BigCommerce ODBC Driver for Python Connection

Accessing the BigCommerce database can be fast and seamless by using the Open Database Connectivity (ODBC) solution. Providing full interoperability, the ODBC driver for BigCommerce allows you to access live BigCommerce data directly from Python. It enables you to retrieve data to Python, and review and modify these data right in this application.

BigCommerce Key Features

Customization and flexibility
Forecasting and analytics
Files sync and share
Sales automation

Python Key Features

Extensive libraries and frameworks
Data manipulation and analysis
Web development and APIs
Machine learning and data science

Direct Connection

Our data connector enables various ODBC-aware applications to establish a direct connection to BigCommerce via TCP/IP to eliminate the need for a BigCommerce client. A direct connection increases the speed of data transmission between Python and BigCommerce for real-time analytics. It streamlines the deployment process since there is no need to distribute any additional client-side software with the ODBC driver for BigCommerce.

What are some reasons to choose Devart ODBC Driver for BigCommerce?

Integration

ODBC Driver for BigCommerce is compatible with Python allowing extraction of data by executing SQL statements.

Platforms Variety

ODBC Driver for BigCommerce can be used with 32-bit and 64-bit applications on both x32 and x64 platforms, so there is no need to additionally configure the driver, applications, or environment.

Fully Unicode-Compliant Driver

With our fully Unicode-compliant driver, you can properly retrieve and modify any data in multilingual BigCommerce databases, regardless of their character set: Latin, Cyrillic, Hebrew, Chinese, etc., and in any language environment.

How to download, install, and configure ODBC Driver for BigCommerce?

Windows
macOS
Linux

Step 1. Download and install the driver

Download the driver. Run the installer and follow the instructions in the wizard. Open ODBC Data Sources. In the System DSN tab, click Add. Select the needed driver and click Finish.

Step 2. Activate the driver

In the License tab of the Configuration dialog, click Input Activation Key and enter your key. Click OK.

Step 3. Configure the Data Source Name (DSN)

In the General tab of the Configuration dialog, fill in the fields with the database connection data. To test the connectivity, click Test Connection. To save the DSN, click OK.

To install and configure ODBC Driver for BigCommerce on macOS, download first the PKG file from the Devart website. Run the downloaded file and follow the instructions in the wizard. After installing the driver, run the 64-bit or 86-bit iODBC utility, find the needed driver, and click Configure. In the dialog box that appears, specify the required connection settings and click OK. To establish a connection to the data source, click Test.

GUI installation on Ubuntu

Installation of an ODBC driver on Ubuntu is carried out from the DEB package. Download the package that matches the bitness of your operating system.

Then open the folder with the downloaded package, double-click the DEB package, and click Install.

Command-line installation on Ubuntu

Open the Terminal and install the driver by using the following command.

Installation on CentOS

If you use CentOC, you need to download the RPM package of the corresponding bitness.

After that, open the Konsole and navigate to the folder with the downloaded package specifying the path to the folder as a parameter of the cd command. Then, install the driver by using the following commands.

To install the devartodbcbigcommerce_i386.deb on a 32-bit system, use:

use sudo dpkg -i devartodbcbigcommerce_i386.deb

To install the devartodbcbigcommerce_amd64.deb on a 64-bit system, use:

sudo dpkg -i devartodbcbigcommerce_amd64.deb

For more detailed information about installing and configuring ODBC Driver for BigCommerce on Ubuntu, go to the documentation.

cd ~/Downloads/

To install the devart-odbc-bigcommerce.i386.rpm on a 32-bit system, use:

sudo -rpm -ivh devart-odbc-bigcommerce.i386.rpm

To install the devart-odbc-bigcommerce.x86_64.rpm on a 64-bit system, use:

sudo rpm -ivh devart-odbc-bigcommerce.x86_64.rpm

For more detailed information about installing and configuring ODBC Driver for BigCommerce on Centos, go to the documentation.

Steps to connect to BigCommerce database in Python
with pyodbc module

01.Import the pyodbc module and create a connection to the database.

import pyodbc 
cnxn = pyodbc.connect('DRIVER={Devart ODBC Driver for SQLite};Direct=True;Database=mydatabase')

02.Execute an INSERT statement to test the connection to the database.

cursor = cnxn.cursor()  
cursor.execute("INSERT INTO EMP (EMPNO, ENAME, JOB, MGR) VALUES (535, 'Scott', 'Manager', 545)")

03.Retrieve a result set from a query, iterate over it and print out all records.

cursor.execute("SELECT * FROM EMP")
row = cursor.fetchone() 
while row: 
    print (row)
    row = cursor.fetchone()

Advantages of Connectivity to BigCommerce from Python via ODBC Driver

Secure Connection

Every operation with BigCommerce becomes significantly faster using such capabilities of our driver as local data caching, connection pooling, and much more.

Working with Python using Multiple Functions

Using Python, you can create tables, add data, select, delete, sort, edit, combine data from several database tables, and find unique values quickly and easily.

ODBC Conformance

The driver fully supports the ODBC interface, its data types, and functions. It offers advanced connection string parameters and allows any ODBC-compliant desktop or web application to connect to BigCommerce from Python on various platforms.

Simple SQL Querying on Python Files

Python processes SQL through its SQL dialect. Using ODBC connection, it is simple to execute different SQL queries to retrieve entire tables or specific columns from Excel, filter data, formate data, numbers, etc.

Cost-Effective Deployment

Installing the driver is easy with the standalone installation file. There is no need in deploying and configuring any additional software which results in a reduction in deployment costs.

Sharing Your Data with Python

You can share your data with other users in Python quickly and easily. Upload your Python data to the SharePoint lists so that it can be available for you and others to use to create reports, data models, dashboards, etc.