A. Create a separate Snowpipe for each S3 bucket. Although this creates more Snowpipe objects, it allows you to specify a different FILE FORMAT and transformation logic for each data source.

B. Use a single Snowpipe with a generic FILE FORMAT that can handle all possible JSON schemas. Implement a VIEW on top of the target table to transform and restructure the data based on the source bucket.

C. Use a single Snowpipe and leverage Snowflake's VARIANT data type to store the raw JSON data. Create separate external tables, each pointing to a specific S3 bucket, and use SQL queries to transform and load the data into the target table.

D. Use a single Snowpipe and leverage Snowflake's ability to call a user-defined function (UDF) within the 'COPY INTO' statement to transform the data based on the S3 bucket path. The UDF can parse the bucket path and apply the appropriate JSON schema transformation.

E. Since Snowpipe cannot handle multiple schemas with a single pipe, pre-process the data in S3 using an AWS Lambda function to transform all files into a common schema before they are ingested by the Snowpipe.