Scheduling data clean-up jobs using Lambda

 In today’s cloud-native applications, data clean-up tasks—such as deleting old records, archiving logs, or clearing temporary files—are essential for maintaining optimal performance, reducing costs, and complying with data retention policies. AWS offers a serverless and cost-effective way to automate these tasks using AWS Lambda in combination with Amazon EventBridge (formerly CloudWatch Events).

This blog will guide you through how to schedule data clean-up jobs using AWS Lambda, explaining the architecture, implementation, and best practices.

☁️ What Is AWS Lambda?

AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. You simply upload your code and Lambda takes care of everything required to run and scale it. It’s ideal for short-lived, periodic tasks like clean-up jobs.

🔁 Common Use Cases for Scheduled Data Clean-Up

• Deleting expired user sessions from a database
• Archiving logs to Amazon S3 and deleting older entries
• Cleaning up unused files from Amazon S3 buckets
• Purging stale records from Amazon DynamoDB or RDS
• Removing old entries from CloudWatch Logs

🛠️ Step-by-Step: Scheduling Clean-Up Jobs with Lambda

Step 1: Create a Lambda Function

Go to the AWS Lambda Console.

• Click Create Function and choose Author from scratch.
• Give it a name like data-cleanup-job.
• Choose the appropriate runtime (e.g., Python, Node.js, or Java).

Define the function logic to perform clean-up tasks. Example (Python for DynamoDB):

python

import boto3

from datetime import datetime, timedelta

def lambda_handler(event, context):

    dynamodb = boto3.resource('dynamodb')

    table = dynamodb.Table('your-table-name')

    threshold_date = (datetime.utcnow() - timedelta(days=30)).isoformat()

    response = table.scan()

    for item in response['Items']:

        if item['timestamp'] < threshold_date:

            table.delete_item(Key={'id': item['id']})

    return "Clean-up completed."

Step 2: Set Up IAM Permissions

Ensure your Lambda function has the necessary permissions to access services like DynamoDB, S3, or RDS. Attach an IAM role with the required policies (e.g., AmazonDynamoDBFullAccess).

Step 3: Schedule the Job with EventBridge

Go to the Amazon EventBridge Console.

Create a new Rule:

Name: daily-data-cleanup

Schedule: Use a cron expression or rate expression (e.g., rate(1 day) or cron(0 2 * * ? *) for 2 AM UTC daily).

Set the target as your Lambda function.

Save the rule.

Now, your Lambda function will run at the defined interval automatically.

✅ Best Practices

Use Environment Variables to configure time thresholds and resource names.

Enable Logging in CloudWatch to monitor execution.

Add Alarms for failures using CloudWatch Alarms or AWS SNS.

Test Locally before scheduling, and run manual invocations to verify behavior.

Set a Timeout and Memory Limit in Lambda that matches your task size.

📌 Conclusion

Automating data clean-up tasks using AWS Lambda and EventBridge is a scalable, efficient, and cost-effective way to maintain cloud hygiene. Whether you’re deleting old records, cleaning up S3 buckets, or archiving logs, this serverless setup requires minimal management and can easily adapt to your evolving needs. Start small, monitor thoroughly, and gradually automate more tasks for a cleaner and smarter infrastructure.

Learn AWS Data Engineer Training
Read More: How to apply row-level security in Redshift

Visit IHUB Training Institute Hyderabad
Get Direction