How to Build an ETL App for Adobe Commerce 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 Adobe Commerce and the petl framework, you can build Adobe Commerce-connected applications and pipelines for extracting, transforming, and loading Adobe Commerce data. This article shows how to connect to Adobe Commerce with the CData Python Connector and use petl and pandas to extract, transform, and load Adobe Commerce data.

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

Connecting to Adobe Commerce Data

Connecting to Adobe Commerce 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.

Adobe Commerce uses the OAuth 1 authentication standard. To connect to the Adobe Commerce REST API, you will need to obtain values for the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties by registering an app with your Adobe Commerce system. See the "Getting Started" section in the help documentation for a guide to obtaining the OAuth values and connecting.

You will also need to provide the URL to your Adobe Commerce system. The URL depends on whether you are using the Adobe Commerce REST API as a customer or administrator.

• Customer: To use Adobe Commerce as a customer, make sure you have created a customer account in the Adobe Commerce homepage. To do so, click Account -> Register. You can then set the URL connection property to the endpoint of your Adobe Commerce system.

• Administrator: To access Adobe Commerce as an administrator, set CustomAdminPath instead. This value can be obtained in the Advanced settings in the Admin menu, which can be accessed by selecting System -> Configuration -> Advanced -> Admin -> Admin Base URL.

  If the Use Custom Admin Path setting on this page is set to YES, the value is inside the Custom Admin Path text box; otherwise, set the CustomAdminPath connection property to the default value, which is "admin".

After installing the CData Adobe Commerce Connector, follow the procedure below to install the other required modules and start accessing Adobe Commerce 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 Adobe Commerce 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.adobe commerce as mod

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

cnxn = mod.connect("OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://127.0.0.1:33333;Url=https://myAdobe Commercehost.com;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Adobe Commerce

Use SQL to create a statement for querying Adobe Commerce. In this article, we read data from the Products entity.

sql = "SELECT Name, Price FROM Products WHERE Style = 'High Tech'"

Extract, Transform, and Load the Adobe Commerce Data

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

Loading Adobe Commerce Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Adobe Commerce

table1 = [ ['Name','Price'], ['NewName1','NewPrice1'], ['NewName2','NewPrice2'], ['NewName3','NewPrice3'] ]

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

With the CData Python Connector for Adobe Commerce, you can work with Adobe Commerce 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 Adobe Commerce to start building Python apps and scripts with connectivity to Adobe Commerce 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.adobe commerce as mod

cnxn = mod.connect("OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://127.0.0.1:33333;Url=https://myAdobe Commercehost.com;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Name, Price FROM Products WHERE Style = 'High Tech'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Price'], ['NewName1','NewPrice1'], ['NewName2','NewPrice2'], ['NewName3','NewPrice3'] ]

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