What is the difference between RDD, DataFrame, and Dataset?
💡 Model Answer
RDD (Resilient Distributed Dataset) is Spark’s low‑level API that gives full control over data but offers no automatic optimization. DataFrame is a higher‑level, schema‑aware API that uses Catalyst for query optimization and Tungsten for execution, providing better performance for structured data. Dataset is a typed API (available in Scala/Java) that combines the safety of RDDs with the optimizations of DataFrames. In practice, use RDDs for complex, custom transformations not expressible in SQL; use DataFrames/Datasets for structured data processing, leveraging Spark SQL’s optimizations.
This answer was generated by AI for study purposes. Use it as a starting point — personalize it with your own experience.
🎤 Get questions like this answered in real-time
Assisting AI listens to your interview, captures questions live, and gives you instant AI-powered answers — invisible to screen sharing.
Get Assisting AI — Starts at ₹500