Avoiding data skew

Data skew is a common problem that can affect the performance and scalability of Apache Spark applications. Data skew occurs when the data being processed is not evenly distributed across partitions, resulting in some tasks taking much longer than others and wasting cluster resources. Data skew can be caused by operations that require shuffling or repartitioning the data, such as join, groupBy, or orderBy.

In this recipe, we will learn how to detect and handle data skew in Apache Spark using various techniques and tips.

How to do it…

1. Import the required libraries: Start by importing the necessary libraries for working with Delta Lake. In this case, we need the delta module and the SparkSession class from the pyspark.sql module:

   from pyspark.sql import SparkSession

   from pyspark.sql.functions import rand, col, when, broadcast, concat, lit

2. Create a SparkSession object: To interact with Spark and Delta Lake, we need to create a SparkSession object...