How can the company meet these requirements with the LEAST amount of effort?
Activate S3 server access logging. Import the access logs into an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.
Activate S3 server access logging. Use Amazon Athena to create an external table with the log files. Use Athena to create a SQL query to analyze the access patterns.
Invoke an AWS Lambda function for every S3 object access event. Configure the Lambda function to write the file access information, such as user, S3 bucket, and file key, to an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.
Record an Amazon CloudWatch Logs log message for every S3 object access event. Configure a CloudWatch Logs log stream to write the file access information such as user. S3 bucket, and file key, to an Amazon Kinesis Data Analytics for SQL application. Perform a sliding window analysis.
Explanations:
S3 server access logging provides access logs, but importing these logs into Amazon Aurora and using SQL queries to analyze the access patterns requires additional setup, management, and effort. Athena would be more suitable for querying logs.
Activating S3 server access logging and using Amazon Athena to query the logs directly is the simplest and most efficient approach. Athena is serverless and can easily analyze log data stored in S3 with minimal effort and cost.
Invoking an AWS Lambda function for every access event is an over-complicated solution. Lambda would introduce unnecessary complexity and cost, especially given the serverless capabilities of Athena for log analysis.
Using CloudWatch Logs and Kinesis Data Analytics for SQL would require setting up multiple services and additional complexity. This approach is overkill for analyzing S3 access patterns, where Athena would be simpler and more cost-effective.