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

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

Connecting to Shopify Data

Connecting to Shopify 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.

To make use of all the features of the data provider, provide the AppId, Password, and ShopUrl connection properties.

To obtain these values, see the Getting Started section in the help documentation to register the data provider as an application with Shopify.

After installing the CData Shopify Connector, follow the procedure below to install the other required modules and start accessing Shopify 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 Shopify 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.shopify as mod

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

cnxn = mod.connect("AppId=MyAppId;Password=MyPassword;ShopUrl=https://yourshopname.myshopify.com;")

Create a SQL Statement to Query Shopify

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

sql = "SELECT FirstName, Id FROM Customers WHERE FirstName = 'jdoe1234'"

Extract, Transform, and Load the Shopify Data

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

Loading Shopify Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Shopify

table1 = [ ['FirstName','Id'], ['NewFirstName1','NewId1'], ['NewFirstName2','NewId2'], ['NewFirstName3','NewId3'] ]

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

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

cnxn = mod.connect("AppId=MyAppId;Password=MyPassword;ShopUrl=https://yourshopname.myshopify.com;")

sql = "SELECT FirstName, Id FROM Customers WHERE FirstName = 'jdoe1234'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['FirstName','Id'], ['NewFirstName1','NewId1'], ['NewFirstName2','NewId2'], ['NewFirstName3','NewId3'] ]

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