Cracking Apache Spark Interviews | RDD vs DataFrame vs Dataset | Senior Data Engineer Guide
In this video, we break down one of the most common Apache Spark interview questions:
RDD vs DataFrame vs Dataset — explained from a senior data engineer’s perspective.
Instead of definitions, we focus on how these abstractions differ internally, how Spark executes them, and when you should use each one in real projects and interviews.
What You’ll Learn
What RDD, DataFrame, and Dataset really are
Differences in performance, optimization, and memory usage
Role of Catalyst Optimizer and Tungsten
Why DataFrames are usually faster than RDDs
When RDDs still make sense
How to answer follow-up interview questions confidently
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