Big Data Mock Interview | DSA | APACHE SPARK | PYTHON | DATABRICKS #interview

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ะŸะพะบะฐะทะฐั‚ัŒ ะพะฟะธัะฐะฝะธะต

I have trained over 20,000+ professionals in the field of Data Engineering in the last 5 years.

๐–๐š๐ง๐ญ ๐ญ๐จ ๐Œ๐š๐ฌ๐ญ๐ž๐ซ ๐’๐๐‹? ๐‹๐ž๐š๐ซ๐ง ๐’๐๐‹ ๐ญ๐ก๐ž ๐ซ๐ข๐ ๐ก๐ญ ๐ฐ๐š๐ฒ ๐ญ๐ก๐ซ๐จ๐ฎ๐ ๐ก ๐ญ๐ก๐ž ๐ฆ๐จ๐ฌ๐ญ ๐ฌ๐จ๐ฎ๐ ๐ก๐ญ ๐š๐Ÿ๐ญ๐ž๐ซ ๐œ๐จ๐ฎ๐ซ๐ฌ๐ž - ๐’๐๐‹ ๐‚๐ก๐š๐ฆ๐ฉ๐ข๐จ๐ง๐ฌ ๐๐ซ๐จ๐ ๐ซ๐š๐ฆ!

"๐€ 8 ๐ฐ๐ž๐ž๐ค ๐๐ซ๐จ๐ ๐ซ๐š๐ฆ ๐๐ž๐ฌ๐ข๐ ๐ง๐ž๐ ๐ญ๐จ ๐ก๐ž๐ฅ๐ฉ ๐ฒ๐จ๐ฎ ๐œ๐ซ๐š๐œ๐ค ๐ญ๐ก๐ž ๐ข๐ง๐ญ๐ž๐ซ๐ฏ๐ข๐ž๐ฐ๐ฌ ๐จ๐Ÿ ๐ญ๐จ๐ฉ ๐ฉ๐ซ๐จ๐๐ฎ๐œ๐ญ ๐›๐š๐ฌ๐ž๐ ๐œ๐จ๐ฆ๐ฉ๐š๐ง๐ข๐ž๐ฌ ๐›๐ฒ ๐๐ž๐ฏ๐ž๐ฅ๐จ๐ฉ๐ข๐ง๐  ๐š ๐ญ๐ก๐จ๐ฎ๐ ๐ก๐ญ ๐ฉ๐ซ๐จ๐œ๐ž๐ฌ๐ฌ ๐š๐ง๐ ๐š๐ง ๐š๐ฉ๐ฉ๐ซ๐จ๐š๐œ๐ก ๐ญ๐จ ๐ฌ๐จ๐ฅ๐ฏ๐ž ๐š๐ง ๐ฎ๐ง๐ฌ๐ž๐ž๐ง ๐๐ซ๐จ๐›๐ฅ๐ž๐ฆ."

๐‡๐ž๐ซ๐ž ๐ข๐ฌ ๐ก๐จ๐ฐ ๐ฒ๐จ๐ฎ ๐œ๐š๐ง ๐ซ๐ž๐ ๐ข๐ฌ๐ญ๐ž๐ซ ๐Ÿ๐จ๐ซ ๐ญ๐ก๐ž ๐๐ซ๐จ๐ ๐ซ๐š๐ฆ -

30 INTERVIEWS IN 30 DAYS- BIG DATA INTERVIEW SERIES

This mock interview series is launched as a community initiative under Data Engineers Club aimed at aiding the community's growth and development

Link of Free SQL & Python series developed by me are given below -

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TIMESTAMPS : Questions Discussed
00:00 Introduction
02:00 Puzzle question
03:40 5th highest salary in a table
04:17 dense_rank
04:50 How to find duplicates in a table
05:33 lead and lag
06:26 What is datafactory? How is it different from databricks?
07:28 Use of datafactory and databricks in orchestrating the etl workflows
09:17 What is Hive metastore?
10:30 How would you design a highly available and scalable architecture in azure databricks?
12:19 Difference between spark session and spark context
13:02 storage service on azure
15:04 Scenario based questions - SQL
18:53 Types of joins in pyspark
21:29 types of triggers
22:10 Difference between rdd and dataframe,datasets
23:29 Spark architecture
26:55 Drawbacks of mapreduce
28:17 Difference between list and tuples,Dictionaries
29:07 lambda functions in python
29:40 Question based on spark architecture

Music track: Retro by Chill Pulse
Background Music for Video (Free)

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#mockinterview #bigdata #career #dataengineering #data #datascience #dataanalysis #productbasedcompanies #interviewquestions #apachespark #google #interview #faang #companies #amazon #walmart #flipkart #microsoft #azure #databricks #jobs
  1. Sumit Mittal
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ะšะพะผะผะตะฝั‚ะฐั€ะธะธ
ะะฒั‚ะพั€

last question: worker node config should smaller compare to driver node. as spark works on distributed architecture, also cost comes to picture

CctnsHelpdesk
ะะฒั‚ะพั€

for quiz question how come chocolates are coming as 22 can someone plz explain
my understanding 15 coins = 15 chocolates
3 wrapper = 1chocolate
then 15/3 = 5 chocolate
5 chocolate = 5 wrapper = 1 chocolate
15+5+1 = 21 chocolate

divyaborse