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Top Big Data Interview Questions asked in 2024 | Cloud Data Engineer | Azure | Spark | SQL 

Sumit Mittal
ะŸะพะดะฟะธัะฐั‚ัŒัั 118 ั‚ั‹ั.
ะŸั€ะพัะผะพั‚ั€ะพะฒ 16 ั‚ั‹ั.
50% 1

๐“๐จ ๐ž๐ง๐ก๐š๐ง๐œ๐ž ๐ฒ๐จ๐ฎ๐ซ ๐œ๐š๐ซ๐ž๐ž๐ซ ๐š๐ฌ ๐š ๐‚๐ฅ๐จ๐ฎ๐ ๐ƒ๐š๐ญ๐š ๐„๐ง๐ ๐ข๐ง๐ž๐ž๐ซ, ๐‚๐ก๐ž๐œ๐ค trendytech.in/... for curated courses developed by me.
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 -
๐‘๐ž๐ ๐ข๐ฌ๐ญ๐ซ๐š๐ญ๐ข๐จ๐ง ๐‹๐ข๐ง๐ค (๐‚๐จ๐ฎ๐ซ๐ฌ๐ž ๐€๐œ๐œ๐ž๐ฌ๐ฌ ๐Ÿ๐ซ๐จ๐ฆ ๐ˆ๐ง๐๐ข๐š) : rzp.io/l/SQLINR
๐‘๐ž๐ ๐ข๐ฌ๐ญ๐ซ๐š๐ญ๐ข๐จ๐ง ๐‹๐ข๐ง๐ค (๐‚๐จ๐ฎ๐ซ๐ฌ๐ž ๐€๐œ๐œ๐ž๐ฌ๐ฌ ๐Ÿ๐ซ๐จ๐ฆ ๐จ๐ฎ๐ญ๐ฌ๐ข๐๐ž ๐ˆ๐ง๐๐ข๐š) : rzp.io/l/SQLUSD
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, Ganesh Ramdas Kudale, / ganesh-kudale-50bb14ab shares invaluable insights and practical guidance drawn from his extensive expertise in the Big Data Domain.
Our expert guest interviewee, Prithvi Salve, / prithvi-salve-45545a1ba has an interesting approach to answering the interview questions on Apache Spark, SQL and Azure Cloud Services.
Link of Free SQL & Python series developed by me are given below -
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Social Media Links :
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TIMESTAMPS : Questions Discussed
01:00 Introduction
01:47 What is Hadoop and how does it work?
03:09 Why move from MapReduce to Spark?
05:07 Does Spark provide storage?
05:47 Give a high-level explanation of Spark.
06:50 Why switch from RDDs to DataFrames in Spark?
07:53 Which languages does Spark support?
08:27 What are RDDs and their importance?
09:47 What happens during actions/transformations in Spark?
11:15 Explain Spark architecture.
13:06 What are deployment modes and their use cases?
14:30 Describe the plans created when executing a Spark job.
16:00 What is a predicate push down?
18:10 Explain jobs, stages, and tasks in Spark.
19:10 What are the types of transformations in Spark?
20:38 Difference between repartition and coalesce?
23:30 Should you infer schema or specify it when creating a DataFrame?
24:19 What are the ways to enforce schema? Provide an example.
24:54 SQL coding questions
41:09 Which Azure cloud services have you used?
41:35 Explain Databricks architecture at a high level.
42:40 How do you run SQL queries in Databricks?
44:10 How can one notebook run another in Databricks?
45:35 Can you use parameters when running Databricks notebooks?
46:07 Difference between Data Lake and Delta Lake? Pros and cons of each.
48:11 What activities are available in ADF?
49:09 Scenario-Based question
Music track: Retro by Chill Pulse
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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

ะžะฟัƒะฑะปะธะบะพะฒะฐะฝะพ:

 

21 ะฐะฒะณ 2024

ะŸะพะดะตะปะธั‚ัŒัั:

ะกัั‹ะปะบะฐ:

ะกะบะฐั‡ะฐั‚ัŒ:

ะ“ะพั‚ะพะฒะธะผ ััั‹ะปะบัƒ...

ะ”ะพะฑะฐะฒะธั‚ัŒ ะฒ:

ะœะพะน ะฟะปะตะนะปะธัั‚
ะŸะพัะผะพั‚ั€ะตั‚ัŒ ะฟะพะทะถะต
ะšะพะผะผะตะฝั‚ะฐั€ะธะธ : 18   
@rishabhkesarwani-br2rx
@rishabhkesarwani-br2rx 3 ะผะตััั†ะฐ ะฝะฐะทะฐะด
The guy answered very well ! Got the good idea on what to say and what to avoid during interview
@gudiatoka
@gudiatoka 3 ะผะตััั†ะฐ ะฝะฐะทะฐะด
16:53 Broadcast join decided on the go or run time which is by Adaptive Query Execution not spark sql engine or catalytic optimizer as said
@gudiatoka
@gudiatoka 3 ะผะตััั†ะฐ ะฝะฐะทะฐะด
When ever transformation applied it never created a dag rather than it created a lineage between rrds and action created a DAG
@lazzybirdflying3225
@lazzybirdflying3225 ะœะตััั† ะฝะฐะทะฐะด
Though it is a mock interview, I appreciate his calm and pleasant responses to all the questions!
@shaileshchile329
@shaileshchile329 2 ะผะตััั†ะฐ ะฝะฐะทะฐะด
Thanks for the videos. It's very helpful!
@kashamp9388
@kashamp9388 3 ะดะฝั ะฝะฐะทะฐะด
basically, well interview
@shrikantkorate5933
@shrikantkorate5933 2 ะผะตััั†ะฐ ะฝะฐะทะฐะด
he answered to the point most of the questions very good
@user-nc9nt9nw5w
@user-nc9nt9nw5w ะœะตััั† ะฝะฐะทะฐะด
The million dollar question is...."Is he selected"..??? and how did he do in the 2nd round..??..2nd round questions please..
@junaid20950
@junaid20950 23 ะดะฝั ะฝะฐะทะฐะด
this is a demo QnA just for our understanding what questions are asked in DE interview btw he got selected in Deloitte with 120% hike cheers ๐ŸŽ‰
@ravulapallivenkatagurnadha9605
@ravulapallivenkatagurnadha9605 3 ะผะตััั†ะฐ ะฝะฐะทะฐะด
Continue this series
@axatdewangan
@axatdewangan 2 ะผะตััั†ะฐ ะฝะฐะทะฐะด
Great answers!
@Nalaka-Wanniarachchi
@Nalaka-Wanniarachchi 3 ะผะตััั†ะฐ ะฝะฐะทะฐะด
Well scored.
@hdr-tech4350
@hdr-tech4350 ะœะตััั† ะฝะฐะทะฐะด
Java used in Hadoop Bound to work on mapreduce Can only work on batch process not real time in map reduce
@suvenduku2
@suvenduku2 ะœะตััั† ะฝะฐะทะฐะด
Sir pls provide the questions in description
@hdr-tech4350
@hdr-tech4350 ะœะตััั† ะฝะฐะทะฐะด
Spark core -Rdd (flexible) high level apis- Df and Spark sql (easy to write query) Transformation n action Spark submit process Deployment modes Types of transformation Repartition n coalesce Methods for schema enforcement - ddl, struct Consecutive wins in sql
@jithindev9185
@jithindev9185 2 ะผะตััั†ะฐ ะฝะฐะทะฐะด
๐Ÿ‘๐Ÿ‘๐Ÿ‘๐Ÿ‘
@RohitSharma-ny1oq
@RohitSharma-ny1oq 3 ะผะตััั†ะฐ ะฝะฐะทะฐะด
Good explanation men๐Ÿ˜…
@rushirajkadge3995
@rushirajkadge3995 ะœะตััั† ะฝะฐะทะฐะด
The row_number values for marks are not correct (35:16). The correct output is: Marks Row_number 100 1 100 2 99 1 98 1 98 2 98 3 97 1 96 1 95 1
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