A. Directly query the raw customer data using SQL and Snowpark, computing descriptive statistics like mean, median, and standard deviation for all numeric columns and frequency counts for categorical columns. Store the results in a temporary table for further analysis.

B. Utilize Snowpark to create a sampled dataset (e.g., 1% of the original data) and perform all exploratory data analysis on the sample to reduce the data volume and potential exposure of PII.

C. Apply differential privacy techniques using Snowpark to add noise to the summary statistics generated from the customer data, masking the individual contributions of each customer while revealing overall trends.

D. Create a masked view of the customer data using Snowflake's dynamic data masking features. This view masks sensitive PII columns while allowing you to compute aggregate statistics and identify patterns using SQL and Snowpark functions. Columns like 'email' are masked using and columns like are masked using .

E. Export the entire customer dataset to an external data lake for exploratory analysis using Spark and Python. Apply data masking in Spark before analysis.