Data Engineer Mock Interview | SQL | PySpark | Project & Scenario based Interview Questions
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- Опубліковано 16 вер 2024
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𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐫𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 -
𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤 (𝐂𝐨𝐮𝐫𝐬𝐞 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐫𝐨𝐦 𝐈𝐧𝐝𝐢𝐚) : rzp.io/l/SQLINR
𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤 (𝐂𝐨𝐮𝐫𝐬𝐞 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐫𝐨𝐦 𝐨𝐮𝐭𝐬𝐢𝐝𝐞 𝐈𝐧𝐝𝐢𝐚) : rzp.io/l/SQLUSD
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, Ankur Bhattacharya, / ankur-bhattacharya-100... shares invaluable insights and practical advice coming from his extensive experience, catering to aspiring data engineers and seasoned professionals alike.
Our talented guest interviewee, Praroop Sacheti, / praroopsacheti has a remarkable approach to answering the interview questions in a very well articulated manner.
Link of Free SQL & Python series developed by me are given below -
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Discussed Questions : Timestamp
1:30 Introduction
3:29 When you are processing the data with databricks pyspark job. What is the sink for your pipeline?
4:58 Are you incorporating fact and dimension tables, or any schema in your project's database design?
5:50 What amount of data are you dealing with in your day to day pipeline?
6:33 What are the different types of triggers in ADF?
7:45 What is incremental load ? How can you implement it through ADF ?
10:03 Difference between Data Lake and Data Warehouse?
11:41 What is columnar storage in a data warehouse ?
13:38 What were some challenges encountered during your project, and how were they resolved? Describe the strategies implemented to optimize your pipeline?
16:18 Optimizations related to Databricks or pyspark ?
20:41 What is broadcast join ? What exactly happens when we broadcast the table ?
23:01 SQL coding question
35:46 PySpark coding question
Tags
#mockinterview #bigdata #career #dataengineering #data #datascience #dataanalysis #productbasedcompanies #interviewquestions #apachespark #google #interview #faang #companies #amazon #walmart #flipkart #microsoft #azure #databricks #jobs
For incremental laod why we go about MERGE or UPSERT. MERGE or UPSERT we use to implement SCD types. For incremental load what we want is to copy newly arrived data in ADLS. For which we keep track of some reference key, through which we can recognize the new data. For example, in an Order fact table lets say it is Order_ID which keeps on increasing whenever we get a new order.
Please attach the questions list link(in view mode) that are asked in mock interview in description
Please provide the interview feedback in few mins at the end to help more with this.
Good initiative. This is quite helpful on how to answer the scenario based questions, with an example. Thank you sir, Ankur and Praroop! 🙌
So nice of you
we need to controll flow with cfg file for incremental dataload
not merge or upsert .
Great Initiative Sumit Sir !
thank you. A big thanks to people who are participating in this.
Sir please make videos on topics like " Someone working in Tech Support from past 5 years and now moving to Data Engineer" What they should write in their resume like in experience section... Whether should give try as fresher or whatever
Sir, I also have the same question.
Yes that is very valuable. As most of the people are working in different roles but being in support roles in data field we are interested to switch into data engg.
surely will release a video on this soon
great content! very insightful questions and answers!
Glad you enjoyed it!
Great video for new data engineers like me.
Glad you enjoyed it
Please also some video regarding what kinds of problems data engineer face in their day to days working
noted, will bring a video on this soon
Please make videos for freshers as well, because these days no one is looking for freshers for data engineering roles...
will make a video for sure
Hi Sir, Thanks for this series, very insightful. Just a query, does majority of the interviews goes till coding part or majority cases its theory only? or is it mix and match?
Yes they do
Hi Folks, below is the solution to the PySpark problem written in >>SCALA
thank you so much sumit sir its really helpful
Happy to share more such informative videos for the community!
Good Work Praroop ❤
Praroop has rocked it.
Hi Sir ,Request you to please upload more videos on Data engineer mock interview
one video daily for next 30 days
sir please make complete video on sql and mock interviews too
Definitely, will be covered in the upcoming videos
Please make interview session
for fresher.
surely
can u make a video for aws cloud as of azure
surely
I want to give mock interview.
wahh
😅
Really?
Solution for Pyspark Problem
def location_f(loc):
if loc == 'CHN':
return 'CHENNAI'
elif loc == 'AP':
return 'ANDHRA PRADESH'
elif loc == 'HYD':
return 'HYDERABAD'
else:
return loc
re_location = F.udf(location_f, StringType())
df1 = df.withColumn('ref_id1', F.split('ref_id','\DIV-|\_')).drop('ref_id')
df2 = df1.withColumn('ref_id', F.col('ref_id1')[2]).withColumn('location', re_location(F.col('ref_id1')[1]))
df3 = df2.select('name', 'ref_id', 'salary','location')
df3.show
from pyspark.sql.functions import col, lit,when
df_employee.withColumn("LOCATION",
when(col("REF-ID").like("DIV-CHN%"), "CHN-CHENNAI")
.when(col("REF-ID").like("DIV-HYD%"), "HYD-HYDERABAD")
.when(col("REF-ID").like("DIV-AP%"), "AP-ANDHRA PRADESH")
.when(col("REF-ID").like("DIV-PUNE%"), "PUNE-PUNE")).show()
df_new = df.select(col("name"),col("refid"),col("salary"),split("refid","-")[1].alias("l"),split("l","_")[0].alias("loc")).drop(col("l"))
final_result_df = df_new.withColumn("location",when(col("loc")=="CHN","CHENNAI")\
.when(col("loc")=="HYD","HYDERABAD")\
.when(col("loc")=="AP","ANDRA_PRADESH")\
.when(col("loc")=="PUN","PUNE") ).drop("loc")