How should the company move the data to Amazon S3 to meet these requirements?
Use an Amazon CloudWatch metric stream to send the EC2 Auto Scaling status data to Amazon Kinesis Data Firehose. Store the data in Amazon S3.
Launch an Amazon EMR cluster to collect the EC2 Auto Scaling status data and send the data to Amazon Kinesis Data Firehose. Store the data in Amazon S3.
Create an Amazon EventBridge (Amazon CloudWatch Events) rule to invoke an AWS Lambda function on a schedule. Configure the Lambda function to send the EC2 Auto Scaling status data directly to Amazon S3.
Use a bootstrap script during the launch of an EC2 instance to install Amazon Kinesis Agent. Configure Kinesis Agent to collect the EC2 Auto Scaling status data and send the data to Amazon Kinesis Data Firehose. Store the data in Amazon S3.
Explanations:
Using Amazon CloudWatch metric streams allows for near-real-time reporting of EC2 Auto Scaling events. By sending this data to Amazon Kinesis Data Firehose, it can be efficiently stored in Amazon S3 without impacting the launch speed of EC2 instances. This approach is serverless and leverages AWS services designed for scalable data ingestion.
Launching an Amazon EMR cluster introduces unnecessary complexity and resource management overhead. EMR is not a serverless solution and would likely add latency to data processing, potentially impacting the speed of EC2 instance launches. Additionally, EMR is not designed for real-time event reporting in this context.
Invoking a Lambda function on a schedule does not provide real-time updates and may not capture all EC2 Auto Scaling events as they occur. This method relies on periodic execution rather than event-driven architecture, which is less efficient for near-real-time reporting.
Using a bootstrap script to install the Kinesis Agent on each EC2 instance introduces additional setup and management overhead. This method is not serverless and can delay instance launches due to the time taken to install and configure the agent, negatively impacting performance.