Which solution will meet these requirements with the LEAST operational overhead?
Create an Amazon Kinesis data stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data. Invoke an AWS Lambda function to send the data to the Kinesis Data Analytics application.
Create an Amazon Kinesis data stream to store the data in Amazon S3. Create an Amazon EMR cluster to analyze the data. Invoke an AWS Lambda function to send the data to the EMR cluster.
Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon EMR cluster to analyze the data.
Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data.
Explanations:
Kinesis Data Analytics is typically used for real-time stream processing, but it would require complex setup and operational overhead to manage encryption and storage.
Using an EMR cluster introduces high operational overhead for real-time analysis, which is not the best fit for this use case. EMR clusters are more suited for batch processing.
While Kinesis Data Firehose delivers data to S3 efficiently, using EMR for real-time analytics would add unnecessary complexity and overhead.
Kinesis Data Firehose provides automatic encryption and near-real-time data delivery to S3, and Kinesis Data Analytics is suitable for efficient, low-overhead real-time analysis.
As I recall, the answer is:
Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data.