A data engineer must clean and prepare several terabytes of raw data that reside in Amazon S3, then load the processed data into Amazon Redshift for analytics. Data analysts require the ability to run complex queries. The solution should remove the need for complex ETL pipelines and avoid managing infrastructure. Which approach provides the required functionality with the LEAST operational overhead?

The correct approach leverages AWS Glue DataBrew for data preparation and AWS Glue for loading, both serverless services that minimize operational overhead. DataBrew offers a visual interface for cleaning and transforming data without writing code, and AWS Glue handles the ETL process efficiently. Using Amazon Redshift directly for queries allows data analysts to run complex queries on the processed data, meeting the requirement. Incorrect options: Amazon EMR requires managing clusters, increasing operational overhead. AWS Step Functions is for workflow orchestration, not direct data loading into Redshift in this context. Amazon QuickSight is a BI tool, not for running complex SQL queries directly on Redshift. AWS Lambda is suitable for smaller, event-driven tasks, but not ideal for processing several terabytes of data due to execution limits. Kinesis Data Firehose is for streaming data, not batch loading of pre-existing S3 data. Amazon Athena is for querying data in S3, not for querying data loaded into Redshift. AWS Glue for preparation is good, but AWS DMS is primarily for database migration and continuous replication, not the most efficient choice for a one-time or recurring batch load from S3 to Redshift. Redshift Spectrum queries data directly in S3, not the processed data loaded into Redshift.