Reducing Delta Lake table size and I/O cost with compression

Delta Lake tables are stored as Parquet files in a directory, along with a transaction log that tracks changes to the table. One of the benefits of using Delta Lake is that it supports various compression codecs for Parquet files, such as gzip, snappy, lzo, zstd, and brotli. Compression can help reduce the size of the table on disk and the amount of data transferred over the network, which can improve performance and save costs.

In this recipe, we will learn how to use compression with Delta Lake tables and how to measure the impact of compression on table size and I/O cost.

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, DeltaTable

   from pyspark.sql import SparkSession

   from pyspark.sql.functions...