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

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

Connecting to Customer.io Data

Connecting to Customer.io 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

To obtain your Customer.io App API Key, navigate to the Customer.io UI under Data & Integrations > Integrations > Customer.io API and generate your API key.

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Customer.io App API Key.

Example Connection String

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

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

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

Create a SQL Statement to Query Customer.io

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

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

Extract, Transform, and Load the Customer.io Data

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

Loading Customer.io Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Customer.io 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 Customer.io 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\CustomerIO.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";")

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

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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