10 frequently asked questions on spark | Spark FAQ | 10 things to know about Spark

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  • Опубликовано: 31 дек 2024

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  • @nikhilgupta110
    @nikhilgupta110 2 года назад +14

    I'm impressed , no copy paste , Covered whole architecture and done full justice to the title. Great work ,you are heading in the right direction, keep going :).

  • @vaibhavjoshi6853
    @vaibhavjoshi6853 6 месяцев назад +2

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

  • @sachinragde
    @sachinragde 2 года назад +1

    Thank you ma'am u explain it very nicely due to u I understand it
    And ur voice is very beautiful
    Thank you so much

  • @rajeshd3940
    @rajeshd3940 2 года назад +2

    quite formative for beginners and Appreciate the efforts made

  • @iwonazwierzynska4056
    @iwonazwierzynska4056 Год назад +1

    The next great video

  • @dhavaldalasaniya
    @dhavaldalasaniya 2 года назад +1

    Very nice !! Simple & very knowledgeable 👍🏻

  • @ramkumarananthapalli7151
    @ramkumarananthapalli7151 2 года назад +2

    Thanks for making this video. Kindly make more videos on big data engineering.

  • @sathwikemmadi4428
    @sathwikemmadi4428 2 года назад +3

    Great content shreya ,I really like all the videos and wanted to learn more about datalakes and best practices designing datalakes.If you have some time and bandwidth please keep a post on the above.
    Thanks for all your hardwork.

    • @BigDataThoughts
      @BigDataThoughts  2 года назад +1

      Thanks sathwik.
      Will surely do. Have posted one on what are data lakes ruclips.net/video/wHG0ljN3plg/видео.html
      Will post on best practices too.

    • @BigDataThoughts
      @BigDataThoughts  2 года назад

      This week have posted on data lake best practices

  • @harshmohan8419
    @harshmohan8419 2 года назад +2

    Mam you are awesome.. teaching talent with example is like god gifted us.....
    Mam, can u briefly tell me if we use yarn, then what would be the role of the cluster manager and its application master in spark.. because driver directly communicating to executor

  • @khajaabdulmujeebbasha6327
    @khajaabdulmujeebbasha6327 2 года назад

    mam there is shuffle in coalese even it is considering as narrow transformation why?

  • @angelangeli2089
    @angelangeli2089 2 года назад +1

    good explanation and easy!! :)

  • @geetakadam6728
    @geetakadam6728 2 года назад

    Hello Mam I had gone through your video the way you have delivered is very nice.
    I have one dought though silly but I wants to know in local system that means on our laptop when we execute pyspark code who is executor and who is worker and on cloud please explain me executor and worker?
    Thanks in advance

  • @guptaashok121
    @guptaashok121 2 года назад

    Is it driver which reads all data and divide in partition and sends to executor?

    • @BigDataThoughts
      @BigDataThoughts  2 года назад

      Yes executors job is to just execute.

    • @guptaashok121
      @guptaashok121 2 года назад

      @@BigDataThoughts will it not create bottleneck..

    • @BigDataThoughts
      @BigDataThoughts  2 года назад

      @@guptaashok121 no it won't be cos actual data resides on executors, driver is just the coordinator. while assigning task principal of data locality is kept in mind.

    • @guptaashok121
      @guptaashok121 2 года назад

      @@BigDataThoughts that's what my doubt is, if driver is not reading the data then how it sends command to executor on which data to work on

  • @samirharris1
    @samirharris1 Год назад +1

    Thankyou 😊

  • @SagarSingh-ie8tx
    @SagarSingh-ie8tx 2 года назад +2

    Nice

  • @winfred4193
    @winfred4193 2 года назад +1

    Love it!! Become an online boss - Promo-SM !!

  • @mithunnambiar1433
    @mithunnambiar1433 2 года назад +1

    This is what you call WOMEN IN TECH.. absolutely fantastic!!! 💩

  • @ranvijaymehta
    @ranvijaymehta Год назад +1

    Thanks mam ... Today I have subscribed BigData Thoughts

  • @techmahesh9560
    @techmahesh9560 Год назад +1

    Nice