Caching and persistence

To make Spark applications run faster, developers can use two important techniques: caching and persistence. These techniques allow Spark to store some or all of the data in memory or on disk so that it can be reused without recomputing it. By caching or persisting DataFrames, you can store some intermediate results in the memory (default) or other more durable storage, such as disk space, and/or replicate them. This way, you can avoid recomputing these results when they are needed again in later stages. DataFrames can be cached using the cache() or persist() methods on them.

In this recipe, we will learn how to cache and persist Spark DataFrames.

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 import StorageLevel

   from pyspark.sql...