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Introduction In the era of Big Data , organizations are generating information at an unprecedented rate. From social media interactions and online transactions to IoT sensors and enterprise systems, the data universe is expanding every second. Managing, processing, and extracting valuable insights from this massive volume of data requires not only advanced technology but also a highly efficient computational model. This is where the Parallel MapReduce computation model plays a transformative role. Understanding the MapReduce Computation Model At its core, the MapReduce model is a programming framework designed for processing large datasets across distributed systems. It simplifies complex data processing by dividing the workload into two distinct phases — Map and Reduce — each handling a specific part of the computation. The Map Phase: This phase takes input data and converts it into key-value pairs. The data is then distributed across multiple nodes, where each node processe...