How to Build an ETL App for BigCommerce Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for BigCommerce and the petl framework, you can build BigCommerce-connected applications and pipelines for extracting, transforming, and loading BigCommerce data. This article shows how to connect to BigCommerce with the CData Python Connector and use petl and pandas to extract, transform, and load BigCommerce data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live BigCommerce data in Python. When you issue complex SQL queries from BigCommerce, the driver pushes supported SQL operations, like filters and aggregations, directly to BigCommerce and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to BigCommerce Data

Connecting to BigCommerce data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

BigCommerce authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Big Commerce developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL. These three parameters will be set as connection properties to your driver.

Additionally, in order to connect to your BigCommerce Store, you will need your StoreId. To find your Store Id please follow these steps:

1. Log in to your BigCommerce account.
2. From the Home Page, select Advanced Settings > API Accounts.
3. Click Create API Account.
4. A text box named API Path will appear on your screen.
5. Inside you can see a URL of the following structure: https://api.bigcommerce.com/stores/{Store Id}/v3.
6. As demonstrated above, your Store Id will be between the 'stores/' and '/v3' path paramters.
7. Once you have retrieved your Store Id you can either click Cancel or proceed in creating an API Account in case you do not have one already.

After installing the CData BigCommerce Connector, follow the procedure below to install the other required modules and start accessing BigCommerce through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for BigCommerce Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.bigcommerce as mod

You can now connect with a connection string. Use the connect function for the CData BigCommerce Connector to create a connection for working with BigCommerce data.

cnxn = mod.connect("OAuthClientId=YourClientId; OAuthClientSecret=YourClientSecret; StoreId='YourStoreID'; CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query BigCommerce

Use SQL to create a statement for querying BigCommerce. In this article, we read data from the Customers entity.

sql = "SELECT FirstName, LastName FROM Customers WHERE FirstName = 'Bob'"

Extract, Transform, and Load the BigCommerce Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the BigCommerce data. In this example, we extract BigCommerce data, sort the data by the LastName column, and load the data into a CSV file.

Loading BigCommerce Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'LastName')

etl.tocsv(table2,'customers_data.csv')

In the following example, we add new rows to the Customers table.

Adding New Rows to BigCommerce

table1 = [ ['FirstName','LastName'], ['NewFirstName1','NewLastName1'], ['NewFirstName2','NewLastName2'], ['NewFirstName3','NewLastName3'] ]

etl.appenddb(table1, cnxn, 'Customers')

With the CData Python Connector for BigCommerce, you can work with BigCommerce data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for BigCommerce to start building Python apps and scripts with connectivity to BigCommerce data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.bigcommerce as mod

cnxn = mod.connect("OAuthClientId=YourClientId; OAuthClientSecret=YourClientSecret; StoreId='YourStoreID'; CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT FirstName, LastName FROM Customers WHERE FirstName = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'LastName')

etl.tocsv(table2,'customers_data.csv')

table3 = [ ['FirstName','LastName'], ['NewFirstName1','NewLastName1'], ['NewFirstName2','NewLastName2'], ['NewFirstName3','NewLastName3'] ]

etl.appenddb(table3, cnxn, 'Customers')