Organizing data with Z-ordering for efficient query execution

Data skipping in Delta Lake can speed up query performance by avoiding unnecessary data reads. Data skipping works best when the data is organized in a way that collocates related information in the same set of files. This can be achieved by using Z-ordering, a technique that reorders the data based on one or more columns. In this recipe, you will learn how to use Z-ordering with Delta Lake for efficient query execution.

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 Z-order the table by specific columns and compare the query performance of the optimized table with the non-optimized 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...