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Difference Between Apache Hadoop and Apache Storm

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  • Last Updated : 30 Sep, 2022
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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. Apache-Hadoop-vs-Apache-Storm 

Below is a table of differences between Apache Hadoop and Apache Storm: 

Features Apache Hadoop Apache Storm
Processing Distributed batch processing which uses MapReduce Distributed real-time data processing which uses DAGs
Latency High Latency i.e slow computation Low Latency i.e fast computation
Written Language Whole frame work is written in Java Frame work is written in Clojure and Java
Streaming processing It is State-ful streaming processing It is State-less streaming processing
Setup Easy to setup but operating cluster is hard Easy to use
Data streaming Data is dynamic and continuously streamed Data is static and nonvolatile i.e data is persistence
Speed Slow Fast
Use cases It is used in Twitter, Navisite, Wego etc It is used in Black Box Data, Search Engine Data etc
Architecture Hadoop 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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