This is the best interview till now. He worked on security , CDC concepts etc. Thanks sumit sir for conducting these interviews. Really helpful who is preparing for the interviews.
I am getting prepared for the interview next week. As you said failed interview better then not attending interview and these mock interviews giving me the confidence to face the interview. Great initiative, learning and help. Thanks!
This is one of the best interviews and got good insights Sumit sir.Learnt the new concept of thin executor and fat executor and his optimisation techniques are also unique like join re order Adq etc…
Hey Thank you Sumit Mittal, This is been helping me, I am about watched 2 interviews from your channel. Been aspiring to switch into data engineering, from a Business/Analyst/Consultant role. I think watching these interviews is giving me a lot of confidence, to start applying.
These videos have been very helpful. I have gone over many and this would be beneficial for a long time. Great work, initiative and thanks for the efforts.
I have 2 queries, he has mentioned he made changes at code level by changing the joins to broadcast if possible, But in databricks AQE is enabled and it does the job for us. 2nd, he has mentioned bucketing is not available in databricks, is it correct?
Very nice interview sir can we have an interview for someone who is having more than 5 years of experience and is looking to join as an Architect level or Team Lead, Manager in Data Engineering world.
I would like to know the answer for the question, "why in spark why we will get duplicate of the column on which join is performed, whereas in sql we do not get that duplicate ?" From my limited knowledge, I know that in both spark and sql joins we will get the duplicate columns when join is performed on two tables. I would really appreciate it if you could please let me know the correct answer.
The reason behind this lies in the fundamental differences between Spark and traditional SQL databases. Spark performs parallel processing to handle partitioned data distributed across different nodes in the cluster. It is inevitable that there would be duplicates while performing joins on such datasets in this context. In contrast, traditional SQL databases usually operates on a single machine or a tightly coupled cluster and ensures uniqueness by preventing the duplicated from being generated in the final results
This is the best interview till now. He worked on security , CDC concepts etc. Thanks sumit sir for conducting these interviews. Really helpful who is preparing for the interviews.
Ram is one of the best candidates of the series.
All the best Ram.
yes definitely, he has a great exposure
I am getting prepared for the interview next week. As you said failed interview better then not attending interview and these mock interviews giving me the confidence to face the interview. Great initiative, learning and help. Thanks!
Sumit sir you are a legend in BIGDATA...
You are the best trainer I've seen so far... Thanks for making the bigdata enthusiastics life easier....
Always happy to help!
This is one of the best interviews and got good insights Sumit sir.Learnt the new concept of thin executor and fat executor and his optimisation techniques are also unique like join re order Adq etc…
This serires is great and gives me confidence to and sit in the interview.
Definitely. It is a great help sir.
Hey Thank you Sumit Mittal,
This is been helping me, I am about watched 2 interviews from your channel.
Been aspiring to switch into data engineering, from a Business/Analyst/Consultant role.
I think watching these interviews is giving me a lot of confidence, to start applying.
best interview till now
absolutely
These videos have been very helpful. I have gone over many and this would be beneficial for a long time. Great work, initiative and thanks for the efforts.
Been watching the interviews everyday. Definitely helpful. IMO based on the performance of the interviewee feedback at the end would be great.
we will add it in upcoming ones
You are doing great work. Lot of respect
Sumit - these mock interviews are worth it's weight in gold.
Best interview, every interviewer should give feedback at the end. it can be more helpful.
noted the suggestion, will incorporate it from the upcoming interviews
What an amazing interview. This series is so helpful. Thanks sumit sir
Keep watching for more such engaging interviews
Thanks for conducting these mock interviews Sumit sir. It is really helpful😊
The interview series is really helpful, Thank you
Really helpful for all Data engineer
Happy to know that you are finding the interview series helpful
The interviews are very helpful…
Thank you Sumit and Team🎉🎉
Glad to hear that you found the mock interviews insightful. More such sessions are scheduled for release in the upcoming days.
The series is amazing , I am learning a lot. Please keep making it
Happy to hear that!
It just wonderful task... Its very much helpful ❤❤❤❤❤
Thank you sumit sir and team, your hard work really means a lot
I have 2 queries, he has mentioned he made changes at code level by changing the joins to broadcast if possible, But in databricks AQE is enabled and it does the job for us.
2nd, he has mentioned bucketing is not available in databricks, is it correct?
Great work , Thankyou
Keep up the good work
Thanks for sharing. Very helpful
Glad it was helpful! Keep watching for more such interesting content
It was really effective to us very very helpfull
Happy to know that you found the interview helpful
Most of the interviews seem to focus on junior and mid-level DE. I’d also appreciate some video for Senior DE role.
let me know if you would like to attend?
Thank you sir , this videos are very helpful
What is the common salary range for Big data developer (4yrs) ?
Thanks for the interview series!!
glad that you liked it
Very nice interview sir can we have an interview for someone who is having more than 5 years of experience and is looking to join as an Architect level or Team Lead, Manager in Data Engineering world.
Definitely, there would be many more such videos released as part of this interview series. Will try to consider all possible scenarios
Gr8 effort 🌟
this is very helpful series
Thank you as always 🙏🏻
Always happy to share good content for all the data enthusiasts
Very informative.
thank you
Sumit Sir, Please share any mock interview which will cover Azure cloud alone if possible
As soon as possible, noted
I would like to know the answer for the question, "why in spark why we will get duplicate of the column on which join is performed, whereas in sql we do not get that duplicate ?"
From my limited knowledge, I know that in both spark and sql joins we will get the duplicate columns when join is performed on two tables.
I would really appreciate it if you could please let me know the correct answer.
The reason behind this lies in the fundamental differences between Spark and traditional SQL databases. Spark performs parallel processing to handle partitioned data distributed across different nodes in the cluster. It is inevitable that there would be duplicates while performing joins on such datasets in this context. In contrast, traditional SQL databases usually operates on a single machine or a tightly coupled cluster and ensures uniqueness by preventing the duplicated from being generated in the final results
@@sumitmittal07Thankyou so much ! That really makes sense now.
Very helpful video.... gr8 help ...
Realistic interview ❤
glad that you enjoyed it
Very helpful ❤
Glad it was helpful!
Great one 🙌
Very helpful
Too technical man🔥
Best
It was awesome
Glad that you are finding the mock interviews informative.
Good one!!
I would like to attend interview, let me know what is the process?
Please fill this form - forms.gle/UMpNCZvAHgoLvvuJ6