Mapreduce In Hadoop | MapReduce Explained | MapReduce Architecture | MapReduce Tutorial |Simplilearn



This MapReduce tutorial will help you learn what is MapReduce, an analogy on MapReduce, the steps involved in MapReduce, how MapReduce performs parallel processing, MapReduce workflow, the architecture of MapReduce and a demo on MapReduce. MapReduce is a programming framework that processes data parallelly on multiple data nodes. You will understand how MapReduce accepts data in various formats, splits and records the data to generate input key-value pairs, uses mappers and combiners to generate intermediate key-value pairs and then uses shuffle, sort and reduce to generate the final output and stores it on HDFS. Finally, you will get hands-on expertise on how to run tasks using MapReduce. Now, let’s get started and learn MapReduce in detail.

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Below are explained in this MapReduce tutorial:
1. MapReduce Analogy (02:49)
2. What is Hadoop MapReduce? (04:46)
3. Parallel processing in MapReduce (10:31)
4. Hadoop MapReduce Workflow (11:05)
5. MapReduce Architecture (18:00)
6. Demo on MapReduce (20:41)

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What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
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13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques

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6 thoughts on “Mapreduce In Hadoop | MapReduce Explained | MapReduce Architecture | MapReduce Tutorial |Simplilearn”
  1. around 2:03 you said "every idividual would be counting books on every floor". Is this correct or each will be counting books on allocated floor?. Reply fast. Rest is good

  2. Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Thanks for watching the video. Cheers!

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