A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub…

While QuickSight with Athena is a good combination, heat maps are better suited for visualizing data density or correlation rather than directly identifying the 'strongest performer' with precise aggregated values. Pivot tables offer a more direct numerical summary and ranking capability for this specific requirement.

Connecting QuickSight directly to Amazon S3 for querying Parquet files is less efficient and flexible than using Amazon Athena as an intermediary. Athena provides robust SQL query capabilities over S3 data lakes, making it a better choice for structured data analysis.

Amazon QuickSight with Amazon Athena as the data source is ideal for querying structured data like Parquet files directly in Amazon S3 without managing infrastructure, fulfilling the 'least amount of effort' and efficiency requirements. A pivot table is the most effective visual type for senior leadership to quickly aggregate sales data by country and sub-organization, enabling easy identification and comparison of the strongest performers through numerical summaries and ranking.

Direct connection from QuickSight to Amazon S3 for querying Parquet files is less optimized compared to using Amazon Athena for structured data in a data lake. Although pivot tables are appropriate, the data source connection method is not the most efficient.