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

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

Connecting to Gumroad Data

Connecting to Gumroad 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.

Using OAuth Authentication

To authenticate to Gumroad and connect to your own data or to allow other users to connect to their data, you can use the OAuth 2.0 standard. This is the recommended authentication method.

First you need to register an OAuth application with Gumroad. You can create an OAuth application by visiting your Gumroad account settings at https://app.gumroad.com/settings/advanced and navigating to the Applications section.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. The CData API Profile for Gumroad will automatically walk through the OAuth process in order to obtain the access token.
• OAuthClientID: Set this to the client_id that is specified in your app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

Example connection string

Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;

After installing the CData Gumroad Connector, follow the procedure below to install the other required modules and start accessing Gumroad 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 Gumroad 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 Gumroad Connector to create a connection for working with Gumroad data.

cnxn = mod.connect("Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Gumroad

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

sql = "SELECT ,  FROM CustomFields WHERE ProductId = 'prod_abc123xyz'"

Extract, Transform, and Load the Gumroad Data

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

Loading Gumroad Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Gumroad 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 Gumroad 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\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT ,  FROM CustomFields WHERE ProductId = 'prod_abc123xyz'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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