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

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

Connecting to Klaviyo Data

Connecting to Klaviyo 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.

To authenticate to Klaviyo, provide an API Key. You can generate or view your API keys under 'My Account'

1. Navigate to 'Settings' > 'API Keys'
2. Click 'Create API Key'.
3. Name your API key and choose the desired scopes.

To connect in your CData solutions, set API Key to your Klaviyo API key.

If you wish to use OAuth authentication, refer to the Help documenation.

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

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

cnxn = mod.connect("APIKey=my_api_key;")

Create a SQL Statement to Query Klaviyo

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

sql = "SELECT Id, Name FROM Campaigns WHERE Status = 'draft'"

Extract, Transform, and Load the Klaviyo Data

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

Loading Klaviyo Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Klaviyo

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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

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

cnxn = mod.connect("APIKey=my_api_key;")

sql = "SELECT Id, Name FROM Campaigns WHERE Status = 'draft'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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