Which open-source framework enables distributed storage and processing of large data sets across clusters of computers?

Hadoop is the foundational open-source framework designed specifically for the distributed storage and processing of large data sets across clusters of computers. It accomplishes this through its two primary components: the Hadoop Distributed File System (HDFS) and MapReduce, which is a programming model for processing large data sets in parallel.

HDFS allows for the storage of data across multiple nodes in a cluster, ensuring redundancy and fault tolerance, while MapReduce enables the efficient processing of this data through parallel computations. This architecture is particularly well-suited for handling petabytes of data, making Hadoop a powerful tool for big data applications.

While Apache Spark is also used for distributed data processing and can perform tasks more rapidly than Hadoop's MapReduce due to its in-memory processing capabilities, it does not provide its own storage system like HDFS. Instead, Spark typically relies on external storage systems, including HDFS.

TensorFlow is a framework for building and training machine learning models and does not focus on data storage or parallel processing in the same way that Hadoop does. Kubernetes, on the other hand, is a container orchestration system that can manage applications in containers but is not specifically designed for the storage and processing of large data sets like Hadoop.

Thus, Hadoop stands out as the correct answer