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Extract, Transform, and Load Gmail Data in Python

The CData Python Connector for Gmail enables you to create ETL applications and pipelines for Gmail data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Gmail and the petl framework, you can build Gmail-connected applications and pipelines for extracting, transforming, and loading Gmail data. This article shows how to connect to Gmail with the CData Python Connector and use petl and pandas to extract, transform, and load Gmail data.

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

Connecting to Gmail Data

Connecting to Gmail 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.

There are two ways to authenticate to Gmail. Before selecting one, first ensure that you have enabled IMAP access in your Gmail account settings. See the "Connecting to Gmail" section under "Getting Started" in the installed documentation for a guide.

The User and Password properties, under the Authentication section, can be set to valid Gmail user credentials.

Alternatively, instead of providing the Password, you can use the OAuth authentication standard. To access Google APIs on behalf on individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, you will need to provide the User. See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

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

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

cnxn = mod.connect("User=username;Password=password;")

Create a SQL Statement to Query Gmail

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

sql = "SELECT Subject, Size FROM Inbox WHERE From = 'test@test.com'"

Extract, Transform, and Load the Gmail Data

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

Loading Gmail Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Gmail

table1 = [ ['Subject','Size'], ['NewSubject1','NewSize1'], ['NewSubject2','NewSize2'], ['NewSubject3','NewSize3'] ]

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

With the CData Python Connector for Gmail, you can work with Gmail 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 Gmail Python Connector to start building Python apps and scripts with connectivity to Gmail 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.gmail as mod

cnxn = mod.connect("User=username;Password=password;")

sql = "SELECT Subject, Size FROM Inbox WHERE From = 'test@test.com'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Subject','Size'], ['NewSubject1','NewSize1'], ['NewSubject2','NewSize2'], ['NewSubject3','NewSize3'] ]

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