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

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

Connecting to ActiveCampaign Data

Connecting to ActiveCampaign 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.

ActiveCampaign supports authenticating with the API Key. To connect to ActiveCampaign, set the following:

• URL: This can be found in your account on the My Settings page under the Developer tab. For example: https://{yourAccountName}.api-us1.com
• APIKey: This can be found in your account on the Settings page under the Developer tab. Each user in your ActiveCampaign account has their own unique API key.

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

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

cnxn = mod.connect("URL=yourUrl;APIKey=yourApiKey")

Create a SQL Statement to Query ActiveCampaign

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

sql = "SELECT LastName, Email FROM Contacts WHERE LastName = 'Smith'"

Extract, Transform, and Load the ActiveCampaign Data

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

Loading ActiveCampaign Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to ActiveCampaign

table1 = [ ['LastName','Email'], ['NewLastName1','NewEmail1'], ['NewLastName2','NewEmail2'], ['NewLastName3','NewEmail3'] ]

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

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

cnxn = mod.connect("URL=yourUrl;APIKey=yourApiKey")

sql = "SELECT LastName, Email FROM Contacts WHERE LastName = 'Smith'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['LastName','Email'], ['NewLastName1','NewEmail1'], ['NewLastName2','NewEmail2'], ['NewLastName3','NewEmail3'] ]

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