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

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

Connecting to Unbounce Data

Connecting to Unbounce 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 Unbounce Profile on disk (e.g. C:\profiles\Unbounce.apip).

Next, set the ProfileSettings connection property to the connection string for Unbounce (see below).

Unbounce API Profile Settings

Unbounce uses OAuth to authenticate to your data.

In order to authenticate to Unbounce, you will first need to register an OAuth application. To do so, go to https://developer.unbounce.com/getting_started/ and complete the Register OAuth Application form.

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 your app settings.
• OAuthClientSecret: Set this to Client Secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

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

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

Create a SQL Statement to Query Unbounce

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

sql = "SELECT Id, Name FROM Tags WHERE State = 'active'"

Extract, Transform, and Load the Unbounce Data

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

Loading Unbounce Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Name FROM Tags WHERE State = 'active'"

table1 = etl.fromdb(cnxn,sql)

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

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