Optimizing Delta Lake table partitioning for query performance

Partitioning is a technique that improves the performance of Delta Lake queries by reducing the amount of data that needs to be scanned, filtered, or shuffled. Partitioning works by dividing a large table into smaller, more manageable parts, called partitions, based on a column or a set of columns. Each partition contains only the rows that match the partitioning criteria.

In this recipe, we will use PySpark to read the CSV file into a Spark DataFrame and write it to a Delta Lake table. We will then partition the table by specific columns and compare the query performance of the partitioned table with the non-partitioned table.

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 delta import configure_spark_with_delta_pip

   from pyspark.sql...