Answer: Use AWS Lambda with a Python script

## Explanation **Why AWS Lambda is the most cost-effective solution:** 1. **Small data size**: Each mattress generates only 2 MB of data per night, which is a very small amount of data. 2. **Short execution time**: Processing finishes within 30 seconds, which is well within Lambda's 15-minute maximum execution time. 3. **Memory requirement**: 1 GB of memory is easily supported by Lambda (up to 10 GB). 4. **Event-driven processing**: Lambda can be triggered automatically when new data arrives in S3. 5. **Cost efficiency**: - Lambda charges per millisecond of execution time and memory used - With 2 MB data and 30-second processing time, the cost would be extremely low - No need to pay for idle resources (serverless) - No cluster management overhead **Why other options are less cost-effective:** - **AWS Glue (A & D)**: Glue is designed for ETL jobs on larger datasets and has a minimum billing duration of 1 minute plus DPU costs. For small, frequent jobs, Lambda is more cost-effective. - **Amazon EMR (B)**: EMR is for big data processing on large clusters and has significant overhead for cluster setup/teardown. It's overkill for 2 MB of data. **Key considerations:** - Lambda's pay-per-use model is ideal for small, frequent processing tasks - S3 event notifications can trigger Lambda automatically - Python script in Lambda can easily process and summarize the data - Results can be written back to S3 or stored in a database for immediate availability This solution provides the fastest processing with minimal cost, meeting the requirement for results to be "available as soon as possible" while being the most cost-effective option.

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