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Every developer has their own ecosystem: some use Bash, some live inside VSCode terminals, others flip between JavaScript, Docker, Makefiles, and Jenkins. It’s chaos—but productive chaos.
I used to juggle a bunch of tools too. Until I realized something wild:
Let me explain why this works, and how you can do it too.
? Problem: Too Many Little Tools
Modern dev workflows involve:
- Bash scripts for CLI automation
- JSON/YAML tools for config munging
- awk, sed, jq for data surgery
- Makefiles or npm scripts for building
- Cron jobs or CI pipelines for scheduling
- Docker/CLI tools for deployment
None of these tools talk to each other well. They're often brittle, verbose, and hard to debug. Plus, switching mental contexts between them wastes time.
So I asked myself: what if I used Python as the universal bridge between all these tools?
? The Big Shift: Python as Dev Glue
Think of Python as a multi-lingual middleman. Here's how I use it:
| Problem | Old Tool | Python Replacement |
|---|---|---|
| Auto-build system | Makefile | build.py |
| Scrape GitHub API | curl + jq | requests + json |
| Parse .env files | Bash | python-dotenv |
| YAML → JSON | yq | pyyaml + json |
| Cron jobs | crontab + script | schedule + threading |
I still invoke Bash or Docker when needed—but Python becomes the controller, the orchestrator, the brain.
? Core Benefits of Using Python as Glue
1. Cross-Platform without Trying
Your Bash scripts break on Windows. Your Docker setups break on Alpine. But Python? Write once, run (nearly) anywhere.
2. Access to Rich Libraries
You want to compress a folder? Send an email? Trigger a Slack webhook? There’s a library for that.
3. Unit Testing for DevOps
I now write unit tests for my build pipelines. Bash can’t do that. Python can:
import unittest
class TestBuild(unittest.TestCase):
def test_version_file(self):
with open("VERSION") as f:
self.assertRegex(f.read(), r"\d+\.\d+\.\d+")
4. Inline CLI Tool Wrapping
Want to wrap docker ps?
import subprocess
print(subprocess.run(["docker", "ps"], capture_output=True).stdout.decode())
Now you can transform output, parse it, and react—all in Python.
Real Use Case: Build System ReplacementOld Makefile:
build:
docker build -t myapp .
test $(curl -s ) != ""
New build.py:
import subprocess, requests
def docker_build():
subprocess.run(["docker", "build", "-t", "myapp", "."], check=True)
def verify_public_ip():
ip = requests.get("").text
assert ip.strip() != ""
docker_build()
verify_public_ip()
Cleaner. Reusable. Testable. IDE-friendly.
? Unified Format Conversion Tool
One of my favorite side-effects of this approach: I built a single Python CLI tool that does:
- YAML → JSON
- CSV → Dict
- Markdown → HTML
- TOML → JSON
- JSON → YAML
python convert.py config.yaml --to json
Internally, it just uses:
import yaml, json
print(json.dumps(yaml.safe_load(open("config.yaml")), indent=2))
The days of installing jq, yq, xmlstarlet, csvkit? Gone.
? Bonus: Python Makes Debugging Devops Workflows Fun
Ever tried debugging a 70-line Bash script that silently fails when a file is missing?
Now I get:
FileNotFoundError: [Errno 2] No such file or directory: 'app.log'
Debugging feels like dev again—not archaeology.
? Final Takeaway
If you're a:
- Backend dev managing scripts
- DevOps engineer juggling JSON, YAML, and Bash
- Hobbyist building tools and automations
You can treat Python as your personal operating system, unifying:
- File ops
- CLI interactions
- Web APIs
- Config munging
- Task scheduling
All in one language. All with testability, portability, and sanity.
? My Python DevOps Stack
| Task | Python Tool |
|---|---|
| CLI Parsing | argparse, typer |
| Scheduling | schedule, APScheduler |
| API calls | requests, httpx |
| File IO | os, pathlib, shutil |
| Format Conversion | json, yaml, toml, csv, markdown2 |
| CLI Output | rich, click, tabulate |
| Testing | unittest, pytest |
- ? Me writing another Makefile
- ? Me realizing I can just use Python
- ? Python saving me from shell hell
- ? When I delete 40 Bash scripts
- ? When my cronjob has unit tests
Have you replaced shell scripts, Makefiles, or CI steps with Python too?
Drop your favorite glue.py snippets in the comments. Let’s build a library of mini automations. ?
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