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

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

Connecting to Printful Data

Connecting to Printful 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.

Start by setting the Profile connection property to the location of the Printful Profile on disk (e.g. C:\profiles\Printful.apip). Next, set the ProfileSettings connection property to the connection string for Printful (see below).

Printful API Profile Settings

In order to authenticate to Printful, you'll need to provide your API Key. To get your API Key, first go to 'Settings' then 'Stores'. Select the Store you would like to connect to, then click the 'Add API Access' button to generate an API Key. Set the API Key in the ProfileSettings property to connect.

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

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

cnxn = mod.connect("Profile=C:\profiles\Printful.apip;ProfileSettings='APIKey=my_api_key';")

Create a SQL Statement to Query Printful

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

sql = "SELECT Id, Store FROM Orders WHERE Status = 'inprocess'"

Extract, Transform, and Load the Printful Data

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

Loading Printful Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Printful 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 API Driver for Python to start building Python apps and scripts with connectivity to Printful 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Printful.apip;ProfileSettings='APIKey=my_api_key';")

sql = "SELECT Id, Store FROM Orders WHERE Status = 'inprocess'"

table1 = etl.fromdb(cnxn,sql)

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

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