How does MapReduce handle large datasets in a distributed computing environment?
- Data Compression
- Data Partitioning
- Data Replication
- Data Shuffling
MapReduce handles large datasets in a distributed computing environment through data partitioning. The input data is divided into smaller chunks, and each chunk is processed independently by different nodes in the cluster. This parallel processing enhances the overall efficiency of data analysis.
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