Hadoop is data processing framework which has traditionally been used to run the map/reduce jobs. These are all long running jobs that take minutes or hours to complete. Spark has been designed to run on top of Hadoop and it is also an alternative to the traditional batch map and reduce model that can be used for real-time stream data processing and fast interactive queries that finish within seconds. So, Hadoop supports the both traditional map/reduce and Spark.
Difference between Hadoop Map-reduce and Apache Spark
Spark stores the data in-memory whereas Hadoop stores the data on disk. Hadoop uses the replication to achieve the fault tolerance whereas Spark uses different data storage model, resilient distributed datasets ,it uses a clever way of guaranteeing the fault tolerance that minimizes the network of input and output.
Training:
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