A weather forecasting company collects continuous temperature readings, aggregates them into larger Apache Parquet files, encrypts the files using client-side encryption with KMS-managed keys (CSE-KMS), and writes them to an Amazon S3 bucket in daily prefixes. The company occasionally runs SQL queries to compute sample moving averages for a specific calendar day. Which solution is the MOST cost-effective to meet these requirements?

Amazon Athena is the most cost-effective solution because it is a serverless, pay-per-query service that can directly query data in Amazon S3, including Parquet files encrypted with CSE-KMS. This eliminates the need to provision or manage any infrastructure, and you only pay for the data scanned. Amazon S3 Select is less suitable because it is designed for simple filtering and retrieval of data within a single object, not for complex analytical queries across multiple Parquet files. Amazon Redshift with Redshift Spectrum is powerful but generally more expensive due to the underlying Redshift cluster costs, even with Spectrum for external data. The use case described doesn't necessitate a full data warehouse. Amazon EMR Serverless with SparkSQL is also a viable option for querying data in S3, but it can be more expensive than Athena for ad-hoc queries due to its resource provisioning model and the overhead of Spark.