Extract, Transform, and Load Magento Data in Python

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Magento Python Connector

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



The CData Python Connector for Magento enables you to create ETL applications and pipelines for Magento data in Python with petl.

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

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

Connecting to Magento Data

Connecting to Magento 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.

Magento uses the OAuth 1 authentication standard. To connect to the Magento REST API, you will need to obtain values for the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties by registering an app with your Magento 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 Magento system. The URL depends on whether you are using the Magento REST API as a customer or administrator.

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

  • Administrator: To access Magento 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 Magento Connector, follow the procedure below to install the other required modules and start accessing Magento 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 Magento 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.magento as mod

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

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

Create a SQL Statement to Query Magento

Use SQL to create a statement for querying Magento. 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 Magento Data

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

Loading Magento 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 Magento

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

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

With the CData Python Connector for Magento, you can work with Magento 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 Magento Python Connector to start building Python apps and scripts with connectivity to Magento 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.magento as mod

cnxn = mod.connect("OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://127.0.0.1:33333;Url=https://mymagentohost.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')