A company stores a data lake on Amazon S3 that ingests Apache Parquet data from multiple sources. The data requires several transformation steps (anomaly filtering, normalizing date/time values, and generating aggregates) before analysts can access the transformed data in S3. The company wants a prebuilt, no-code transformation solution that provides data lineage and data profiling, and that allows sharing the transformation steps across the company. Which solution meets these requirements?

AWS Glue DataBrew is the correct solution because it is a visual data preparation tool that offers a no-code interface for data transformation, directly addressing the requirement for a prebuilt, no-code solution. DataBrew provides built-in features for data profiling and supports data lineage by tracking transformations. Its "recipes" allow sharing transformation steps across the company, ensuring consistency and reusability. AWS Glue Studio uses a visual canvas but is primarily for building ETL jobs with code or visual scripting, not a no-code solution for data preparation. Amazon EMR Serverless is a powerful analytics platform but requires code (e.g., Spark, Hive) for transformations, not a no-code approach. Amazon Athena is a query service for S3 data; while it can transform data with SQL, it's not a no-code solution, nor does it inherently provide data profiling or lineage in the same way DataBrew does.