Which of the following represents a characteristic of Spark's architecture?

Spark's architecture is fundamentally based on the concept of a cluster computing framework. This characteristic allows Spark to efficiently process large datasets across multiple nodes in a cluster, thereby enhancing its ability to handle distributed data processing tasks. Unlike single-node systems, Spark can leverage the power of a cluster to scale horizontally, which is essential when working with big data.

A cluster computing framework enables the execution of parallel processing, distributing data and computations across various nodes. This design allows for increased speed and efficiency, especially for tasks that can be executed concurrently. Spark's ability to manage resources and distribute tasks in a cluster environment is central to its performance advantages over many traditional data processing systems.

In contrast, single-threaded execution would severely limit performance by restricting the processing to one operation at a time, and is not representative of Spark's distributed nature. While Spark does support in-memory processing that greatly enhances the speed of data operations, it is not limited to this method—it also efficiently handles data stored on disk. Finally, while Spark can process data in batches, it is not solely dependent on batch processing; it supports both batch and real-time streaming processing, making it versatile in handling various data types and processing needs.