Difference Between Apache Hadoop and Apache Storm Last Updated : 15 Feb, 2023 Comments Improve Suggest changes Like Article Like Report Apache Hadoop: It is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Apache Storm: It is a distributed stream processing computation framework written predominantly in the Clojure programming language. Originally created by Nathan Marz and the team at BackType, the project was open-sourced after being acquired by Twitter. Below is a table of differences between Apache Hadoop and Apache Storm: FeaturesApache HadoopApache StormProcessingDistributed batch processing which uses MapReduceDistributed real-time data processing which uses DAGsLatencyHigh Latency i.e slow computationLow Latency i.e fast computationWritten LanguageWhole frame work is written in JavaFrame work is written in Clojure and JavaStreaming processingIt is State-full streaming processingIt is State-less streaming processingSetupEasy to setup but operating cluster is hardEasy to useData streamingData is dynamic and continuously streamedData is static and nonvolatile i.e data is persistenceSpeedSlowFastUse casesIt is used in Twitter, Navisite, Wego etcIt is used in Black Box Data, Search Engine Data etcArchitectureHadoop comprises HDFS (used for data storage) and MapReduce (used for Computation) as architectural units.Storm comprises streams, spouts, and bolts as their architectural units. 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