Skip to main content

Microsoft SQL Server bcp (Bulk Copy) wrapper with Azure Synapse Blob alternative

Project description


What is it?

This package is a wrapper for Microsoft's SQL Server bcp utility. Current database drivers available in Python are not fast enough for transferring millions of records (yes, I have tried pyodbc fast_execute_many). Despite the IO hits, the fastest option by far is saving the data to a CSV file in file system (preferably /dev/shm tmpfs) and using the bcp utility to transfer the CSV file to SQL Server.

Azure Synapse / Blob extensions

If the following env vars are set (with examples):


Then instead of using the bcp command line utility, the CSV file will be copied to the Azure Storage Blob temporarily and COPY will be used to transfer it from there into the Synapse Database.

You may need to


on Synapse to the user that is connecting.

The sqlcmd utility will still be required.

How Can I Install It?

Make sure your computeer has the requirements.

Install locally from a git clone:

pip install -e .

or via requirements.txt:

-e git+


Following examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.

Flat File

Following example assumes that you have a comma separated file with no qualifier in path 'tests/data1.csv'. The code below sends the the file to SQL Server.

import bcpy

sql_config = {
    'server': 'sql_server_hostname',
    'database': 'database_name',
    'username': 'test_user',
    'password': 'test_user_password1234'
sql_table_name = 'test_data1'
csv_file_path = 'tests/data1.csv'
flat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)
sql_table = bcpy.SqlTable(sql_config, table=sql_table_name)


The following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.

import bcpy
import numpy as np
import pandas as pd

sql_config = {
    'server': 'sql_server_hostname',
    'database': 'database_name',
    'username': 'test_user',
    'password': 'test_user_password1234'
table_name = 'test_dataframe'
df = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),
bdf = bcpy.DataFrame(df)
sql_table = bcpy.SqlTable(sql_config, table=table_name)


You need a working version of Microsoft bcp installed in your system. Your PATH environment variable should contain the directory of the bcp utility. Following are the installation tutorials for different operating systems.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bcpyaz-0.2.0.tar.gz (11.5 kB view hashes)

Uploaded Source

Built Distribution

bcpyaz-0.2.0-py3-none-any.whl (12.7 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page