This scenario requires a specific combination of Snowflake's extensibility features: the ability to run Python (pandas) code, the ability to return multiple rows for a single input (tabular output), and high performance through vectorization.
A User-Defined Table Function (UDTF) is the correct architectural choice when an input value needs to be expanded into a set of rows. While a standard UDF returns exactly one value per input row, a UDTF can return zero, one, or many rows, making it ideal for "parsing" or "splitting" tasks. By using a Vectorized UDTF (specifically utilizing the Python handler with pandas), the Analyst can process blocks of rows as pandas DataFrames or Series. This is significantly more efficient than processing one row at a time because it reduces the overhead of the Snowflake-to-Python execution engine.
Evaluating the Options:
* Option A is incorrect because a standard UDF can only return a single scalar value per input row, making it impossible to "return multiple rows of parsed output."
* Option C is incorrect because External Functions are used to call code hosted outside of Snowflake (like an AWS Lambda). While they can process data, they introduce network latency and are not the native way to run pandas code for internal joins.
* Option D is incorrect because Stored Procedures are generally used for administrative tasks and procedural logic. While they can return tables, they cannot be directly "joined" with other tables in a standard SELECT statement in the same way a UDTF can.
* Option B is the correct choice. It satisfies the requirement for pandas integration, provides the necessary multi-row output via the process and end_partition methods, and allows the analyst to use the LATERAL keyword to join the parsed results directly with existing tables in a single SQL statement.