Agar tumhara goal DevOps + SRE + Automation hai, to modules random order me mat seekho. Ek roadmap follow karo. Har module ka real-world use hona chahiye.

Phase 1: File & System Automation (Must Know)

Ye modules har DevOps engineer use karta hai.

Module	Kyu seekhna hai?	Example
✅ pathlib	Files/Folders manage karna	Logs, configs, backups
✅ os	Environment variables, process info	os.environ, os.chdir()
✅ shutil	Copy, move, zip, delete	Backup scripts
✅ subprocess	Linux commands run karna	kubectl, docker, git
✅ glob	Pattern se files find karna	*.log, *.yaml
✅ tempfile	Temporary files	Installers, scripts
✅ zipfile / tarfile	Archives banana	Log rotation

---

Phase 2: Data Handling

Automation scripts me data bahut aata hai.

Module	Use
✅ json	APIs aur config files
✅ yaml (PyYAML)	Kubernetes, Ansible
✅ csv	Reports
✅ configparser	INI config files
✅ tomllib (Python 3.11+)	TOML configs

Example:

import json

with open("config.json") as f:
    config = json.load(f)

---

Phase 3: Dates & Logging

Har automation script me logging honi chahiye.

Module	Use
✅ datetime	Time calculations
✅ time	Sleep, timestamps
✅ logging	Professional logging

Instead of:

print("Deployment started")

Use:

import logging

logging.info("Deployment started")

Ye production-level approach hai.

---

Phase 4: Shell Automation (Most Important)

⭐ subprocess

Ye DevOps ki backbone hai.

Example:

import subprocess

result = subprocess.run(
    ["kubectl", "get", "pods"],
    capture_output=True,
    text=True
)

print(result.stdout)

Isse tum Python se:

• Docker

• Kubernetes

• Terraform

• Git

• Linux commands

• AWS CLI

sab chala sakte ho.

---

Phase 5: HTTP APIs

Almost har cloud tool API provide karta hai.

requests

import requests

response = requests.get("https://api.github.com")

print(response.status_code)

Use cases:

• GitHub API

• Jira

• Jenkins

• Grafana

• Prometheus

• AWS APIs

• Slack notifications

---

Phase 6: CLI Scripts

Professional scripts arguments lete hain.

argparse

Example:

python deploy.py --env prod --region us-east-1

Python:

import argparse

parser = argparse.ArgumentParser()

parser.add_argument("--env")

args = parser.parse_args()

print(args.env)

---

Phase 7: Environment Variables

Secrets kabhi code me nahi likhte.

import os

token = os.getenv("GITHUB_TOKEN")

Ye DevOps ka standard practice hai.

---

Phase 8: Regex

re

Logs parse karne ke liye.

Example:

ERROR
WARNING
INFO

Python:

import re

re.findall(r"ERROR", log)

---

Phase 9: Concurrency

SRE me multiple servers ek saath check karne padte hain.

Modules:

• threading

• concurrent.futures

• asyncio

Example:

100 servers ka health check parallel me karna.

---

Phase 10: Cloud SDKs

After Python basics:

AWS

boto3

Azure

azure-sdk

Google Cloud

google-cloud

---

Libraries Every DevOps Engineer Should Know

• requests

• PyYAML

• boto3

• paramiko (SSH)

• fabric

• jinja2 (templates)

• click or typer (CLI tools)

• rich (beautiful terminal output)

• python-dotenv

---

Agar main tumhare liye roadmap banaun to ye hoga:

Python Basics
      │
      ▼
pathlib
      │
      ▼
os
      │
      ▼
shutil
      │
      ▼
subprocess   ⭐⭐⭐⭐⭐
      │
      ▼
json
      │
      ▼
logging
      │
      ▼
datetime
      │
      ▼
argparse
      │
      ▼
requests ⭐⭐⭐⭐⭐
      │
      ▼
PyYAML ⭐⭐⭐⭐⭐
      │
      ▼
Regex
      │
      ▼
Threading
      │
      ▼
boto3
      │
      ▼
Docker Automation
      │
      ▼
Kubernetes Automation

Mere suggestion ke hisaab se next module subprocess hona chahiye.

Reason simple hai: DevOps aur SRE me 80% automation existing command-line tools (kubectl, docker, git, systemctl, helm, terraform, aws) ko automate karne se hi hoti hai. subprocess seekhne ke baad tum Python se almost har CLI tool control kar paoge.

Uske baad sequence rakho:

1. subprocess

2. json

3. logging

4. argparse

5. requests

6. PyYAML

Ye sequence follow karoge to tum production-grade automation scripts likhne ke liye strong foundation bana loge.