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

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

Connecting to Google Calendar Data

Connecting to Google Calendar 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.

You can connect to Google APIs on behalf of individual users or on behalf of a domain. Google uses the OAuth authentication standard. See the "Getting Started" section of the help documentation for a guide.

After installing the CData Google Calendar Connector, follow the procedure below to install the other required modules and start accessing Google Calendar 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 Calendar 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.googlecalendar as mod

You can now connect with a connection string. Use the connect function for the CData Google Calendar Connector to create a connection for working with Google Calendar data.

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Google Calendar

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

sql = "SELECT Summary, StartDateTime FROM VacationCalendar WHERE SearchTerms = 'beach trip'"

Extract, Transform, and Load the Google Calendar Data

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

Loading Google Calendar Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the VacationCalendar table.

Adding New Rows to Google Calendar

table1 = [ ['Summary','StartDateTime'], ['NewSummary1','NewStartDateTime1'], ['NewSummary2','NewStartDateTime2'], ['NewSummary3','NewStartDateTime3'] ]

etl.appenddb(table1, cnxn, 'VacationCalendar')

With the CData Python Connector for Google Calendars, you can work with Google Calendar 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 Python Connector for Google Calendars to start building Python apps and scripts with connectivity to Google Calendar 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.googlecalendar as mod

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Summary, StartDateTime FROM VacationCalendar WHERE SearchTerms = 'beach trip'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Summary','StartDateTime'], ['NewSummary1','NewStartDateTime1'], ['NewSummary2','NewStartDateTime2'], ['NewSummary3','NewStartDateTime3'] ]

etl.appenddb(table3, cnxn, 'VacationCalendar')