Difference Between Apache Hadoop and Apache Storm

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: 

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-full 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.