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PySpark repartition() Function Tutorial: Optimize Data Partitioning for Better Performance
⚙️ Learn how to use the repartition() function in PySpark to control and optimize the number of partitions in your DataFrames. This tutorial explains how repartition() works, when to use it over coalesce(), and how it improves the performance of Spark jobs by redistributing data efficiently across the cluster.

✅ What You’ll Learn:

What repartition() does in PySpark

Key differences between repartition() and coalesce()

How to increase partitions to enable better parallelism

When to use repartitioning for performance optimization

Practical examples in data loading, transformations, and writes

💡 Ideal for data engineers and Spark developers working with large datasets who want to fine-tune performance using partition control.

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