How to Build an ETL App for Typeform Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK 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 Connect AI and use petl to extract, transform, and load Typeform data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to Typeform in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Typeform" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Typeform.

   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.
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for Typeform Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

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. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, API1).

sql = (
    "SELECT Id, Title "
    "FROM [API1].[API].[Tags] "
    "WHERE SettingsIsPublic = 'true'"
)

Extract, Transform, and Load the Typeform Data

With a connection and query in hand, 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.

table1 = etl.fromdb(conn, sql)

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

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

Typeform is a read-only source in Connect AI, so this pipeline can extract and transform Typeform data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with Typeform data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live Typeform data through petl using the CData Connect AI Python SDK. For more information on connecting to Typeform (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Typeform data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT Id, Title "
    "FROM [API1].[API].[Tags] "
    "WHERE SettingsIsPublic = 'true'"
)

table1 = etl.fromdb(conn, sql)

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

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