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Extract, Transform, and Load Google Drive Data in Python

The CData Python Connector for Google Drive enables you to create ETL applications and pipelines for Google Drive 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 Google Drive and the petl framework, you can build Google Drive-connected applications and pipelines for extracting, transforming, and loading Google Drive data. This article shows how to connect to Google Drive with the CData Python Connector and use petl and pandas to extract, transform, and load Google Drive data.

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

Connecting to Google Drive Data

Connecting to Google Drive 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 Drive Connector, follow the procedure below to install the other required modules and start accessing Google Drive 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 Drive 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.googledrive as mod

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

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

Create a SQL Statement to Query Google Drive

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

sql = "SELECT Name, Size FROM Files WHERE Starred = 'true'"

Extract, Transform, and Load the Google Drive Data

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

Loading Google Drive Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Google Drive

table1 = [ ['Name','Size'], ['NewName1','NewSize1'], ['NewName2','NewSize2'], ['NewName3','NewSize3'] ]

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

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

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

sql = "SELECT Name, Size FROM Files WHERE Starred = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Size'], ['NewName1','NewSize1'], ['NewName2','NewSize2'], ['NewName3','NewSize3'] ]

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