This tutorial guides you through building a serverless image processing pipeline using AWS services. We'll create a Next.js frontend deployed on an ECS Fargate cluster, interacting with an API Gateway, Lambda functions, S3 buckets, and DynamoDB.

This project enhances your understanding of serverless architecture and AWS service integration. Remember: this is a demo; production requires enhanced security and configuration.

Prerequisites:

• An AWS account with an IAM user possessing necessary permissions.
• Familiarity with AWS services (S3, API Gateway, Lambda, DynamoDB, ECS, ECR).
• Node.js installed. The GitHub repository provides code examples.

AWS Service Setup:

1. Create S3 Buckets: Two S3 buckets are needed: sample-image-uploads-bucket (general purpose) and sample-thumbnails-bucket (with public read access disabled initially).

2. Configure sample-thumbnails-bucket Permissions: Grant public read access to this bucket using the following policy:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "PublicRead",
      "Effect": "Allow",
      "Principal": "*",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::sample-thumbnails-bucket/*"
    }
  ]
}

3. Create DynamoDB Table: Create a DynamoDB table named image_metadata with a string primary key image_id.

4. Create IAM Role: Create an IAM role (Lambda-Image-Processor-Role) for Lambda functions, granting AmazonS3FullAccess, AmazonDynamoDBFullAccess, and CloudWatchLogsFullAccess permissions.

Lambda Functions:

1. image-processor Function: This function processes uploaded images, creates thumbnails, and stores metadata in DynamoDB. It uses the Pillow library, requiring a Lambda layer (arn:aws:lambda:us-east-1:770693421928:layer:Klayers-p39-pillow:1 - remember to adjust the region). The function is triggered by S3 PUT events.

2. get-image-metadata Function: This function retrieves image metadata from DynamoDB for the frontend.

API Gateway:

Create an HTTP API Gateway (image-gallery-api) integrating with the get-image-metadata Lambda function. The GET /images endpoint will be used by the frontend.

Testing: Upload an image to sample-image-uploads-bucket. The Lambda function should process it, and the API Gateway should return the metadata.

Frontend (Next.js):

Create a Next.js app, fetch image data from the API Gateway, and display thumbnails. Remember to update next.config.mjs with the correct image domain.

Docker and ECS Deployment:

1. Create a Dockerfile: Containerize the Next.js app.

2. Push to ECR: Push the Docker image to Amazon ECR.

3. Create ECS Cluster and Task Definition: Create an ECS Fargate cluster and a task definition using the ECR image. Ensure the task role has necessary ECR permissions. Create an IAM role for the ECS task execution.

4. Create ECS Service: Create an ECS service to run the container.

5. Access the Application: Access your deployed Next.js application via the public IP of the ECS task.

Conclusion: This comprehensive tutorial demonstrated building a fully functional serverless image processing pipeline on AWS. Remember to implement robust security and error handling for production deployments. Consider using CloudFront for improved S3 access control and optimizing DynamoDB queries for scalability.

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