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Partitioning vs Bucketing | Interview Question | PySpark #pyspark #bigdata #pwc #interview

Partitioning and bucketing are techniques used to optimize data storage and improve query performance in PySpark. The choice between them depends on the specific use case and the nature of the queries that will be executed on the data.

Sample Data:

date product amount region
01-01-2024 Product_0 0 Region_0
02-01-2024 Product_1 10 Region_1
03-01-2024 Product_2 20 Region_2
04-01-2024 Product_1 30 Region_0
05-01-2024 Product_4 40 Region_1
06-01-2024 Product_0 50 Region_2
07-01-2024 Product_1 60 Region_0
08-01-2024 Product_2 70 Region_1
09-01-2024 Product_2 80 Region_2
10-01-2024 Product_4 90 Region_0

Check out this video and do let me know your doubts we can connect on
linkedIn : https://www.linkedin.com/in/priyam-jain-0946ab199/

PWC interview Question:
https://www.youtube.com/watch?v=axBQzNZ9YnQ
https://www.youtube.com/watch?v=HevbUGp2HZ8

Deloitte interview Question:
https://www.youtube.com/watch?v=__cRigKAEHs&t=140s

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#pyspark #spark #bigdata #bigdataengineer #dataengineering #dataengineer #deloitte #pwc #mnc

Видео Partitioning vs Bucketing | Interview Question | PySpark #pyspark #bigdata #pwc #interview канала pysparkpulse
Interview question on pyspark learn pyspark learn big data with pyspark hands-on pyspark examples pyspark datetime manipulation pyspark tutorial for data engineers improve data quality in pyspark pyspark data transformation techniques pyspark interview question bigdata interview question bigdata interview series pyspark deloitte deloitee interview question deloitee data engineer interview partitioning vs bucketing partitioning in spark bucketing in spark
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