Which distributed computing framework is commonly used for data processing?

The commonly used distributed computing framework for data processing is Apache Hadoop. Hadoop is designed for storing and processing large datasets across clusters of computers in a distributed computing environment, which allows it to handle big data effectively. It features a distributed file system (HDFS) that enables data to be spread across multiple nodes, ensuring high availability and fault tolerance. By utilizing the MapReduce programming model, Hadoop processes vast amounts of data in parallel, significantly increasing processing speed and scalability.

In contrast, the other options serve different purposes and do not primarily function as distributed computing frameworks. Microsoft SQL Server and Oracle DBA are relational database management systems focused on structured data management, whereas MongoDB is a NoSQL database designed for document storage and retrieval, lacking the inherent distributed processing capabilities that Hadoop offers. Thus, the unique strengths of Apache Hadoop in handling large-scale data processing tasks in distributed environments make it the correct choice.