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

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

Connecting to Stripe Data

Connecting to Stripe 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.

Use the OAuth authentication standard to connect to Stripe. To authenticate using OAuth, you will need to register an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Stripe

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

sql = "SELECT Email, Discount FROM Customers WHERE Delinquent = 'False'"

Extract, Transform, and Load the Stripe Data

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

Loading Stripe Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Stripe

table1 = [ ['Email','Discount'], ['NewEmail1','NewDiscount1'], ['NewEmail2','NewDiscount2'], ['NewEmail3','NewDiscount3'] ]

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Email, Discount FROM Customers WHERE Delinquent = 'False'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Email','Discount'], ['NewEmail1','NewDiscount1'], ['NewEmail2','NewDiscount2'], ['NewEmail3','NewDiscount3'] ]

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