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What are benefits of spark over mapreduce?
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Due to the availability of in-memory processing, Spark implements the processing around 10-100x faster than Hadoop MapReduce. MapReduce makes use of persistence storage for any of the data processing tasks.
Unlike Hadoop, Spark provides in-built libraries to perform multiple tasks form the same core like batch processing, Steaming, Machine learning, Interactive SQL queries. However, Hadoop only supports batch processing.
Hadoop is highly disk-dependent whereas Spark promotes caching and in-memory data storage.
Spark is capable of performing computations multiple times on the same dataset. This is called iterative computation while there is no iterative computing implemented by Hadoop.
Read more in this blog about the comparison of Spark and MapReduce. Your Comment
Unlike Hadoop, Spark provides in-built libraries to perform multiple tasks form the same core like batch processing, Steaming, Machine learning, Interactive SQL queries. However, Hadoop only supports batch processing.
Hadoop is highly disk-dependent whereas Spark promotes caching and in-memory data storage.
Spark is capable of performing computations multiple times on the same dataset. This is called iterative computation while there is no iterative computing implemented by Hadoop.
Read more in this blog about the comparison of Spark and MapReduce. Your Comment
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