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

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

Connecting to Paddle Data

Connecting to Paddle 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 API Key Authentication

Paddle uses API key authentication. To obtain an API key:

1. Sign in to your Paddle account at https://vendors.paddle.com
2. Navigate to Developer Tools > Authentication
3. Click "Generate API Key"
4. Assign the appropriate permissions for the data you wish to access
5. Copy the generated key (sandbox keys begin with pdl_sdbx_apikey_; production keys begin with pdl_live_apikey_)

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Paddle API key.

Example Connection String

Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";

Connecting to Paddle

Once the authentication is configured, you can connect to Paddle and query data from any of the available tables such as Products, Customers, Subscriptions, and Transactions.

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

cnxn = mod.connect("Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";")

Create a SQL Statement to Query Paddle

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

sql = "SELECT , FROM Products WHERE = ''"

Extract, Transform, and Load the Paddle Data

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

Loading Paddle Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Paddle 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 Paddle data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";")

sql = "SELECT , FROM Products WHERE = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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