Live Data Engineering Coding Round Mock Interview | Apache Spark | Big Data Project
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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
Our highly experienced guest interviewer, Solon Kumar Das, / solondas shares invaluable insights and practical guidance drawn from his extensive expertise in the Big Data Domain.
Our expert guest interviewee, Dhruv Dubey, / dhruvadubey has an interesting approach to answering the interview questions on Spark, SQL & Big Data Project.
Link of Free SQL & Python series developed by me are given below -
SQL Playlist - ⢠SQL tutorial for every...
Python Playlist - ⢠Complete Python By Sum...
Don't miss out - Subscribe to the channel for more such informative interviews and unlock the secrets to success in this thriving field!
Social Media Links :
LinkedIn - / bigdatabysumit
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Student Testimonials - trendytech.in/#testimonials
TIMESTAMPS : Questions Discussed
00:00 - Introduction
02:04 - Project Responsibilities
09:37 - Spark architecture
11:25 - When does shuffling happen? Before you trigger the groupBy or after?
12:23 - Major challenges you faced in your project
16:25 - Performance tuning techniques
18:15 - When do we use Partitioning and bucketing
20:02 - MapReduce drawbacks
21:38 - When do we use caching and when do we use persist
24:24 - Memory management in Spark
26:41 - Difference between DataFrame and Dataset
27:26 - File types in Spark
31:10 - Coding questions
Music track: Retro by Chill Pulse
Source: freetouse.com/music
Background Music for Video (Free)
Tags
#mockinterview #bigdata #career #dataengineering #data #datascience #dataanalysis #productbasedcompanies #interviewquestions #apachespark #google #interview #faang #companies #amazon #walmart #flipkart #microsoft #azure #databricks #jobs
talented youngsters, good questions & good answers, learnt a lot, thank you very much :)
Thank you ð
select id,dept,salary,
avg(Salary) over(partition by dept) as avg_sal,
dense_rank() over(partition by dept order by salary desc) as rn
from emp100
job --> stages --> tasks , dag scheduler, task scheduler missing in explanation
for ele in s.split(' '):
if ele in d:
d[ele]+=1
else:
d[ele]=1
for key,val in {i : d[i] for i in sorted(d,key=d.get,reverse=True)}.items():
print(f'{key}|{val}')
S = """Data engineering is a good skill
Data is a new Oil
Data Enginering skill is massive"""
words = S.split()
dict1 = {}
for i in words:
dict1[i] = words.count(i)
print(dict1)