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

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

Connecting to Typeform Data

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

TypeForm API Profile Settings

Authentication to TypeForm uses the OAuth standard.

To authenticate to TypeForm, you must first register and configure an OAuth application with TypeForm here: https://admin.typeform.com/account#/section/tokens. Your app will be assigned a client ID and a client secret which can be set in the connection string. More information on setting up an OAuth application can be found at https://developer.typeform.com/get-started/.

Note that there are several different use scenarios which all require different redirect URIs:

• CData Desktop Applications: CData desktop applications (Sync, API Server, ArcESB) accept OAuth tokens at /src/oauthCallback.rst. The host and port is the same as the default port used by the application. For example, if you use http://localhost:8019/ to access CData Sync then the redirect URI will be http://localhost:8019/src/oauthCallback.rst.
• CData Cloud Applications: CData cloud applications are similar to their desktop counterparts. If you access Connect AI at https://1.2.3.4/ then you should use the redirect https://1.2.3.4/src/oauthCallback.rst.
• Desktop Application: When using a desktop application, the URI https://localhost:33333 is recommended.
• Web Application: When developing a web application using the driver, use your own URI here such as https://my-website.com/oauth.

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

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

Create a SQL Statement to Query Typeform

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

sql = "SELECT Id, Title FROM Tags WHERE SettingsIsPublic = 'true'"

Extract, Transform, and Load the Typeform Data

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

Loading Typeform Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Title FROM Tags WHERE SettingsIsPublic = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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