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 as needed.
Explanations:
Amazon Redshift is a data warehouse solution that requires data loading, which adds operational overhead and is not necessary for on-demand analysis of JSON logs.
Amazon CloudWatch Logs is not ideal for analyzing JSON logs stored in S3, as it is primarily for log storage and monitoring, not for querying external log files.
Amazon Athena allows for serverless querying directly on data stored in S3 using SQL, making it the most efficient option with minimal operational overhead.
AWS Glue and Apache Spark on EMR introduce unnecessary complexity and operational overhead for simple, on-demand log analysis compared t
I infer that the answer is:
Use Amazon Athena directly with Amazon S3 to run the queries as needed.