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MapReduce Word Count Example using Hadoop and Java
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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.
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Timestamps :
00:00 - Word Count Example
00:10 - Check the Hadoop Version and start all daemons
01:20 - Create Maven Project using IntelliJ
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
GitHub Link :
Hadoop Installation Link :
IntelliJ Installation Link :
Install Java JDK :
Stay Connected :
Join Community :
Follow me on :
Timestamps :
00:00 - Word Count Example
00:10 - Check the Hadoop Version and start all daemons
01:20 - Create Maven Project using IntelliJ
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
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