A. Create a SQL UDF that directly calculates the Haversine distance using Snowflake's built-in mathematical functions (SIN, COS, ACOS, RADIANS). This is straightforward and easy to implement.

B. Create a Java UDF that calculates the Haversine distance, leveraging optimized mathematical libraries. This allows for potentially faster execution due to lower- level optimizations.

C. Create an External Function (using AWS Lambda or Azure Functions) to calculate the Haversine distance. This allows for offloading the computation to a separate compute environment.

D. Create a SQL UDF that pre-calculates the RADIANS for latitude and longitude only once and stores them in a temporary table, using those values for subsequent distance calculations within the same session.

E. Create a SQL UDF leveraging Snowflake's VECTORIZED keyword, hoping to automatically leverage SIMD instructions, without any code changes to mathematical calculation inside the UDF