A company has an Amazon S3 data lake governed by AWS Lake Formation. The company wants to create an Amazon QuickSight visualization by joining the data lake with operational data stored in an Amazon Aurora MySQL database. The company must enforce column-level authorization so the marketing team can access only a subset of columns. Which solution meets these requirements with the LEAST operational overhead?

The correct solution leverages Lake Formation's capabilities for data ingestion and fine-grained access control. A Lake Formation blueprint automates ingesting data from Aurora MySQL into the S3 data lake, simplifying the ETL process. Lake Formation then enforces column-level authorization directly on the data lake, which Athena (used by QuickSight) respects. This centralizes access control and minimizes operational overhead by using Lake Formation's native security features. Incorrect options: Using Amazon EMR to ingest directly to QuickSight SPICE bypasses the data lake and Lake Formation's governance, making column-level authorization difficult to manage consistently across the data lake. Using AWS Glue Studio to ingest to S3 and then an IAM policy for QuickSight users is less efficient for column-level control than Lake Formation, which is designed for this purpose. IAM policies on S3 buckets are for object-level, not column-level, access. AWS Glue Elastic Views is for creating materialized views across various data stores, but an S3 bucket policy cannot enforce column-level access control within the data stored in S3.