What does Hadoop specifically support in computing?

Hadoop specifically supports distributed storage and processing of large-scale data, which is fundamentally designed to handle vast amounts of data across a cluster of computers. This capability allows Hadoop to process data in a parallelized fashion, leveraging the power of multiple machines to improve efficiency and speed. The architecture of Hadoop, which includes components like Hadoop Distributed File System (HDFS) for storage and MapReduce for processing, enables it to break down large data sets into smaller, manageable chunks that can be processed simultaneously, therefore supporting scalability and reliability in data management.

In contrast, other choices focus on different aspects of computing. Single-user data processing refers to scenarios where data is handled by one user or one system, which does not leverage the distributed capabilities of Hadoop. Cloud-based applications may utilize Hadoop for storing and processing data but do not define what Hadoop natively supports. Real-time data analytics implies a type of processing that is not the primary strength of Hadoop, as it is typically more suited for batch processing. Overall, the emphasis of Hadoop's design is on efficiently managing and processing large sets of distributed data across multiple nodes in a computing environment.