What should the solutions architect do to meet these requirements with the LEAST amount of operational overhead?
Use Amazon Redshift to load all the content into one place and run the SQL queries as needed.
Use Amazon CloudWatch Logs to store the logs. Run SQL queries as needed from the Amazon CloudWatch console.
Use Amazon Athena directly with Amazon S3 to run the queries as needed.
Use AWS Glue to catalog the logs. Use a transient Apache Spark cluster on Amazon EMR to run the SQL queries ad needed.
Explanations:
Amazon Redshift requires loading data into a data warehouse, which involves more operational overhead for managing the Redshift cluster and ingesting data, especially for on-demand queries. This option is not ideal for running simple queries directly on JSON logs in S3.
Amazon CloudWatch Logs is primarily designed for log management and monitoring rather than for running ad-hoc SQL queries on JSON files. While it can store logs, it would not provide the flexibility and ease of querying needed for analysis without significant setup.
Amazon Athena allows users to run SQL queries directly against data stored in S3 without needing to move the data. It is serverless, requiring minimal operational overhead, and supports querying JSON format natively, making it the best choice for on-demand log analysis.
Using AWS Glue to catalog the logs and then running SQL queries via Apache Spark on Amazon EMR introduces unnecessary complexity and operational overhead, as it requires maintaining an EMR cluster. This approach is more suitable for complex data processing rather than simple log analysis.
My best guess is:
Use Amazon Athena directly with Amazon S3 to run the queries as needed.
I sort that the answer is:
Use Amazon Athena directly with Amazon S3 to run the queries as needed.