A marketing company receives a large amount of new clickstream data in Amazon S3 from a marketing campaign. The company needs to analyze the clickstream data in Amazon S3 quickly and then decide whether to process the data further in the data pipeline. Which solution meets these requirements with the LEAST operational overhead?

The correct solution leverages AWS Glue Crawler to automatically discover the schema of the clickstream data in Amazon S3 and register it in the AWS Glue Data Catalog. Amazon Athena can then directly query this cataloged data in S3 using standard SQL, providing a serverless, interactive query service with minimal operational overhead. This allows for quick analysis without provisioning or managing any infrastructure. Creating external tables in a Spark catalog or Hive metastore with Spark/EMR jobs involves managing clusters, increasing operational overhead. Amazon Kinesis Data Analytics is designed for real-time stream processing, not for querying static data in S3, making it an unsuitable choice for this batch analysis requirement.