AWS Lambda runs your Python code in response to events. You upload a function, configure triggers, and AWS handles everything else — scaling, patching, monitoring.

Your First Lambda Function 

  # lambda_function.py
import json

def lambda_handler(event, context):
    name = event.get('name', 'World')
    return {
        'statusCode': 200,
        'headers': {'Content-Type': 'application/json'},
        'body': json.dumps({'message': f'Hello, {name}!'}),
    }

Deploy with AWS CLI 

  # Package the function
zip function.zip lambda_function.py

# Create the function
aws lambda create-function \
    --function-name hello-python \
    --runtime python3.12 \
    --handler lambda_function.lambda_handler \
    --role arn:aws:iam::ACCOUNT:role/lambda-execution-role \
    --zip-file fileb://function.zip

# Invoke it
aws lambda invoke \
    --function-name hello-python \
    --payload '{"name": "Alice"}' \
    response.json

cat response.json

API Gateway Integration

Expose your Lambda as a REST API:

  def lambda_handler(event, context):
    http_method = event['httpMethod']
    path = event['path']

    if http_method == 'GET' and path == '/items':
        return {
            'statusCode': 200,
            'body': json.dumps([{'id': 1, 'name': 'Item 1'}]),
        }

    if http_method == 'POST' and path == '/items':
        body = json.loads(event['body'])
        return {
            'statusCode': 201,
            'body': json.dumps({'id': 2, 'name': body['name']}),
        }

    return {'statusCode': 404, 'body': json.dumps({'error': 'Not found'})}

Environment Variables 

  import os

DATABASE_URL = os.environ['DATABASE_URL']
API_KEY = os.environ['API_KEY']

def lambda_handler(event, context):
    # Use environment variables for configuration
    ...

Set via CLI:

  aws lambda update-function-configuration \
    --function-name hello-python \
    --environment "Variables={DATABASE_URL=postgres://...,API_KEY=secret}"

Lambda Layers

Share dependencies across functions:

  # Build a layer with dependencies
mkdir -p python/lib/python3.12/site-packages
pip install requests boto3 -t python/lib/python3.12/site-packages/
zip -r layer.zip python

aws lambda publish-layer-version \
    --layer-name python-deps \
    --zip-file fileb://layer.zip \
    --compatible-runtimes python3.12

Event Sources

Trigger Use Case
API Gateway REST/HTTP APIs
S3 File upload processing
SQS Message queue processing
DynamoDB Streams Database change reactions
EventBridge Scheduled tasks (cron)
SNS Notification processing

S3 Trigger Example 

  import boto3
import json

s3 = boto3.client('s3')

def lambda_handler(event, context):
    for record in event['Records']:
        bucket = record['s3']['bucket']['name']
        key = record['s3']['object']['key']
        print(f"Processing {key} from {bucket}")

        response = s3.get_object(Bucket=bucket, Key=key)
        content = response['Body'].read().decode('utf-8')
        # Process the file...

Best Practices

  1. Keep functions small and focused — one responsibility per function
  2. Use environment variables for configuration, not hardcoded values
  3. Set appropriate timeouts — default is 3 seconds, max is 15 minutes
  4. Use Lambda Layers for shared dependencies to reduce deployment package size
  5. Enable X-Ray tracing for debugging distributed applications
  6. Handle cold starts — keep packages lean, use provisioned concurrency for latency-sensitive apps

AWS Lambda is the most mature serverless platform with the largest ecosystem of integrations.