Map Reduce Concept with Simple Example

[@NewtonInDaHouseYo]

Very well explained. Concise and easy to follow. Thanks!

[@LegendKopper]

Why do you provide an example to not going further into it? It would have been very helpful if you explained the concrete shuffle and reduce step with the city sales.

[@Reet_triple_charm]

i guess because that nonsensical one row example in the beginning to show "aggregation" of sales fell apart ... I literally got annoyed in the first one minute itself.

[@jigonro]

Once the mapped data has been shuffled, it can be reduced in parallel. Shuffling, as show on the video, moves data so that each reducer has the data that needs to work independently of the other reducers and therefore the reducers can work in parallel. Remember, MapReduce is actually Map -> Shuffle -> Reduce

[@nitinsridhar7555]

Thanks for this video, Could be explained better.

[@luisdominik128]

@Francis Moshe yup, I have been watching on instaflixxer for months myself :D

[@portermagnus9154]

@Francis Moshe Yea, have been using instaflixxer for months myself :)

[@andrewrandall9989]

@Francis Moshe yea, I have been watching on InstaFlixxer for months myself =)

[@mysticlunala8020]

Comment section scares me.

[@albertkwan4261]

Well done! Thx!

[@colleejennifer2609]

I'm waiting for the continuation))

[@abufirnazmujiburrahman360]

Very nice explanation it's very helpful for me thank u 🤝

[@shahulvellore4594]

A very nice and crispy tutorial.. a really excellent one..

[@mxCurrent]

Hey! nice video thanks for that!
Can you maybe explain what the node is here (number of processors?)? And what is the difference between node and mapper?

[@sajjaduddin8188]

Thanks for the explanation

[@shahulvellore4594]

A very nice explanation and well presented with quality presentation materials.. thanks..

[@asimbaki]

That's not simple example.

[@abdelrahmanelkadi9343]

How? What exactly did you not understand?

[@4a6vamsi]

Good one.But where are filtering the data for 2015.I do not see explanation for this in the video?

[@lakshay510]

This is the Best Explanation.

[@pushpakgosavi9697]

Excellent... Example with drawing

[@SanjithMohan7]

I cringe a little bit everytime you say amaze-on. It's Amazon.. like the rainforest. Otherwise nice video, well explained

[@dadduworld]

ideas are bulletproof

[@rajivjha123]

what the hell . such basic things are presented with such complexity and non technical ?

[@T-Radi]

A lot of concepts these days are just over-complicated with fancy names, but when you look into them, they are very simple ideas.

[@barsvelioglu2276]

This is really nice explanation for the concept

[@simranarora4902]

nice video... well explained

[@rachanamunot483]

Good explaination but... plz will you provide & explain mapreduce using Mongodb and its example

[@prasannas6986]

👍🏼👍🏼👍🏼

[@shiva_tharun]

Thank you!! Really good explanation! :)

[@junkingjunking8477]

Did not get the concept. Not good for the desc of video.

[@8bit_hero850]

that's not simple....

[@brianlora9073]

wordcounter

[@Reet_triple_charm]

"Big Data Trunk" .. please change title of video to a "stupid video on many counts "

[@raghupatrunihemanth4908]

this video will not help

[@andrewsvega2023]

I didnt understand shit!!!!

[@soumyaranjanmohapatrasoumya015]

Very confusing explanation. :(

[@PerryCodes]

Amayzon?

[@mominazhar7275]

reduzers

[@tula__]

Accent it is.

[@badrounetbadr8732]

haters shut up