aws-lambda-s3-image-optimizer

This project is a fully automated serverless architecture example that detects images uploaded to AWS S3 and automatically reduces their size by 50% using the Pillow library.

It demonstrates Infrastructure as Code (IaC) using Terraform and an event-driven workflow powered by AWS Lambda.

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Architecture

graph LR
    User(["👤 User"])

    subgraph AWS ["☁️ AWS Cloud"]

        subgraph S3In ["S3 Source Bucket"]
            Raw["proje-raw-images"]
        end

        subgraph Lambda ["AWS Lambda — Python 3.9"]
            Trigger["S3 ObjectCreated\nEvent Trigger"]
            Func["lambda_function.py\nImage Processing"]
            Layer["Pillow Layer\n(Klayers)\nresize → 50%"]
        end

        subgraph S3Out ["S3 Destination Bucket"]
            Opt["proje-optimized-images"]
        end

        CW["CloudWatch\nLogs"]
        IAM["IAM Role\nS3 Read + Write"]

    end

    User -->|"upload image"| Raw
    Raw -->|"ObjectCreated event"| Trigger
    Trigger --> Func
    Func --> Layer
    Layer -->|"save optimized image"| Opt
    Func -->|"execution logs"| CW
    IAM -. "permissions" .-> Func

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Architecture Features

Infrastructure as Code (IaC): All AWS resources (S3, IAM, Lambda) are managed using Terraform.

Event-Driven: The system runs in real time using the S3 ObjectCreated trigger.

Serverless: No server management is required; you only pay when execution happens.

Scalable: Thanks to AWS Lambda, it can process hundreds of images simultaneously.

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🚀 Technologies Used

• Language: Python 3.9
• Infrastructure: Terraform
• AWS Services: Lambda, S3, IAM, CloudWatch
• Libraries: Pillow (PIL), Boto3

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📂 Project Structure

• main.tf — Defines AWS resources such as S3 buckets, IAM roles, and Lambda configuration.
• lambda_function.py — Contains the image processing logic written in Python.
• Layers (Klayers) — Lambda layer integration for the Pillow library.

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⚙️ Setup and Deployment

Initialize Terraform:

terraform init

Create the infrastructure on AWS:

terraform apply

Upload an image to the proje-raw-images bucket and check the proje-optimized-images bucket!

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