Efficient data processing with AWS Lambda and DynamoDB: Handling timeouts and scalability issues

I am building a serverless application using AWS Lambda and DynamoDB. The application processes a large amount of incoming data, performs some transformations, and then stores the results back into DynamoDB. However, I am encountering issues with Lambda function timeouts and scalability as the data volume increases. Specifically, the Lambda functions often exceed the maximum execution time, and the DynamoDB read/write capacity units are sometimes insufficient to handle the load.

I tried increasing the memory allocation for the Lambda functions and optimizing the code to reduce execution time, but this only provided a marginal improvement. I also set up DynamoDB Auto Scaling, but it still struggles to keep up with sudden spikes in traffic. I expected the Lambda functions to complete within their time limits and DynamoDB to scale automatically to handle the increased load.

I have a few suggestions to help you achieve your goal:

1. Use DynamoDB on-demand mode, that will allow it to handle bursts in traffic
2. Recreate your Lambda Batch Size, too many items on the same function will cause it to exceed the time limit
3. Increase your Lambda parallelization factor to 10, that'll give you 10X improvment in performance.