What is the primary purpose of the Reduce phase in MapReduce?

The Reduce phase in MapReduce aggregates the intermediate key-value pairs generated during the Map phase. It performs operations like summing, averaging, or concatenating, depending on the problem at hand. The results are then written to HDFS. Example: In a word count application: • Map phase: Generates intermediate pairs like (word, 1). • Reduce phase: Aggregates these pairs to compute total counts like (word, total_count). This separation of concerns ensures scalability and parallelism in Big Data processing. ________________________________________ Why Other Options Are Incorrect: 1. Splitting input data into smaller chunks: This is done in the InputSplit phase, not during Reduce. 2. Processing key-value pairs to generate intermediate data: This occurs in the Map phase, not in the Reduce phase. 3. Shuffling and sorting intermediate data: The Shuffle and Sort step precedes the Reduce phase and ensures data is organized for aggregation. 4. Storing the processed data in HDFS: This is the final output phase, unrelated to the logic of the Reduce phase.