The correct choice is AWS Glue because it is a serverless ETL and data catalog service that can discover, cleanse, transform, and organize raw data in an S3 data lake so teams can later analyze it with SQL tools.

AWS Glue provides an automated Data Catalog for schema discovery and metadata management and it offers serverless ETL capabilities with generated or custom jobs to clean and transform data at scale. It integrates directly with S3 for input and output and it produces cataloged tables that query engines can use, which makes it well suited for preparing data for analysis.

AWS Glue also registers datasets in the Data Catalog so services that run SQL such as Amazon Athena or Redshift Spectrum can query the prepared data without extra ingestion steps.

Amazon Athena is serverless and it lets you run SQL queries directly against data in S3, but it is not designed to perform comprehensive ETL or automated data discovery and cleansing before analysis.

Amazon Redshift is a managed data warehouse that is optimized for analytic queries and long term storage of transformed data, but it is not primarily a serverless ETL and it does not replace a purpose built data discovery and cleansing service.

AWS Step Functions is for workflow orchestration and it coordinates tasks across services, but it does not natively perform data discovery or the kind of large scale data transformation that a serverless ETL service provides.

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