A. Create a single, wide Snowflake table with all possible columns from all CSV files, defining all columns as VARCHAR. Load all data into this table, then create views for each 'record_type' that cast the data to the correct data types.

B. Create multiple Snowflake tables, one for each 'record_type', with the corresponding schema. Use a Snowflake task that runs periodically to identify new files in the S3 bucket, determine their 'record_type', and load them into the appropriate table using a dynamic SQL query.

C. Use Snowflake's VARIANT data type for a single column in the Snowflake table. Load the entire CSV file content as a JSON string into this column. Create views to extract and cast the data based on the 'record_type'.

D. Create a single Snowflake table with a VARIANT column to store the raw data. Use a Snowpipe to load the data continuously. Implement a stored procedure that is triggered by the Snowpipe to parse the VARIANT data based on 'record_type' and insert it into correctly typed columns in the same table.

E. Create a single external table pointing to the S3 bucket. Use a Snowflake stream on the external table to track changes. Implement a series of tasks, one for each 'record_type', that transform data from the stream and load into a single target table with appropriate datatypes.