DATABASE FUNDAMENTALS
BASICS OF BIG DATA
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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True
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False
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Either A or B
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None of the above
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Detailed explanation-1: -Apache Spark is potentially 100 times faster than Hadoop MapReduce. Apache Spark utilizes RAM and isn’t tied to Hadoop’s two-stage paradigm. Apache Spark works well for smaller data sets that can all fit into a server’s RAM. Hadoop is more cost-effective for processing massive data sets.
Detailed explanation-2: -Apache Spark is very much popular for its speed. It runs 100 times faster in memory and ten times faster on disk than Hadoop MapReduce since it processes data in memory (RAM).
Detailed explanation-3: -Spark provides a faster and more general data processing platform. Spark lets you run programs up to 100x faster in memory, or 10x faster on disk, than Hadoop.
Detailed explanation-4: -Apache Spark is well-known for its speed. It runs 100 times faster in-memory and 10 times faster on disk than Hadoop MapReduce. The reason is that Apache Spark processes data in-memory (RAM), while Hadoop MapReduce has to persist data back to the disk after every Map or Reduce action.
Detailed explanation-5: -The primary difference between Spark and MapReduce is that Spark processes and retains data in memory for subsequent steps, whereas MapReduce processes data on disk. As a result, for smaller workloads, Spark’s data processing speeds are up to 100x faster than MapReduce.