AWS Automation Using Python Boto3 & AWS CLI

Objective

To understand and practice AWS automation using Python (Boto3) and AWS CLI, including:

• Setting up Python and required packages

• Configuring AWS permissions

• Accessing AWS services using IAM Role and IAM User

• Automating EC2 and S3 operations using Python scripts

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Prerequisites

• AWS Account

• EC2 Instance (Amazon Linux)

• IAM access (Role/User)

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Environment Details

• OS: Amazon Linux

• Python Version: Python 3

• AWS Services Used: EC2, S3, IAM

• Libraries: boto3, AWS CLI

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Step 1: Python Installation & Verification

Installed Python 3 using yum package manager:

• Verified Python location

• Installed Python 3

• Checked Python version

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Step 2: Installing pip and boto3

• Verified pip availability

• Installed boto3 using pip

• Validated installation without entering Python shell

Commands used:

• python3 -c "import boto3"

No errors confirmed that boto3 was installed successfully.

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Step 3: AWS Permissions Setup

Option 1: EC2 Instance (IAM Role)

• Created an IAM Role for EC2 service

• Attached required policies:

  • EC2 Full Access

  • S3 Full Access

  • RDS Full Access (if needed)

• Attached the role to EC2 instance

This allowed direct AWS access without using access keys.

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Option 2: Local Machine (IAM User)

• Created IAM User with programmatic access

• Generated Access Key & Secret Key

• Attached service-specific policies

• Configured AWS CLI using:

aws configure

Entered:

• Access Key

• Secret Key

• Region

• Output format

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Step 4: AWS CLI Validation

Tested AWS access using CLI commands:

• Listed EC2 instances

• Listed S3 buckets

Commands:

• aws ec2 describe-instances

• aws s3 ls

Successful output confirmed proper IAM permissions and configuration.

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Step 5: Python Script – List EC2 Instances

Created a Python script using boto3 to:

• Connect to EC2 service

• Fetch EC2 instance details

• Print raw response for understanding data structure

Observed:

• Response returned as a dictionary

• Instances grouped under Reservations

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Step 6: Python Script – Start Stopped EC2 Instances

Enhanced the script to:

• Iterate through EC2 instances

• Identify instances in stopped state

• Collect instance IDs

• Start stopped instances automatically

Handled edge case:

• If no stopped instances found, script exits safely

This helped in understanding:

• Nested dictionary parsing

• Conditional automation logic

• Real-world AWS automation use case

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Step 7: Python Script – List S3 Buckets

Created another Python script to:

• Connect to S3 service

• Fetch list of all buckets

• Print bucket names one by one

Observed:

• Buckets returned as a list of dictionaries

• Each bucket contains metadata like name and creation date

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Key Learnings

• Difference between IAM Role vs IAM User

• How AWS CLI and boto3 use the same credentials

• Understanding AWS API responses (dict & list structure)

• Writing safe automation scripts with validations

• Real-world AWS automation using Python

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Conclusion

This lab provided hands-on experience with AWS automation using Python boto3 and AWS CLI.
It strengthened my understanding of AWS permissions, scripting logic, and service-level automation, which is essential for real-world cloud and DevOps tasks.