This content originally appeared on DEV Community and was authored by Kachi
If you're working with AWS and Python, Boto3 is your best friend! It’s the official AWS SDK for Python, allowing you to interact with AWS services programmatically.
In this guide, we’ll cover:
✅ What is Boto3?
✅ Installation & Setup
✅ Basic Boto3 Operations
✅ Common Use Cases
✅ Best Practices & Tips
🤔 What is Boto3?
Boto3 is the AWS SDK for Python, enabling developers to:
- Create, configure, and manage AWS services (EC2, S3, Lambda, DynamoDB, etc.)
- Automate cloud workflows (deployments, backups, scaling)
- Integrate AWS into Python apps (serverless, data pipelines, DevOps)
It provides two API layers:
- Low-level (Client API) – Direct AWS service calls (raw responses)
- High-level (Resource API) – Pythonic, object-oriented interface
⚙️ Installation & Setup
1. Install Boto3
pip install boto3
2. Configure AWS Credentials
Boto3 needs AWS credentials. You can set them up via:
- AWS CLI (Recommended)
aws configure
- Environment Variables
export AWS_ACCESS_KEY_ID="YOUR_ACCESS_KEY"
export AWS_SECRET_ACCESS_KEY="YOUR_SECRET_KEY"
export AWS_DEFAULT_REGION="us-east-1"
- Hardcoded (Not Recommended for Prod)
import boto3
client = boto3.client(
's3',
aws_access_key_id='YOUR_KEY',
aws_secret_access_key='YOUR_SECRET',
region_name='us-east-1'
)
🔧 Basic Boto3 Operations
1. Listing S3 Buckets
import boto3
s3 = boto3.client('s3')
response = s3.list_buckets()
for bucket in response['Buckets']:
print(bucket['Name'])
2. Launching an EC2 Instance
ec2 = boto3.client('ec2')
response = ec2.run_instances(
ImageId='ami-0abcdef1234567890', # Amazon Linux AMI
InstanceType='t2.micro',
MinCount=1,
MaxCount=1
)
print(response['Instances'][0]['InstanceId'])
3. Invoking a Lambda Function
lambda_client = boto3.client('lambda')
response = lambda_client.invoke(
FunctionName='my-lambda-function',
Payload='{"key": "value"}'
)
print(response['Payload'].read().decode('utf-8'))
🚀 Common Use Cases
1. Automating S3 File Uploads
s3 = boto3.client('s3')
s3.upload_file('local_file.txt', 'my-bucket', 'remote_file.txt')
2. Querying DynamoDB
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('Users')
response = table.get_item(Key={'user_id': '123'})
print(response['Item'])
3. Managing CloudWatch Logs
logs = boto3.client('logs')
response = logs.filter_log_events(
logGroupName='/aws/lambda/my-function',
limit=10
)
for event in response['events']:
print(event['message'])
💡 Best Practices & Tips
✅ Use IAM Roles for EC2/Lambda (Avoid hardcoding keys)
✅ Reuse Boto3 Clients (They’re thread-safe)
✅ Enable Pagination for Large Responses
paginator = s3.get_paginator('list_objects_v2')
for page in paginator.paginate(Bucket='my-bucket'):
for obj in page['Contents']:
print(obj['Key'])
✅ Handle Errors Gracefully
try:
s3.get_object(Bucket='my-bucket', Key='nonexistent.txt')
except s3.exceptions.NoSuchKey:
print("File not found!")
✅ Use Boto3 Sessions for Multi-Account Access
session = boto3.Session(profile_name='dev-profile')
s3 = session.client('s3')
📌 Conclusion
Boto3 is a powerful tool for AWS automation and cloud management in Python. Whether you're:
- Deploying serverless apps
- Managing infrastructure
- Building data pipelines
…Boto3 makes it easy and efficient.
🔹 Got questions? Drop them in the comments! 👇
AWS #Python #Boto3 #CloudComputing #DevOps
This content originally appeared on DEV Community and was authored by Kachi
Kachi | Sciencx (2025-04-21T23:00:00+00:00) Getting Started with Boto3: The AWS SDK for Python. Retrieved from https://www.scien.cx/2025/04/21/getting-started-with-boto3-the-aws-sdk-for-python/
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