Skipping data for faster query execution

In this recipe, we will show you how to perform tasks to optimize and vacuum Delta tables. Delta Lake enables you to create, manage, and query data as tables, which are stored as Parquet files in a directory. However, without proper optimization, Delta Lake tables can suffer from performance issues such as slow query execution, high I/O costs, and inefficient data layout.

To optimize Delta Lake tables for efficient read query execution, you need to perform the following tasks:

• Use the OPTIMIZE command to compact small files into larger ones and sort the data within each file by one or more columns
• Use the ZORDER clause to cluster data by one or more columns that are frequently used in filter predicates
• Use the VACUUM command to remove stale files that are no longer referenced by the table

How to do it…

1. Import the required libraries: Start by importing the necessary libraries for working with Delta Lake...