Which approach benefits from data partitioning during query execution?

Data partitioning is a technique where large datasets are divided into smaller, manageable pieces, which can greatly enhance the performance of data queries. The approach that benefits the most from data partitioning during query execution is parallelized data scanning.

When data is partitioned, each partition can be scanned concurrently across multiple processing units. This parallelization allows for faster retrieval of data, as different nodes or threads can work simultaneously on different partitions. As a result, the overall query execution time is drastically reduced, making it an optimal approach for querying large datasets efficiently.

Other approaches mentioned, such as sequential data processing or reduced complexity in SQL queries, do not leverage the advantages of partitioning to the same extent as parallelized scanning. Sequential processing generally accesses data in a linear fashion, which does not benefit from divided data for speed. Similarly, while reducing complexity in SQL queries can make them easier to read, it does not directly improve performance through parallel execution or partitioning. Scalable data extraction processes might utilize partitioning for managing growing data, but they don't inherently focus on querying performance as much as parallelized data scanning does.