You need to search historical JSON-format logs stored in Amazon S3 from 10 Lambda functions for error messages. Error values may appear under different fields, but all error strings start with the same prefix. Which method is the most operationally efficient way to analyze those log files?

The most operationally efficient method is to use AWS Glue and Amazon Athena. AWS Glue can crawl and catalog the JSON-formatted logs directly from S3, creating a schema that Athena can then query using standard SQL. This approach is highly scalable and cost-effective for querying large datasets without needing to load them into a separate database. Athena is designed for ad-hoc analysis of data in S3. S3 Select is less efficient for this scenario because it requires running individual queries against potentially many objects and doesn't provide a unified view or schema for complex analysis across multiple files. CloudWatch Logs Insights and CloudWatch Contributor Insights are designed for logs ingested into CloudWatch Logs, not for historical JSON files stored directly in S3. While Lambda logs can go to CloudWatch Logs, the question specifies logs "stored in Amazon S3," implying they are not primarily in CloudWatch Logs for this analysis.