I would like to send a large `pandas.DataFrame` to a remote server running MS SQL. The way I do it now is by converting a `data_frame` object to a list of tuples and then send it away with pyODBC&#39;s `executemany()` function. It goes something like this:

     import pyodbc as pdb

     list_of_tuples = convert_df(data_frame)

     connection = pdb.connect(cnxn_str)

     cursor = connection.cursor()
     cursor.fast_executemany = True
     cursor.executemany(sql_statement, list_of_tuples)
     connection.commit()

     cursor.close()
     connection.close()

I then started to wonder if things can be sped up (or at least more readable) by using `data_frame.to_sql()` method. I have came up with the following solution:
     
     import sqlalchemy as sa

     engine = sa.create_engine(&quot;mssql+pyodbc:///?odbc_connect=%s&quot; % cnxn_str)
     data_frame.to_sql(table_name, engine, index=False)

Now the code is more readable, but the upload is **at least 150 times slower**...

Is there a way to flip the `fast_executemany` when using SQLAlchemy?
     

I am using pandas-0.20.3, pyODBC-4.0.21 and sqlalchemy-1.1.13.

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Speeding up pandas.DataFrame.to_sql with fast_executemany of pyODBC