PySpark: DataFrame vs RDD

Published 2026-07-04 in PySpark

PySpark: DataFrame vs RDD If you’re preparing for Data Engineering or Databricks interviews, you’ve definitely been asked: What’s the difference between RDD and DataFrame? Many people know the definitions, but interviewers want to know when to use each one. In this carousel, you’ll learn: What is an RDD? What is a DataFrame? RDD vs DataFrame comparison Why DataFrames are faster Real-world project example Top interview questions & answers When to use RDD vs DataFrame in production Quick Tip: For 90%+ of real-world Data Engineering projects, you’ll work with DataFrames because they are schema-based, optimized using the Catalyst Optimizer, and deliver much better performance. Question for you: Have you ever used RDD in a real project, or do you mostly work with DataFrames? Let me know in the comments!

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