What framework is commonly used for processing large datasets across clusters of computers?

The framework that is commonly used for processing large datasets across clusters of computers is Apache Hadoop. This open-source framework allows for the distributed storage and processing of large data sets using a model known as MapReduce. It utilizes the Hadoop Distributed File System (HDFS) to store data across a cluster of computers, which enables high-throughput access to application data.

Hadoop’s design is particularly well-suited for tasks that involve processing vast amounts of data, as it can efficiently distribute tasks across multiple nodes in the cluster, allowing for concurrent processing. This increases speed and performance when handling big data applications, making it a preferred choice in the field of large-scale data analytics.

The other available options, while they each have their strengths and important roles within the big data ecosystem, serve different purposes or are built on top of frameworks like Hadoop rather than being core processing frameworks on their own. For instance, Apache Spark is often used for fast data processing and can run on top of Hadoop, but it is distinct from the core capabilities of Hadoop itself. Similarly, Apache Cassandra focuses on NoSQL database storage, and Apache Storm is designed for real-time stream processing rather than batch processing of large datasets.