Summary of "Day-7 | Live AWS Project using SHELL SCRIPTING for DevOps | AWS DevOps project| #devops #aws #2023"

Video Title:

Day-7 | Live AWS Project using Shell Scripting for DevOps | AWS DevOps project| #devops #aws #2023

Summary of Technological Concepts, Product Features, and Analysis

  1. Context & Purpose:
    • The video is part of a full DevOps course (Day 7) focusing on a real-time AWS project using Shell Scripting.
    • The project simulates a common DevOps task: tracking AWS resource usage to maintain cost-effectiveness and manageability on cloud infrastructure.
  2. Why Move to Cloud?
    • Two primary reasons:
      1. Reduce maintenance overhead (no need to manage physical servers).
      2. Cost-effectiveness via pay-as-you-go pricing.
    • Organizations need to track resource usage to avoid paying for unused resources (e.g., unused EC2 instances or unattached EBS volumes).
  3. Project Objective:
  4. Implementation Approach:
    • Use AWS CLI commands within a Bash shell script to query AWS resources.
    • The script outputs resource details and can be scheduled to run automatically using a Cron job (Linux scheduler), ensuring daily reports without manual intervention.
    • The example organization is “example.com,” and the demo uses a real AWS account.
  5. Key AWS CLI Commands Used:
  6. Shell Scripting Best Practices:
    • Use shebang (#!/bin/bash) for bash scripting to avoid compatibility issues with other shells like dash.
    • Add comments at the top of the script for author info, date, version, and purpose.
    • Add comments before commands for clarity and maintainability.
    • Use echo statements to print descriptive messages for better readability of output.
    • Use chmod to set executable permissions on the script.
  7. Output Handling and Debugging:
    • Redirect output to files for easier reading (| more, or output redirection).
    • Use set -x to enable debug mode, which prints each command before execution for troubleshooting.
  8. Improving Output with JSON Parsing:
    • AWS CLI outputs JSON data, which can be verbose.
    • Use jq, a JSON parser tool, to extract specific fields (e.g., instance IDs) to simplify and clarify the output.
    • Example: 
      aws ec2 describe-instances | jq '.Reservations[].Instances[].InstanceId'
    • This approach filters out unnecessary details and presents concise information.
  9. Scheduling with Cron job:
    • Automate script execution daily at a fixed time (e.g., 6 PM) using cron.
    • Cron jobs run scripts automatically without manual login or intervention, ensuring timely reports.
  10. Further Improvements & Assignments:
    • The instructor encourages viewers to enhance the script by integrating it with cron and improving modularity (e.g., using shell functions).
    • Future videos will cover more advanced Shell Scripting projects.

Key Takeaways / Guide Steps for the Project

  1. Setup:
    • Install and configure AWS CLI (aws configure with access key, secret key, region, and output format).
    • Ensure bash shell environment and jq installed.
  2. Script Development:
    • Start with a bash script with proper shebang.
    • Write commands to list AWS resources using AWS CLI.
    • Add echo statements for clarity.
    • Use jq to parse JSON output and simplify reports.
  3. Execution & Debugging:
    • Make script executable (chmod +x script.sh).
    • Run script and verify output.
    • Use set -x for debugging if needed.
  4. Automation:
    • Create a Cron job to schedule the script daily at a specific time.
  5. Output Management:
    • Redirect script output to a file for reporting purposes.

Main Speaker / Source

This video serves as a practical tutorial for DevOps engineers or AWS administrators to automate AWS resource usage tracking using Shell Scripting and AWS CLI, emphasizing cost control and automation best practices.

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