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

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

Connecting to Recurly Data

Connecting to Recurly 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 Recurly Profile on disk (e.g. C:\profiles\Recurly.apip). Next, set the ProfileSettings connection property to the connection string for Recurly (see below).

Recurly API Profile Settings

Navigate to Integrations > API Credentials in your Recurly account and click Add Private API Key.

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

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

Create a SQL Statement to Query Recurly

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

sql = "SELECT Id, Object FROM AccountAcquisition WHERE Channel = 'organic'"

Extract, Transform, and Load the Recurly Data

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

Loading Recurly Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Recurly 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 Recurly 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\Recurly.apip;ProfileSettings='APIKey=your_api_key';")

sql = "SELECT Id, Object FROM AccountAcquisition WHERE Channel = 'organic'"

table1 = etl.fromdb(cnxn,sql)

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

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