narrow and wide transformation in spark | | Operations in Pyspark RDD | Pyspark tutorials - 6

#PySpark
This is Sixth Video with a explanation of Pyspark RDD Narrow and Wide Transformations Operations.
i have covered below Transformations in this video:
Narrow : Map,Filter,flatmap,filter,sample,union
Wide : Intersection,Join,Distinct.
Stay tuned for Part 6 Video of Key Value Pair RDD Transformations in PYSPARK
BIG DATA IS PROBLEM and HADOOP IS A SOLUTION
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[@marvhan888]

why everybody complaining about the speed. this is just perfect for me

[@sanskarram992]

Your content is amazing for begineers.Thanks for such a wonderful content.

[@RaviSahani-zc9zj]

Best explanation for beginners. On KZbin and Udemy.

[@tesfayesusyimenu3292]

Superb explanation!

[@rahulchawla6696]

nice explanation thank you

[@ishtigokak3526]

hey ranjan amazing content... great job keep it up

[@pranjalgupta9427]

Thanks❤

[@ThaoHoang-ym9fu]

Thank you for the comprehensive coverage. The speed is a little too fast to follow. Thanks again.

[@RanjanSharma]

Sorry about that I have decreased the pace in latest videos. thanks for exploring :)

[@akzhere]

Could you please zoom-in on cells a little while running them for better visuals :) thanks!

[@RanjanSharma]

Sure will do in my further videos

[@agyaatkavi]

Thanks :)

[@Ghumakkars_world]

it would have been easy to go to next video if 2nd video link was provided at the end of the video as provided in earlier video's.

[@RanjanSharma]

ok I will do the same. Thanks for Exploring

[@raghavagrawal6263]

Very nice lecture. The only request is to please speak a little bit slow means take a breath in between.

[@infinioda108]

Please please add few more videos, like a small project or something else.

[@Ghumakkars_world]

20:04 -- MS paint explaination of num - num2 & num2 - num is wrong... its just the other way around. (as we got the result in the jupiter notebook)

[@RanjanSharma]

I did not get you. i am getting the same answer on paint and jupyter notebook. i.e. 2,2,5,5,3 and 10,1,7,15. Please let me know if i am seeing other way. I might be wrong.

[@FoodBoxByLalima]

​@@RanjanSharma A-B means All elements of A which not present in B. so num -num2 = (5,5,3,2,2) similarly num2 -num = (1,7,10,15) in jupyter notebook num.separate(num2) means num-num2, so output is (5,5,3,22) which is different in your MS paint.

[@sohammajumder4379]

Yes.. I agree with you.. the paint explanation should be vice versa.. when you are saying num2 - num, num 2 is your parent and you should remove the common element between num and num2 from num2.

[@Anokhetoons]

y so fasstttttt??

[@faizahmed9276]

whenever I'm applying the num lambda func it's shhowing error in Py4JJavaError Please help

[@mindfulcreativity8613]

I don't think coalesce requires shuffling

[@RanjanSharma]

yes Correct, Coalesce is Narrow transformation and Repartition is Wide Transformation

[@sivakrishna3413]

Except speed. Everything is good. Explanation is too much speed

[@RanjanSharma]

thanks for your feedback ...I tried to keep the video length short. I will try to reduce the pace of the video.. Keep Exploring other Videos and Keep sharing to your friends and colleagues Join Whatsapp Group for AI chat.whatsapp.com/L3Zmt9XBa3UCccPfekYvXm Telegram Group : www.t.me/@MachineLearningIndia

[@brijesh0808]

is it necessarily to be RDD @0:57 or dataframe can be considered here too?

[@harshaharyani2501]

What does seed in sample() transformation means?

[@shubhamvashisth9518]

Seed is a random state, it can have any value but the notion behind using seed is, if you use the same seed for your experimentation, it will generate the same values while using random functionality.

[@JD-xd3xp]

Dude, you are running too fast, are you racing against Rajdhani Express, you need to speak slowly.

[@RanjanSharma]

I have decreased the pace in my latest videos

[@phanikumar3136]

could u plzz attach notebook file

[@RanjanSharma]

Attached now ... drive.google.com/drive/u/1/folders/1yRsR1BsXJCcmmC5IOfexp2STFDL33gYL

[@rajatsharma6137]

I dont get the point of demo on single machine....if we are running it on local machine...what is the point of distributed computation paradigm? Please show a demo where several machines are used and then operations are carried on distributed data...