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.