Change data capture in Delta Lake

Change data capture (CDC) is a technique to capture and process the changes made to a data source, such as a database or a file system. CDC can be useful for various scenarios, such as data synchronization, replication, auditing, and analytics.

Delta Lake supports CDC through a feature called change data feed (CDF), which allows Delta tables to track row-level changes between versions of a Delta table. When enabled on a Delta table, the runtime records “change events” for all data written into the table.

In this recipe, we will learn how to apply CDC to a table using Delta Lake in Python.

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

2. Create a SparkSession...