RDD vs DataFrame in PySpark – Key Differences with Real Examples | PySpark for Beginners

RDD vs DataFrame in PySpark | Key Differences with Examples

Overview

In this tutorial, we explore the fundamental differences between RDD (Resilient Distributed Dataset) and DataFrame in PySpark. You will learn how both are used, when to prefer one over the other, and how their performance and schema-handling differ in real data engineering scenarios.

1️⃣ What is RDD?

RDD is a low-level object for distributed data processing. It's immutable, fault-tolerant, and supports functional transformations using methods like map(), flatMap(), filter().

rdd = spark.sparkContext.parallelize([("Alice", 30), ("Bob", 25)])
print(rdd.collect())

2️⃣ What is DataFrame?

DataFrame is a distributed collection of data organized into named columns, like a table. It's optimized via Catalyst and Tungsten engines for performance, and offers SQL support.

from pyspark.sql import Row

data = [Row(name="Alice", age=30), Row(name="Bob", age=25)]
df = spark.createDataFrame(data)
df.show()

3️⃣ RDD vs DataFrame Comparison Table

Feature	RDD	DataFrame
Schema	Not enforced	Enforced (Column names/types)
Performance	Slower	Optimized (Catalyst)
Ease of Use	Harder (more code)	Easier (SQL & APIs)
Use Case	Complex transformations, unstructured data	Structured data, analytics, ML pipelines

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