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

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

Connecting to ClickUp Data

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

ClickUp API Profile Settings

In order to authenticate to ClickUp, you'll need to provide your API Key. You can find this token in your user settings, under the Apps section. At the top of the page you have the option to generate a personal token. Set the API Key to your personal token in the ProfileSettings property to connect.

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

cnxn = mod.connect("Profile=C:\profiles\ClickUp.apip;ProfileSettings='APIKey=my_personal_token';")

Create a SQL Statement to Query ClickUp

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

sql = "SELECT Id, Name FROM Tasks WHERE Priority = 'High'"

Extract, Transform, and Load the ClickUp Data

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

Loading ClickUp Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with ClickUp 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 ClickUp 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\ClickUp.apip;ProfileSettings='APIKey=my_personal_token';")

sql = "SELECT Id, Name FROM Tasks WHERE Priority = 'High'"

table1 = etl.fromdb(cnxn,sql)

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

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