Which combination of components can meet the requirements for creating a data lake in Amazon S3 with tiered storage?

The selected option emphasizes the critical role of AWS Glue Data Catalog in managing metadata for a data lake built on Amazon S3. In a data lake architecture, effective metadata management is vital for organizing and retrieving the vast amounts of data stored. AWS Glue Data Catalog acts as a central repository that keeps track of data schemas and definitions, making it easier for data analysts and other services to discover, access, and query the data.

By providing a serverless environment that automatically discovers and maintains a catalog of the datasets stored in S3, AWS Glue supports the tiered storage model by allowing users to understand the data's storage costs and access patterns. This, in turn, facilitates efficient querying and data processing operations.

In context, other components like Amazon EMR with Apache Spark or Hive serve essential roles in processing data, but they do not directly address the requirements for metadata management of a data lake. Similarly, although Amazon Athena provides query capabilities, it relies on the underlying metadata managed by Glue for effective data analysis. Therefore, while those options have their own merits in a data lake architecture, the Glue Data Catalog is specifically essential for establishing a well-organized and efficient data lake environment.