Python for DevOps

Because bash can only take you so far...

Author

Vishakha Sadhwani
August 28, 2025

In partnership with

Hi Inner Circle,

This week, we’re diving into a topic that keeps coming up across DevOps, SRE, and Platform Engineering roles — Python.

This isn’t about becoming a backend developer. It’s about becoming the kind of engineer who can automate, integrate, and debug faster and smarter using Python.

You might ask — why Python when bash gets the job done?

Here’s the thing: as workflows get more complex — from handling errors and calling APIs to parsing data and integrating tools — Python scales in ways bash simply doesn’t.

In real-world DevOps, Python acts as the glue that connects infrastructure, APIs, tools, and logic — all in one clean, readable script.

In this edition, I’m breaking down:

  • Where Python fits into day-to-day DevOps

  • Key areas to learn, with real interview-style scenarios

  • Trusted resources to start and practice PROJECTS 

Let’s get into it.

Where is it used?

Python shows up in tasks like:

→ Automating cloud provisioning via SDKs (AWS, GCP, Azure)
→ Triggering REST API calls in CI/CD workflows
→ Parsing logs, metrics, and automating alerts
→ Writing internal scripts to eliminate manual work
→ Integrating with Jenkins, Terraform, Prometheus, Kubernetes

And no — you don’t need a CS degree or developer title.
What you do need is intuition (yes, intuition) for where it fits, and how to use it to automate bottlenecks.

Key Areas to Learn — and Why They Matter

So, where do you start? Let's skip the abstract theory and focus on the skills that actually matters.

I've broken it down into six core competencies that map directly to day-to-day DevOps tasks and what hiring managers are looking for.

1. The Basics: Your Foundation

Before you can automate the cloud, you need to know how to build a solid script. This is your foundation.

  • Topics: Variables, conditionals, loops, functions, lists

  • Use case: Writing a cleanup script that loops through files in a directory and removes logs older than 30 days.

  • Interview focus: Can you write readable, working code under pressure? They want to see that you can think logically and structure a simple program.

2. File & OS Automation: The Ground Game

So much of DevOps is about managing files and processes on a server. Your trusty terminal is great, but Python gives you more power and control.

  • Topics: os, shutil, subprocess, open()

  • Use case: A script that automates deployment by copying build artifacts to specific folders, running shell commands to restart services, and logging the output.

  • Interview focus: How would you write a script to check if a service is running, restart it if it has failed, and log the result to a file?

3. REST API Automation: Connecting Your Tools

Modern infrastructure is a collection of services that talk to each other via APIs. Being able to script these interactions is non-negotiable.

  • Topics: requests, handling headers, auth tokens, parsing JSON

  • Use case: Polling the GitHub Actions API to check the status of a build or writing a script that triggers custom alerts to a Slack channel when a deployment completes.

  • Interview focus: Can you authenticate with an API (like a CI/CD tool), fetch the deployment logs for a specific job, and print out only the error messages?

4. Cloud SDKs: Speaking the Cloud's Language

Whether you're on AWS, Azure, or GCP, the official Software Development Kit (SDK) is your key to unlocking powerful cloud automation. This is where you move from clicking in the console to managing infrastructure with code.

  • Topics: boto3 (AWS), Azure SDK, Google Cloud SDK

  • Use case: Starting or stopping a fleet of EC2 instances based on tags, checking the status of S3 buckets, or automatically provisioning a new persistent disk for a VM.

  • Interview focus: Can you write a script that finds all EC2 instances with the tag env:dev and stops them to save costs over the weekend?

5. CI/CD Integration: The Pipeline Power-Up

Your CI/CD pipeline isn't just for running canned commands. You can embed custom Python scripts right into your pipeline steps to perform advanced logic that simple YAML can't handle.

  • Topics: Using Python in Jenkins (with shared libraries), GitHub Actions, or GitLab CI

  • Use case: A Python script in a pipeline that runs pre-deployment checks (like ensuring a database is accessible), pushes a detailed notification to Microsoft Teams, or validates that infrastructure changes from a Terraform plan are safe to apply.

  • Interview focus: Have you ever written a Python script that runs inside a pipeline step? Describe what it did and why you used Python for it.

6. Monitoring & Logging: Finding the Signal in the Noise 📊

Your systems generate a massive amount of data. Python is perfect for parsing logs, collecting custom metrics, and building automated responses to system events.

  • Topics: Parsing log files, integrating with monitoring tool APIs (like Datadog or Prometheus)

  • Use case: A script that tails an application log, looks for a specific error pattern, and sends an alert. Another common one is detecting high CPU usage from a monitoring API and logging which processes are responsible.

  • Interview focus: Can you write a script that runs every 30 minutes, checks the disk space and memory usage on a server, and logs the results with a timestamp?

Where to Start —

For Learning Python:

For Cloud Automation:

Beginner Friendly Projects (leveraging AI)

Up for a challenge?

Try these advanced projects:

Final Thoughts

You don’t need to master Python like a full-time software engineer.

But knowing how to write just a few good scripts? That’ll make you 10x more effective in any Cloud or DevOps role.

Start small. Automate whatever is causing you or your team a mental breakdown:)

Keep going ~ it compounds faster than you think.

— Vishakha

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