Overview
The MapReduce pattern processes large datasets by breaking the work into two phases: Map (process data in parallel) and Reduce (aggregate results). This pattern is essential for processing large datasets in parallel, distributed computing, data analytics and aggregation, and batch processing jobs.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
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