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AWS Lambda: The Serverless Engine Powering Cloud Automation

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AWS Lambda is one of the most powerful and popular services in the AWS ecosystem. It allows you to run application logic without managing servers, making it ideal for automation, event-driven workflows, and lightweight backend tasks. With Lambda, you simply upload your code and define a trigger—AWS handles provisioning, scaling, and execution automatically.

What Is AWS Lambda?

AWS Lambda is a serverless compute service that executes your code in response to events. It supports languages like Python, Node.js, Java, Go, and more. There is no need to create or manage EC2 servers. Instead, Lambda runs your function in a fully managed environment, scales instantly, and charges you only for the compute time consumed.

Key Characteristics

  • No servers to configure or maintain
  • Automatic scaling from 0 to thousands of requests
  • Pay-per-use billing model
  • Deep integration with AWS services

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Why Is AWS Lambda So Popular?

1. Completely Serverless

You don’t need to provision, patch, or maintain any infrastructure. Lambda removes the operational burden from developers and cloud teams.

2. Automatic, Instant Scaling

Lambda scales based on event load. Whether 1 or 1,000 requests, it adjusts automatically without any configuration.

3. Highly Cost-Effective

Since Lambda charges only when your function executes, there’s no cost for idle time. This makes it ideal for workloads with unpredictable volume.

4. Event-Driven Architecture

Lambda works natively with services like:

  • S3
  • DynamoDB
  • CloudWatch
  • EventBridge
  • SNS / SQS
  • API Gateway

This makes it perfect for automation, integrations, and reactive systems.

5. Multi-Language Support

Lambda supports Python, Node.js, Java, Go, .NET, Ruby, and custom runtimes—giving teams flexibility to choose the best language.

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What Problem Does AWS Lambda Solve?

Traditionally, even small tasks required running dedicated servers—leading to:

  • Costly idle infrastructure
  • Manual scaling
  • OS patching and maintenance
  • Complex automation scripts

Lambda solves this by offering:

  • Serverless compute on demand
  • Automatic resource management
  • Event-based execution
  • Reduced operational workload

It empowers developers to focus purely on logic instead of infrastructure.

Three Real-World Use Cases of AWS Lambda

Below are three practical AWS Lambda use cases (without code), based on your requirement:

1. Automatically Delete Historical Snapshots

Organizations create regular EBS snapshots but often forget to delete old ones, increasing storage cost over time.

How Lambda Helps

  • A scheduled EventBridge rule triggers your Lambda daily.
  • Lambda fetches all EBS snapshots.
  • It identifies snapshots older than a set retention period.
  • It deletes those snapshots automatically.

Benefits

  • Saves storage cost
  • Ensures retention compliance
  • Eliminates manual cleanup 
import boto3

ec2 = boto3.client('ec2')

def lambda_handler(event, context):
    # Describe all snapshots owned by you
    snapshots = ec2.describe_snapshots(OwnerIds=['self'])['Snapshots']

    for snap in snapshots:
        snap_id = snap['SnapshotId']
        print(f"Deleting Snapshot: {snap_id}")
        try:
            ec2.delete_snapshot(SnapshotId=snap_id)
        except Exception as e:
            print(f"Error deleting {snap_id}: {e}")

    return {
        "status": "Completed",
        "deleted_snapshots": len(snapshots)
    }

2. Delete Unused (Available) EBS Volumes

After EC2 instances are terminated, their EBS volumes often remain in the “available” state and keep incurring cost.

How Lambda Helps

  • A scheduled Lambda function lists all EBS volumes.
  • It identifies volumes in "available" state.
  • Lambda deletes these unused volumes.

Benefits

  • Reduces wasted storage cost
  • Improves cloud hygiene
  • Automates cleanup without manual checks 
import boto3

ec2 = boto3.client('ec2')

def lambda_handler(event, context):
    # Find volumes that are 'available' (i.e., NOT attached)
    volumes = ec2.describe_volumes(
        Filters=[{'Name': 'status', 'Values': ['available']}]
    )['Volumes']

    for vol in volumes:
        vol_id = vol['VolumeId']
        print(f"Deleting Unattached Volume: {vol_id}")
        try:
            ec2.delete_volume(VolumeId=vol_id)
        except Exception as e:
            print(f"Error deleting {vol_id}: {e}")

    return {
        "status": "Completed",
        "deleted_volumes": len(volumes)
    }

3. Start EC2 Instances That Are in Stopped State

Some environments require EC2 instances to automatically start during working hours or operational windows and often during patching time.

How Lambda Helps

  • A scheduled trigger (e.g., daily at 9 AM) runs the Lambda function.
  • Lambda checks for EC2 instances in "stopped" state.
  • It starts them automatically based on tag filters or instance IDs.

Benefits

  • Ensures required servers start on time
  • Reduces manual effort
  • Supports DevOps automation 
import boto3

ec2 = boto3.client('ec2')

def lambda_handler(event, context):
    # Find all instances that are stopped
    response = ec2.describe_instances(
        Filters=[
            {'Name': 'instance-state-name', 'Values': ['stopped']}
        ]
    )

    stopped_instances = []

    for reservation in response['Reservations']:
        for instance in reservation['Instances']:
            stopped_instances.append(instance['InstanceId'])

    if stopped_instances:
        print(f"Starting instances: {stopped_instances}")
        ec2.start_instances(InstanceIds=stopped_instances)
    else:
        print("No stopped instances found")

    return {
        "action": "start_instances",
        "instances": stopped_instances
    }

✍️ Author: Omkar Sharma

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