32. Cache and Persist in pyspark | Cache vs Persist | Pyspark Interview Question

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PySpark Tutorial: cache() vs persist() – Understanding Spark Data Storage

Description:

Welcome to our PySpark tutorial! In this video, we'll dive into two essential concepts for optimizing performance in Spark applications: cache() and persist().

Whether you're a data engineer, data scientist, or just getting started with PySpark, understanding these methods is crucial for improving your application's efficiency.

🔍 What You'll Learn:

What is cache()?
Discover how cache() simplifies caching DataFrames and RDDs in PySpark. Learn about its default behavior, which stores data in memory and spills to disk if needed.

What is persist()?
Explore the persist() method and how it provides more control over storage levels. From memory-only to disk-only and serialized formats, find out how to choose the right storage level for your use case.

When to Use Each Method
Understand scenarios where cache() is sufficient and when you might need the flexibility of persist().

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