MapReduce Word Count Example using Hadoop and Java

In MapReduce word count example, we find out the frequency of each word. Here, the role of Mapper is to map the keys to the existing values and the role of Reducer is to aggregate the keys of common values. So, everything is represented in the form of Key-value pair. Source Code Link : https://codewitharjun.com/word-count-example-using-hadoop-and-java/ GitHub Link : https://github.com/arjungautam1/WordCountHadoop Hadoop Installation Link : MacOS : https://youtu.be/inDC9jgwpWY Ubuntu : https://youtu.be/Slbi-uzPtnw IntelliJ Installation Link : MacOS : https://youtu.be/gM7SBQyKCY0 Ubuntu : https://youtu.be/gZ_XtkcPSHY Install Java JDK : MacOS : https://youtu.be/USKdqHp3Glg Linux : https://youtu.be/KiThrZiEE9g PayPal : https://paypal.me/codewitharjun Patreon : https://www.patreon.com/codewitharjun ➡️ Subscribe : https://www.youtube.com/codewitharjun Stay Connected : ➡️ LinkedIn : https://np.linkedin.com/in/arjungautam1 ➡️ Github : https://github.com/arjungautam1 Join Community : ➡️Discord : https://discord.gg/cn2x3jhMW4 Follow me on : ➡️ Medium: https://codewitharjun.medium.com/ ➡️ Dev.to : https://dev.to/codewitharjun Timestamps : 00:00 - Word Count Example 00:10 - Check the Hadoop Version and start all daemons 01:20 - Create Maven Project using IntelliJ 02:15 - Add dependencies in pom.xml file 03:10 - Create Mapper, Reducer and Runner class 04:45 - Create jar file using maven install 05:40 - Create input file and put into hadoop file system (HDFS) 08:00 - Run the jar file using hadoop jar command 10:30 - Check the output #hadoop #mapreduce #CodeWithArjun #Programming #datascience