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Which limitations of MapReduce Apache Spark can remove?

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Apache Spark was developed to overcome the limitations of the MapReduce cluster computing paradigm. Apache Spark saves things in memory, whereas MapReduce keeps shuffling things in and out of disk.

Following is a list of few things which are better in Apache Spark:

  • Apache Spark keeps the cache data in memory, which is beneficial in iterative algorithms and can easily be used in machine learning.
  • Apache Spark is easy to use as it knows how to operate on data. It supports SQL queries, streaming data as well as graph data processing.
  • Spark doesn't need Hadoop to run. It can run on its own using other storages like Cassandra, S3, from which Spark can read and write.
  • Apache Spark's speed is very high as it can run programs up to 100 times faster in-memory or ten times faster on disk than MapReduce.

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