Top Spark Theory | Real Data Engineer Interview Questions You Must Know | Interview Prep скачать в хорошем качестве

Top Spark Theory | Real Data Engineer Interview Questions You Must Know | Interview Prep

In this video, we cover the top Apache Spark theory interview questions every Data Engineer must know. If you're preparing for Spark interviews, this video will help you revise all important concepts asked in real interviews. Whether you're a beginner or experienced Data Engineer, these Spark theory questions will help you crack interviews at product companies and startups. This video is part of my Data Engineer Interview Preparation series where I share real interview questions from Spark, SQL, Airflow, AWS, and Big Data. 🔥 Perfect for: Data Engineer interviews Big Data developer interviews Spark beginners 2–5 years experience engineers Subscribe for more real interview questions and practical explanations. #sparkinterviewquestions, #apachesparkinterviewquestions, #sparktheoryinterviewquestions, #dataengineerinterview, #pysparkinterview, #sparkinterviewquestionsforexperienced, #sparkinterviewquestionsforbeginners, #apachespark, #sparkarchitectureinterviewquestions, #sparkoptimizationtechniques, #widevsnarrow, #repartitionvscoalesce, #sparkshuffleinterviewquestions, #sparkcachingandpersistence, #sparkjobexecutionflow, #bigdatainterview, #dataengineeringinterviewprep, #sparksqlinterviewquestions, #pysparkinterviewpreparation, #bigdataengineerinterview, #apachesparktheory, #sparkfundamentalsinterview, #sparkdeveloperinterviewquestions, #sparkfordataengineers, #sparkinterviewpreparation2026 00:00 Introduction 00:37 What is the difference between Spark and PySpark? 01:23 What is a Driver and What are Executors? 01:59 What is the difference between an RDD and a DataFrame? 02:42 What are Transformations and Actions in Spark? 03:30 What is Lazy Evaluation and why does Spark use it? 04:10 What are Narrow and Wide Transformations? 04:56 What is Shuffle and why is it expensive? 05:40 What is a partition in Spark, and why does it matter? 06:25 What is Cache vs Persist, and when should you use them? 07:09 What is Data Skew and why do Spark jobs get struck at 95%? 08:11 Summary