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What is the difference in idea, design and code, between Apache Spark and Apache Hadoop?

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Apache Spark was introduced to overcome the limitations of Hadoop's external storage-access architecture. Apache Spark replaces Hadoop's original data analytics library, MapReduce, with faster machine learning processing capabilities. However, Spark is not mutually exclusive with Hadoop.
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Apache Spark was introduced to overcome the limitations of Hadoop's external storage-access architecture. Apache Spark replaces Hadoop's original data analytics library, MapReduce, with faster machine learning processing capabilities. However, Spark is not mutually exclusive with Hadoop. read less
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Apache Spark: Fast, in-memory processing for both real-time and batch data. Less code due to high-level APIs. Apache Hadoop: Disk-based batch processing using HDFS and MapReduce. More code required for data processing tasks.
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Apache Spark: Fast, in-memory processing for both real-time and batch data. Less code due to high-level APIs. Apache Hadoop: Disk-based batch processing using HDFS and MapReduce. More code required for data processing tasks. read less
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