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

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

Connecting to Google Ads Data

Connecting to Google Ads 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, specify the DeveloperToken and ClientCustomerId.

See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

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

Create a SQL Statement to Query Google Ads

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

sql = "SELECT Device, Clicks FROM CampaignPerformance WHERE Device = ''Mobile devices with full browsers''"

Extract, Transform, and Load the Google Ads Data

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

Loading Google Ads Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

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

sql = "SELECT Device, Clicks FROM CampaignPerformance WHERE Device = ''Mobile devices with full browsers''"

table1 = etl.fromdb(cnxn,sql)

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

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