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

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

Connecting to Google Tasks Data

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

Google Tasks API Profile Settings

In the Google Cloud Console, enable the Google Tasks API and create OAuth 2.0 credentials to obtain your Client ID and Client Secret.

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

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

Create a SQL Statement to Query Google Tasks

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

sql = "SELECT Id, Kind FROM TaskLists WHERE Title = 'My Tasks'"

Extract, Transform, and Load the Google Tasks Data

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

Loading Google Tasks Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Kind FROM TaskLists WHERE Title = 'My Tasks'"

table1 = etl.fromdb(cnxn,sql)

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

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