Live Big Data Mock Interview | Techno Managerial

𝐓𝐨 𝐞𝐧𝐡𝐚𝐧𝐜𝐞 𝐲𝐨𝐮𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐚𝐬 𝐚 𝐂𝐥𝐨𝐮𝐝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫, 𝐂𝐡𝐞𝐜𝐤 https://trendytech.in/?src=youtube&su... for curated courses developed by me.

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

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐒𝐐𝐋? 𝐋𝐞𝐚𝐫𝐧 𝐒𝐐𝐋 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐰𝐚𝐲 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐬𝐨𝐮𝐠𝐡𝐭 𝐚𝐟𝐭𝐞𝐫 𝐜𝐨𝐮𝐫𝐬𝐞 - 𝐒𝐐𝐋 𝐂𝐡𝐚𝐦𝐩𝐢𝐨𝐧𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦!

"𝐀 8 𝐰𝐞𝐞𝐤 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 𝐝𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐜𝐫𝐚𝐜𝐤 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐨𝐟 𝐭𝐨𝐩 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐛𝐚𝐬𝐞𝐝 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐛𝐲 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐢𝐧𝐠 𝐚 𝐭𝐡𝐨𝐮𝐠𝐡𝐭 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐚𝐧𝐝 𝐚𝐧 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞 𝐚𝐧 𝐮𝐧𝐬𝐞𝐞𝐧 𝐏𝐫𝐨𝐛𝐥𝐞𝐦."

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐫𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 -
𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤 (𝐂𝐨𝐮𝐫𝐬𝐞 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐫𝐨𝐦 𝐈𝐧𝐝𝐢𝐚) : https://rzp.io/l/SQLINR
𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐢𝐧𝐤 (𝐂𝐨𝐮𝐫𝐬𝐞 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐫𝐨𝐦 𝐨𝐮𝐭𝐬𝐢𝐝𝐞 𝐈𝐧𝐝𝐢𝐚) : https://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, Chandrali Sarkar,   / chandrali-sarkar-4570a1102   shares invaluable insights and practical guidance drawn from her extensive expertise in the Big Data Domain.

Our expert guest interviewee, Akash Patel ,   / akash-patel-37158a107   has an interesting approach to answering the interview questions on Pyspark, Hive and SQL.

Link of Free SQL & Python series developed by me are given below -
SQL Playlist -    • SQL tutorial for everyone by Sumit Sir - T...  
Python Playlist -    • Complete Python By Sumit Mittal Sir  

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  
Twitter -   / bigdatasumit  
Instagram -   / bigdatabysumit  
Student Testimonials - https://trendytech.in/#testimonials

TIMESTAMPS : Questions Discussed
00:00 Introduction
01:12 PySpark and Azure integration for pipelines
02:44 Analytics setup and data warehousing
04:38 Configuring Spark job
06:21 Spark optimization
08:58 Shuffling avoidance techniques
10:22 Understanding and minimizing shuffling
11:04 Initial Spark job steps for shuffling reduction
12:40 Spark job partitions
13:47 CPU cores and partition relationship
16:55 Partitioning and bucketing use cases
20:00 Hash functions and tables
23:40 Decreasing partitions
24:23 Coalesce vs. repartition
25:14 Dealing with data skewness
26:06 Partition skew solutions
26:34 Salting purpose
27:35 Scenario-based question
30:01 Narrow and wide transformation examples
31:31 Spark's lazy evaluation
32:25 RDD vs. Spark comparison
33:38 Optimizers in higher-level APIs
34:50 Out-of-memory error handling
37:24 Another scenario-based query
42:00 Job scheduling with Azure Data Factory
43:26 Coding questions

Music track: Retro by Chill Pulse
Source: https://freetouse.com/music
Background Music for Video (Free)

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