Efficient Schema Evolution in Delta Lake with PySpark: A Databricks Tutorial

In this hands-on Databricks tutorial,
we'll explore the dynamic world of Delta Lake and PySpark,
unveiling an efficient strategy for seamless schema evolution.
Our journey kicks off by summoning the mighty Spark session and
crafting a sample DataFrame adorned with an initial schema.
Join us as we guide you through the enchanted process of
writing this DataFrame into the mystical Delta table,
unraveling the secrets of reading it back, and conjuring a User-Defined Function (UDF).
This UDF performs a magical feat, calculating a custom correlation-like measure between
the heroes of our tale - "Age" and "Height."
Experience the magic of Delta Lake as we add the 'Correlation_Age_Height' column,
housing our unique correlation measure. Witness the clarity of the original Delta table,
the creation of a new schema, and the selection of relevant columns for our refreshed DataFrame.
In a grand finale, observe seamless schema evolution as we update and integrate with the mystical 'mergeSchema'
option.