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MapReduce and Apache Spark are both distributed data processing frameworks widely used in big data analytics, but they differ significantly in terms of processing speed, fault tolerance, and ease of use. MapReduce, introduced by Google and popularized through the Hadoop ecosystem, follows a disk-based processing model where intermediate results are written to disk between the map and reduce stages. This design ensures strong fault tolerance, as the system can recover from node failures by re-executing failed tasks using the saved intermediate data. However, the reliance on disk I/O makes MapReduce relatively slower, particularly for iterative or interactive computations, which require multiple passes over the same data. Each iteration in MapReduce incurs _____ ______ _______ ______ _______.
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