SOFTWARE ENGINEERING

Detailed explanation-1: -The partitioner is responsible for processing the map output. Once MapReduce splits the data into chunks and assigns them to map tasks, the framework partitions the key-value data. This process takes place before the final mapper task output is produced. MapReduce partitions and sorts the output based on the key.

Detailed explanation-2: -Reduce stage − This stage is the combination of the Shuffle stage and the Reduce stage. The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS.

Detailed explanation-3: -MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks, and processing them in parallel on Hadoop commodity servers. In the end, it aggregates all the data from multiple servers to return a consolidated output back to the application.