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

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

Connecting to ConstantContact Data

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

ConstantContact API Profile Settings

ConstantContact uses OAuth-based authentication.

First, you will need to register an OAuth application with ConstantContact. You can do so from the ConstantContact API Guide (https://v3.developer.constantcontact.com/api_guide/index.html), under "MyApplications" > "New Application". Your Oauth application will be assigned a client id (API Key) and you can generate a client secret (Secret).

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

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the client_id that is specified in you app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in you app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

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

cnxn = mod.connect("Profile=C:\profiles\ConstantContact.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query ConstantContact

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

sql = "SELECT Id, EmailAddress FROM Contacts WHERE CompanyName = 'Acme, Inc.'"

Extract, Transform, and Load the ConstantContact Data

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

Loading ConstantContact Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with ConstantContact 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 ConstantContact 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\ConstantContact.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT Id, EmailAddress FROM Contacts WHERE CompanyName = 'Acme, Inc.'"

table1 = etl.fromdb(cnxn,sql)

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

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