Pyspark function - Mask Sensitive Data Using Custom Patterns

Explanation of the Key Steps:
Initialization: Start a SparkSession to create and process a PySpark DataFrame.
Dataset Creation: Define a small dataset containing sensitive SSNs and credit card numbers.
DataFrame Creation: Convert the dataset into a PySpark DataFrame.
Masking with regexp_replace:
Replace parts of the SSN with asterisks to anonymize the first five digits.
Replace the first 12 digits of the credit card number to hide sensitive details.
Display Results: Use the Databricks display() function for an interactive and visually appealing table of masked data.