/main/relational/mysql).

Contributing

• Prefer concise, copy/paste-ready commands and configurations
• Include real-world examples and common pitfalls
• Document trade-offs and when to use specific patterns
• Keep examples minimal and reproducible