Which solution meets these requirements with the LEAST operational overhead?
Configure Amazon EMR to read text files from Amazon S3. Run processing scripts to transform the data. Store the resulting JSON file in an Amazon Aurora DB cluster.
Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use Amazon EC2 instances to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.
Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use an AWS Lambda function to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.
Configure Amazon EventBridge (Amazon CloudWatch Events) to send an event to Amazon Kinesis Data Streams when a new file is uploaded. Use an AWS Lambda function to consume the event from the stream and process the data. Store the resulting JSON file in an Amazon Aurora DB cluster.
Explanations:
Amazon EMR is designed for large-scale data processing and would require more operational overhead in terms of setup, maintenance, and cost compared to serverless options. It is not optimal for processing small files quickly after upload, especially with varying demand.
While this option uses Amazon SQS for message queuing, using EC2 instances introduces additional operational overhead in managing and scaling the instances, especially given the variable demand for processing files. This is not the least operational overhead solution.
This option leverages AWS Lambda, which is serverless and scales automatically with demand. It processes files as they are uploaded with minimal operational overhead, and using SQS for event notifications ensures that files are processed quickly without the need for manual management of infrastructure.
Although this solution involves AWS Lambda for processing, using Amazon Kinesis Data Streams adds unnecessary complexity and operational overhead for this use case. Kinesis is generally suited for real-time data streaming, not simple file processing tasks following uploads.
I strategize that the answer is:
Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use an AWS Lambda function to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.
In my experience, the answer is:
Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use an AWS Lambda function to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.