07 Production AWS Lambdas for automation

In this lecture, I’ll explain what this course is really about and why I created it.

• We’ll talk about how AWS Lambda is actually used in real production systems, not just demos.

• I’ll also walk you through the projects we’ll build, how they help in real jobs, and how this course will help you think like a DevOps engineer.

• By the end of this course, you won’t just know Lambda  you’ll know where, why, and how it’s used in real teams

In this section, you’ll see how IAM access keys are checked in real time on a schedule and automatically disabled when they become risky.

We’ll walk through what actually happens when a key crosses the safe age limit, how Lambda takes action without human effort, and the impact this has on account security. You’ll also understand when and why teams decide to disable keys in production to avoid security incidents.

In this section, you’ll see how AWS detects security threats and reacts to them automatically.

We’ll walk through what happens when GuardDuty finds suspicious activity like a third party user trying to login to our instance and if trying to install any malware softwares, how Lambda receives the alert in real time, and how immediate actions are taken to block or limit the threat. You’ll also understand the impact of fast response and why teams rely on automation instead of manual checks in production.

In this section, you’ll learn what really happens when an AWS region goes down and how systems recover without manual help.

We’ll show how health checks detect failures, how Lambda steps in automatically, and how traffic is redirected to a healthy region via route53. You’ll understand the real impact of fast recovery and why teams design systems to heal themselves instead of waiting for human action.

In this section, you’ll see what happens when a file is uploaded to S3 and how actions start automatically.

We’ll walk through how S3 triggers Lambda in real time, how file details are read, and how useful metadata is stored for later use. You’ll understand the impact of this automation and why teams use it to manage large amounts of data without manual work.

In this section, you’ll learn how transactions are checked the moment they come in and how fraud is stopped before money is lost.

We’ll walk through how Lambda reviews each transaction in real time, applies simple risk rules, and blocks suspicious activity instantly. You’ll understand the impact of fast decisions and why teams rely on automation to protect revenue in production systems.

In this video, we build a production-style, event-driven media processing pipeline on AWS using serverless and AI services. This is not a theory demo. This is how you would design and test a real media processing workflow in actual environments. We start from a simple S3 upload and end with automated analysis, metadata storage, and notifications — all without manual intervention. What you’ll learn in this video You’ll see how to design a complete flow where: Uploading a video to S3 automatically triggers processing AWS AI services analyze the media Metadata is stored for later use Notifications are sent when processing completes Bad or low-quality media is detected and handled differently Everything is driven by events, not manual clicks.

Architecture Flow (High Level) S3 Upload → EventBridge → Step Functions → Multiple Lambda Functions → Rekognition / Transcribe / Bedrock → DynamoDB → SNS Notification Hands-on Phases Covered

Phase 1 Create an S3 bucket for raw media uploads and enable EventBridge notifications.

Phase 2 Create a DynamoDB table to store processed media metadata.

Phase 3 Create an SNS topic and subscribe your email to receive processing updates.

Phase 4 Create an IAM role for Lambda with required permissions to access S3, DynamoDB, AI services, and SNS.

Phase 5 Create multiple Lambda functions and configure environment variables.

Phase 6 Build a Step Functions state machine to orchestrate the entire workflow.

Phase 7 Create an EventBridge rule to trigger Step Functions automatically when a video is uploaded.

How to Test This Setup 1. Upload a valid video to the S3 bucket 2. Go to Step Functions and watch the execution 3. Verify the execution completes successfully 4. Check DynamoDB for stored metadata 5. Upload a low-quality or invalid video 6. Observe how the workflow behaves differently

In real AWS environments, unused resources don’t scream for attention — they silently burn money. Old EBS volumes, stopped instances, unused snapshots… all of them slowly increase your monthly bill.

In this video, I show you a real production-style automation to detect idle (zombie) AWS resources and track them automatically  without manual audits. This is not theory. This is how cloud teams actually handle cost hygiene.

What You’ll Build in This Demo An event-driven cleanup detection system that Detects idle EBS volumes Stores idle resource data in DynamoDB Sends alerts using SNS Runs automatically on a schedule Requires zero manual intervention Perfect for DevOps engineers, Cloud engineers, and cost optimization tasks.

? Architecture Overview EventBridge (Scheduled)

↓ AWS Lambda

↓ EC2 / EBS Scan

↓ DynamoDB (ZombieResourceTracker)

↓ SNS (Email Notification)