Python for DevOps Engineers - Introduction and Setup

Introduction

Presenter: Abishek
Topic: Python for DevOps Engineers
Objectives: Understand why Python is used by DevOps engineers, compare shell scripting and Python scripting, discuss real-time use cases, and demonstrate how to install Python and run the first Python program on Windows, Linux, and MacOS.
Repository: GitHub repository for notes, programs, and updates. Contains complete course content from Day 1 to Day 19.

Common Question: Why should DevOps engineers learn Python?
Typical Concern: Shell scripting vs. Python scripting—important for interactions with Linux systems.
Linux Usage: DevOps engineers frequently deal with Linux systems due to better security and fewer vulnerabilities compared to Windows servers.
Shell Scripting: Used primarily for system administration tasks on Linux. Limited to Linux environments.

Used for automating repetitive tasks in Linux environments.
Example: Creating files, folders, checking disk space or memory, etc.
Syntax: Commands written in a .sh file and executed in sequence.
Interaction with Linux: SS to Linux machines, run shell commands for interactions.
Key Utility: Easy for simple, repetitive, system-level tasks.

Cross-Platform: Works on both Linux and Windows systems.
Complex Tasks: Best suited for complex tasks like interacting with APIs, data manipulation, and advanced data processing.
Module Richness: Python has rich modules for handling JSON, data serialization, etc.
Error Handling: Python provides robust error handling mechanisms.
Example Use Case: Automating GitHub interactions to fetch issue details using GitHub API and JSON.
Serialization and Iteration: Easy in Python for processing big JSON objects.

Fetching Issue Details: From GitHub repositories using APIs and processing the JSON response to extract information like the author of each issue.
Advanced Automation: Writing scripts that interact with various APIs and manage data efficiently.
Lambda Functions: Writing serverless functions in AWS that perform complex interactions with other AWS services like S3.

Using GitHub Codespaces: Provides a cloud-based instance with Python pre-installed. Great for those who cannot install Python locally.
- Steps: Fork the repository -> Open Codespaces -> Choose CPU and RAM -> Use terminal to run Python.
- Command Example: python --version to check the installation.
Local Installation: Windows, Linux, MacOS
- Download from Official Site: Go to python.org and download the relevant version.
- Windows: Download the 64-bit version, run the installer, ensure Python is added to PATH.
- Linux: Use package managers like apt, yum, or brew for installation.
- MacOS: Install using Homebrew (brew install python).
Using Visual Studio Code: Recommended for writing and running Python scripts. Use extensions for better functionality.

Task: Install Python using any of the methods shown, set up Visual Studio Code (if on a local machine), and run the first Python program.
Completion: Ensure Python environment is correctly set up to follow along with future tutorials.

Recap: Importance of Python for DevOps, differences between shell scripting and Python scripting, real-time use cases, and setup instructions.
Next Steps: Continue following the course material in the GitHub repository and complete the given assignment.
Interaction: Leave comments for any queries.

Thank you and see you in the next video!