Use SQLAlchemy ORMs to Access BigCommerce Data in Python

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BigCommerce Python Connector

Python Connector Libraries for BigCommerce Data Connectivity. Integrate BigCommerce with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for BigCommerce enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of BigCommerce data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for BigCommerce and the SQLAlchemy toolkit, you can build BigCommerce-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to BigCommerce data to query, update, delete, and insert 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 CData Connector 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.

Follow the procedure below to install SQLAlchemy and start accessing BigCommerce through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model BigCommerce Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with BigCommerce data.

engine = create_engine("bigcommerce:///?OAuthClientId=YourClientId& OAuthClientSecret=YourClientSecret& StoreId='YourStoreID'& CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for BigCommerce Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Customers table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Customers(base):
	__tablename__ = "Customers"
	FirstName = Column(String,primary_key=True)
	LastName = Column(String)
	...

Query BigCommerce Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("bigcommerce:///?OAuthClientId=YourClientId& OAuthClientSecret=YourClientSecret& StoreId='YourStoreID'& CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(FirstName="Bob"):
	print("FirstName: ", instance.FirstName)
	print("LastName: ", instance.LastName)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Customers_table = Customers.metadata.tables["Customers"]
for instance in session.execute(Customers_table.select().where(Customers_table.c.FirstName == "Bob")):
	print("FirstName: ", instance.FirstName)
	print("LastName: ", instance.LastName)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert BigCommerce Data

To insert BigCommerce data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to BigCommerce.

new_rec = Customers(FirstName="placeholder", FirstName="Bob")
session.add(new_rec)
session.commit()

Update BigCommerce Data

To update BigCommerce data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to BigCommerce.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.FirstName = "Bob"
session.commit()

Delete BigCommerce Data

To delete BigCommerce data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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