Yesterday I published a post on and shared on LinkedIn and had a quite big reaction as an popular PBI influencer commented on my post.

One of the comments by Jesus Jayaro called my attention saying that if pyodbc is used with execute many it is faster than data factory.

So I did a quick research and in reality it is fast I am impressed.

On my quick research I could put together a working script. It failed at first and did not want to spend too much time on this.

I tried to do the insert into one chunk and it failed using a script i found in medium and then I tried in small chunks and it worked.

but took 500 seconds. Not bad 👍🏻 compared to the initial 55,000 seconds estimate. So it is 0.8% of the time. So more than 100x faster.

At least in this example data factory was faster it was a 5 minutes 35 seconds against 8 minutes 20 seconds of pyodbc.

here is the script.

import os
import logging

import pandas as pd
import pyodbc

from utils import timing

SQL_SERVER = os.getenv('SQL_SERVER')

driver= '{ODBC Driver 18 for SQL Server}'

def fast_load(chunk: pd.DataFrame)->None:


    MY_TABLE = "taxi"

    with pyodbc.connect(connection_string) as conn:


            insert_to_tmp_tbl_stmt = f"INSERT INTO {MY_TABLE} VALUES (?,?,?,?,?,?,?,?,?,?,?)"
            cursor = conn.cursor()
            cursor.fast_executemany = True
            tuples = chunk.values.tolist()
            if tuples:
                cursor.executemany(insert_to_tmp_tbl_stmt, tuples)
        except Exception as e:

def upload_batches(batch_size:int, df:pd.DataFrame)-> None:
    batches =  int(round(df.shape[0] / batch_size,0))
    reminder =  df.shape[0] % batch_size
    end = 0
    for i in range(batches + 1):
        start = end
        end += batch_size if i < batches else (reminder+1)
        print(f"uploading batch {i+1}/{batches+1}")
        start = end

def run_script()->None:

    taxi = pd.read_csv('taxi.csv')
    upload_batches(10000, taxi)


# func: run_script took: 500.3895 sec

If you can provide a faster script of challenge data factory please contact me on LinkedIn