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50 PySpark Interview Questions and Answers For 2025

ProjectPro

With the global data volume projected to surge from 120 zettabytes in 2023 to 181 zettabytes by 2025, PySpark's popularity is soaring as it is an essential tool for efficient large scale data processing and analyzing vast datasets. Resilient Distributed Datasets (RDDs) are the fundamental data structure in Apache Spark.

Hadoop 68
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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

You can produce code, discover the data schema, and modify it. Smooth Integration with other AWS tools AWS Glue is relatively simple to integrate with data sources and targets like Amazon Kinesis , Amazon Redshift, Amazon S3, and Amazon MSK. For analyzing huge datasets, they want to employ familiar Python primitive types.

AWS 66
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Top 15 Azure Databricks Interview Questions and Answers For 2025

ProjectPro

Various sources, including Structured Data Files, Hive Tables, external databases, existing RDDs, etc., The PySpark Partition method divides a large dataset into smaller datasets using one or more partition keys. When transformations on partitioned data run more quickly, execution performance is improved.

Scala 40
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A 2025 Guide to Ace the Netflix Data Engineer Interview

ProjectPro

The transformation of unstructured data into a structured format is a methodical process that involves a thorough analysis of the data to understand its formats, patterns, and potential challenges. Master Data Engineering at your Own Pace with Project-Based Online Data Engineering Course !

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Data News — Week 22.45

Christophe Blefari

Modeling is often lead by the dimensional modeling but you can also do 3NF or data vault. When it comes to storage it's mainly a row-based vs. a column-based discussion, which in the end will impact how the engine will process data. The end-game dataset. This is probably the concept I liked the most from the video.

BI 130
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Top 25 DBT Interview Questions and Answers for 2025

ProjectPro

DBT (Data Build Tool) can handle incremental data loads by leveraging the incremental model , which allows only new or changed data to be processed and transformed rather than reprocessing the entire dataset. What techniques do you use to minimize run times when dealing with large datasets?

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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

Netflix Tech

As the paved path for moving data to key-value stores, Bulldozer provides a scalable and efficient no-code solution. Users only need to specify the data source and the destination cluster information in a YAML file. Bulldozer provides the functionality to auto-generate the data schema which is defined in a protobuf file.