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

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

Connecting to Clockify Data

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

Clockify API Profile Settings

Log into your Clockify account, navigate to Profile Settings, and copy the API key from the API section.

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

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

Create a SQL Statement to Query Clockify

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

sql = "SELECT Id, WorkspaceId FROM ApprovalRequests WHERE StatusState = 'PENDING'"

Extract, Transform, and Load the Clockify Data

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

Loading Clockify Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, WorkspaceId FROM ApprovalRequests WHERE StatusState = 'PENDING'"

table1 = etl.fromdb(cnxn,sql)

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

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