What is MapReduce♻️in Hadoop🐘| Apache Hadoop🐘

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Sir please make a playlist of big data and add this video along with HDFS, map reduce and continue the playlist with videos on Big Data

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Hadoop you!

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00:02 MapReduce is a key component of Apache Hadoop for distributed and parallel processing of large datasets.
01:28 MapReduce is a two-step process consisting of map and reduce tasks.
02:53 MapReduce in Hadoop is a process where reducer class reduces intermediate results into final results.
04:04 MapReduce components in Hadoop
05:20 Distribute data for maximum sales analysis
06:34 MapReduce is a processing unit that works on data notes in parallel to avoid repeated data calls and delays.
07:46 MapReduce in Hadoop involves parallel processing and shuffling to achieve efficiency.
08:51 MapReduce is a technique in Hadoop for parallel processing.
Crafted by Merlin AI.00:02 MapReduce is a key component of Apache Hadoop for distributed and parallel processing of large datasets.
01:28 MapReduce is a two-step process consisting of map and reduce tasks.
02:53 MapReduce in Hadoop is a process where reducer class reduces intermediate results into final results.
04:04 MapReduce components in Hadoop
05:20 Distribute data for maximum sales analysis
06:34 MapReduce is a processing unit that works on data notes in parallel to avoid repeated data calls and delays.
07:46 MapReduce in Hadoop involves parallel processing and shuffling to achieve efficiency.
08:51 MapReduce is a technique in Hadoop for parallel processing.

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Thanka ❤

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Lecture - 4 Completed ✅ in Big Data

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Thank you for your nice explanation

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Thank You So, much Sir.... Your video benefitted me a lot...

[@ShivaniMathur-c4t]

you're awesome, I was so scared of MapReduce and you explained it in such easy way. Thanks

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Wonderful 😊

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Chumeshwari Boss... jabrdast example देके map reduce पढाया ..thanks lot Sir..❤❤❤💐🙏🤗👍

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[@iotdheeraj7318]

Intermediate data generated during the Map phase is stored locally, but it can be recomputed if lost due to a node failure, preventing the entire job from failing.
(As many people had a doubt about this)

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Just brilliantly explained thank you sir

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Great as always 😎👍
1st Comment 😏😏😏😎

[@IrfanAnjumm]

Hi, at time stamp 09:20 you are talking about the intermediate result. Can you explain where it is performed? Does all the data nodes send the results to the master node so that it performs the shuffling and reducing? Please guide. Thanks for the help

[@Pranauv]

hey there!
as far as i can understand this distributed architecture, i think the processing is done in the data nodes itself and those results are put together by the task trackers at each DN and sent to the main NameNode's Job tracker which then gives the result...
Please Tell me if I'm wrong

[@KarthikBhatl]

This understanding is right. Processing is done at the data node itself @@Pranauv

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Its the divide and conquer technique .

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Many Thanks

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Thanks you sir 🌹

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Where is the shuffling done? On a separate machine?

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Sir please give ur notes ✌🏻

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Mapping is for giving value to the key or is it counting how many times it is appearing in the data? can you clear ?

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plz

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