Issue writing Dataframe to SQL Server Full+
I've seen many developers post about incredible slowness when writing pandas dataframe to a SQL Server table. Some people labeled the issue "chunk size doesn't work" or "data incompatibility slowness" and what not. After a lengthy research I found the following explanation from SQLAlchemy:

http://docs.sqlalchemy.org/en/rel_0_8/faq.html#i-m-inserting-400-000-rows-with-the-orm-and-it-s-really-slow

Briefly, I believe Dataframe.to_sql() method is using SQLAlchemy ORM which uses the Unit of Work method and ends up performing row maintenance work upon insertion of every row of the dataframe. This issue can be resolved if to_sql uses SQLAlchemy CORE instead (link above). Is there a workaround for using Dataframe.to_sql() with SQL Server (not lite) with acceptable performance? I'm really trying all I can to avoid exporting the dataframe to a file and import it to SQL with an SSIS package.

Thanks,
Sam
