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Wonderful explanation

Really appreciate your hard work. Thank you for the great explanation.

Hello Shreya, Can you make a video of hand on Data ingestion in AWS S3?

Can Node/Thread have more partition than no of executors, if yes where the no of partition information will be stored.

Very good session mam if it was a practically show means it is very useful. thank you for your efforts

Thank you for this video.

Getting confidence in spark because of you only. Thanks so so much!

To summarize, what the Datamarts are for a DataWarehouse, same are the DataMesh for a DataLake

How did you make such a good visual explanation? Which tool you used to draw sketches ? Pls guide 🙏

Data mesh and snowflake same..? Data mesh and microsoft fabric same?

Thanks, appreciate it.. is there a plan to post practical videos around spark performance tuning?

Thank you for sharing your thoughts.

this is not excatly asnwer

Please do videos with sample data sets so that it would help for hands on

Seems it is PAAS as mentioned on Microsoft website

Really Thanks to Good And Indepth Explantion

lets say there is no change in records for the next day.. then.. does the data gets overwrite again?? with same records..??

This is excellent and valuable knowledge sharing... Easily one can make out these trainings are coming out of personal deep hands-on experience and not the mere theory ..Great work

Thank you, pls also post some practical videos around the same topic

Thank you for sharing thoughts

First one to monitor the notification from you

how i join small table with big table but i want to fetch all the data in small table like the small table is 100k record and large table is 1 milion record df = smalldf.join(largedf, smalldf.id==largedf.id , how = 'left_outerjoin') it makes out of memory and i cant do broadcast the small df idont know why what is best case here pls help

18/april/2024

your videos on spark are hidden gems

❤

❤

Nice explanation

At the start of the video i was so happy seing all the diagrams.. Later got fully confused & felt complicated and i didnt understand well 😢

I wish I could give 1000 likes. You’re an excellent teacher!

Nice explaination

found it helpful. You may go slower though. I had to stop and rewind few times.

what a wonderfull explanation to the point... thank you

Good playlist for Spark ruclips.net/p/PL1RS9FR9qIPEAtSWX3rKLVcRWoaBDqVBV

Just woow, very simple explanation of a complex cluster overview.. Thanks.

best explanation ever i came across on RUclips. watching all the parts .... Thank you for explaining it so smoothly.

Thank you for sharing thoughts!

That was very well explained. Thank you for putting this together. One question though, do you really think data modelling should be done on the Gold layer? I don't think so because Gold datasets are just busineess level aggregates suited to particular business consumption needs. Whereas Silver layer is the warehouse in Lakehouse. That is where modelling should be done, if needed.

Thank you so much.. all the vdos are very much clear and effective.

Thank you for sharing your thoughts.

Finally it got cleared to me after reading here and there . thank you .

one of the helpful session !

Nicely explained, thank you ..looking forward to learn more around this topic

well explained!!!

Well explained!!!

well explained!!

well explained

Nicely explained and thanks. helping a lot

kindly do similar simple thing for dataproc also bigquery.

Thank you for the detailed explanation. However the problems that I faced with reading dates prior to 1900, does not resolve even after setting all the mentioned properties. Does any one have a working example that solved the issue of reading dates prior to 1900. Below is the code that I added but did not work. conf = sparkContext.getConf() conf.set("spark.sql.legacy.parquet.datetimeRebaseModeInRead", "CORRECTED") conf.set("spark.sql.legacy.parquet.datetimeRebaseModeInWrite", "CORRECTED") conf.set("spark.sql.datetime.java8API.enabled", "true")

Very good information 🎉