What term describes a software framework that allows for distributed processing of large data sets?

The term that best describes a software framework enabling distributed processing of large data sets is known as a distributed computing framework. This type of framework facilitates the execution of data processing tasks across multiple machines, allowing it to handle vast amounts of data efficiently. In distributed computing, the workload is distributed among various nodes in the network, which can operate simultaneously.

This capability is vital for processing large data sets typical in big data environments, where traditional processing methods may be inadequate. Examples of distributed computing frameworks include Apache Hadoop and Apache Spark, which are specifically designed to manage and process extensive data across clusters of servers.

In contrast, other terms like data warehouse, data lake, and data mart relate more to data storage and organization rather than directly describing the processing mechanisms used in a distributed computing context. A data warehouse is focused on storing structured data for analysis, a data lake is designed for storing raw data in its native format, and a data mart is a subset of a data warehouse intended for a specific business line or team. None of these options encompass the functionality of processing large data sets in a distributed manner like a distributed computing framework does.