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

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

Connecting to Google Analytics Data

Connecting to Google Analytics 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.

Google uses 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, set Profile to the profile you want to connect to. This can be set to either the Id or website URL for the Profile. If not specified, the first Profile returned will be used.

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

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

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

Create a SQL Statement to Query Google Analytics

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

sql = "SELECT Browser, Sessions FROM Traffic WHERE Transactions = '0'"

Extract, Transform, and Load the Google Analytics Data

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

Loading Google Analytics Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

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

sql = "SELECT Browser, Sessions FROM Traffic WHERE Transactions = '0'"

table1 = etl.fromdb(cnxn,sql)

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

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