In AWS, what happens when data is partitioned?

When data is partitioned in AWS, it allows for independent scanning of the partitions. Partitioning divides data into smaller, more manageable subsets, which enables services like Amazon Redshift or Amazon Athena to query and process only the relevant partitions of data rather than the entire dataset. This can greatly improve query performance and efficiency, as it reduces the amount of data that needs to be scanned for specific queries.

For example, if a dataset is partitioned based on date, a query that only needs data from a specific week can scan just that week's partition instead of the full dataset. This leads to faster query execution times and reduced costs due to lower data scanning volume.

In contrast, encryption is concerned with securing data, and automatic backups relate to data durability rather than its organization. Storing data in a single location would defeat the purpose of partitioning, which is designed to promote distributed and efficient data processing.