Hadoop vs spark

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Hadoop VS Spark
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• The critical thing to remember about Spark and
  Hadoop is they are not mutually exclusive or
  inclusive but they work well together and makes
  the combination strong enough for lots of big
  data applications.
• Hadoop Defined
• A software library and a framework for permitting
  the distributed processing of big data sets among
  computer clusters using with the help of
  noncomplex programming models is called Hadoop
  and is the project of Apache organization.

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From scaling single computer systems up to
thousands of systems for computing power and
storage, Hadoop does the job with ease.  For
creating the Hadoop framework there are a set of
modules created by Hadoop.  The Primary Hadoop
Framework Modules Are Hadoop Common Hadoop
Distributed File System (HDFS) Hadoop YARN Hadoop
MapReduce
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• Spark Defined
• A rapid and a proper engine for big data
  processing used by most of the Apache Spark
  developers is called Spark. Hadoop's big data
  framework is 800-lb gorilla and Spark is 130-lb
  big data cheetah.
• The real-time data processing capability and
  MapReduce's disk-bound engine are compared to and
  the real-time game is won by the former. Spark is
  also considered a module on Hadoop project page.
• A cluster-computing framework called spark means
  it is contesting with lots of MapReduce than with
  the whole Hadoop.

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• Some Major Points Include 
• Performance
• Ease of Use
• Costs
• Compatibility
• Data Processing
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