A financial company plans to build a data mesh that must include centralized governance, analytics, and access control. They will use AWS Glue for the data catalog and ETL. Which pair of AWS services should they use to implement the data mesh? (Choose two.)

The correct options are Amazon S3 for data storage, Amazon Athena for data analysis, and AWS Lake Formation for centralized data governance and access control. Amazon S3 provides highly scalable, durable, and cost-effective object storage, making it ideal for the raw and refined data layers of a data mesh. Amazon Athena is a serverless query service that allows direct analysis of data stored in S3 using standard SQL, which aligns with the distributed analytics needs of a data mesh. AWS Lake Formation is crucial for centralized governance and access control over data lakes built on S3, enabling fine-grained permissions and metadata management across different data domains in a data mesh architecture. Amazon Aurora and Amazon RDS are relational databases, less suitable for the diverse, often semi-structured data typical of a data mesh compared to S3. Amazon Redshift is a data warehouse, which can be part of a data mesh but doesn't fulfill the primary storage role as comprehensively as S3. Amazon EMR is a big data processing framework, not a primary data analysis tool for end-users like Athena. AWS Glue DataBrew is a data preparation service, not a comprehensive solution for centralized governance and access control across a data mesh.