Apache Spark and Hadoop are the big data frameworks but have different purposes. Hadoop on one hand is a distributed data infrastructure. It helps in distributing massive data collections to massive nodes data collections in a cluster of commodity servers by which there is no requirement to buy and maintain expensive custom hardware. Also it indexes and keeps track of that data making big data processing and analytics more efficient than possible. On the other hand, Spark is a data processing tool which will operate on the distributed data collections, it does not distribute storage.
Hadoop not only have storage component which is known as Hadoop Distributed File System, but it also has a processing component called MapReduce and because of this Spark is not required for getting the process done. They way Hadoop can be used without Spark similarly Spark can also be used without Hadoop. Though Spark does not come with its own file management system, it needs an integration, if its not with HDFS then any other cloud based data platform. Eventually Spark was developed for Hadoop but however it is believed that they work better together.
Spark is also faster than Hadoop’s MapReduce due to the way it processes data. MapReduce operates in steps. Also in one fell swoop Spark operates on the whole data. As has been explained by Kirk Borne, principal data scientist at Booz Allen Hamilton, “The MapReduce workflow looks like this: read data from the cluster, perform an operation, write results to the cluster, read updated data from the cluster, perform next operation, write next results to the cluster, etc.” Further for Spark he said Spark completes the full data analytics operations in-memory and in near real-time: “Read data from the cluster, perform all of the requisite analytic operations, write results to the cluster, done.”
Spark is 10 times faster than Hadoop and upto 100 times faster in memory analytics. The MapReduce processing style can be done when the data operations and reporting needs are static and one can wait for batch mode processing. But if analytics is to be done on streaming data, like sensors on factory floor or ant application of multiple operations then Spark is a better option.
Machines learning algorithms needs multiple operations and common application for Spark has real time marketing campaigns, cyber security analytics, online product recommendations and machine log monitoring.
Hadoop is also naturally resilient to system faults or failures as data are written to the desk after each operation but this is not with Spark, it has an in-built resilience and the fact that its data objects stored in resilient distributed data sets distributed across the data cluster. Borne has said, “These data objects can be stored in memory or on disks, and RDD provides full recovery from faults or failures.”
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