Speed Apache Spark –Spark is lightning fast cluster computing tool. Apache Spark runs applications up to 100x faster in memory and 10x faster on disk than Hadoop. Because of reducing the number of read/write cycle to disk and storing intermediate data in-memory Spark makes it possible. Hadoop MapReduce –MapReduce reads and writes from disk, as a result, it slows down the processing speed. Difficulty Apache Spark –Spark is easy to program as it has tons of high-level operators with RDD – Resilient Distributed Dataset. Hadoop MapReduce –In MapReduce, developers need to hand code each and every operation which makes it very difficult to work. Easy to Manage Apache Spark –Spark is capable of performing batch, interactive and Machine Learning and Streaming all in the same cluster. As a result makes it a completedata analyticsengine. Thus, no need to manage different component for each need. Installing Spark on a cluster will be enough to handle all the requirements. Hadoop MapReduce –As MapReduce only provides the batch engine. Hence, we are dependent on different engines. For example- Storm, Giraph, Impala, etc. for other requirements. So, it is very difficult to manage many components. For more refer below link: Spark vs Hadoop
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