What is the recommended convention for storing images in a serverless event-driven architecture? Base64 image in SNS event exceeds message size limit.

The recommended approach for handling image uploads in a serverless event-driven environment on AWS is to separate the image storage from the event messaging. Instead of including the entire image as a Base64 string in your event payload, you should follow these best practices:

1. Upload the image directly to Amazon S3: When a user uploads an image, store it directly in an S3 bucket. S3 is designed for object storage and can handle large files efficiently.

2. Use event notifications: Configure S3 to trigger an event when a new object is created or updated in the bucket. This event can be sent to AWS Lambda, Amazon SNS, or Amazon SQS.

3. Include metadata in the event: Instead of including the entire image in the event payload, include only the necessary metadata such as the S3 object key, bucket name, and any other relevant information. This keeps the event size small and within the limits of services like SNS (256 KB) or Lambda (6 MB for synchronous invocations).

4. Process images using Lambda: Use a Lambda function to handle the S3 event. This function can perform tasks such as image analysis, resizing, or metadata extraction. It can then store the processed data or metadata in a database like Amazon DynamoDB.

5. Consider using the Serverless Image Handler: AWS provides a solution called the Serverless Image Handler, which automatically processes images uploaded to S3. It uses Amazon Rekognition for image analysis and can apply transformations like resizing and thumbnail creation.

6. Use Amazon CloudFront for delivery: If you need to serve these images to end-users, consider using Amazon CloudFront as a content delivery network. It can work with the Serverless Image Handler to provide dynamic image transformations on-demand.

For large data in event-driven architectures, AWS recommends:

1. Use S3 for large file storage and include only the file reference in your events.
2. Utilize S3 event notifications to trigger processing workflows.
3. Consider using Amazon Kinesis Data Streams for high-volume data ingestion and processing.
4. Implement data compression techniques when appropriate to reduce payload sizes.

By following these practices, you can build a scalable, efficient serverless architecture for image processing that adheres to AWS service limits and best practices.
Sources
Serverless Image Handler
Amazon DynamoDB FAQs | NoSQL Key-Value Database | Amazon Web Services
Dynamic Image Transformation for Amazon CloudFront | AWS Solutions | AWS Solutions Library
Community | Building a Serverless Image Recognition Website with Machine Learning
Optimizing network footprint in serverless applications | AWS Compute Blog