MapReduce and Apache Spark are two major distributed computing frameworks used in data science and big data analytics for processing massive volumes of data across clusters of computers. Both were designed to handle scalability and reliability challenges in large-scale data environments, yet they differ significantly in terms of processing speed, fault tolerance mechanisms, and ease of use. MapReduce, originally developed by Google, laid the foundation for distributed batch processing, while Spark, developed under the Apache Software Foundation, introduced a more flexible and faster approach to big data computation. In terms of data processing speed, the most prominent difference between MapReduce and Spark lies in how they manage intermediate data. MapReduce follows a disk-based processing model. Each job is divided into two main phases: the map phase and the reduce phase. After the map phase completes, its output is written to disk. Similarly, the reduce phase reads this intermediate data from disk before producing final results. This repeated reading and writing to disk ensures reliability but introduces significant latency. As a result, MapReduce is well suited for large batch-processing tasks where execution time is not extremely critical, but it becomes inefficient for workloads that require frequent iterations or real-time responses. Apache Spark, in contrast, is designed around in-memory computing. It stores intermediate results in memory using resilient distributed datasets and related abstractions, allowing data to be reused across multiple operations without repeated disk access. When data fits in memory,