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

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

Connecting to Google Ad Manager Data

Connecting to Google Ad Manager 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 Ads Manager uses the OAuth authentication standard. You can authorize the data provider to access Google Ads Manager as an individual user or with a service account that you create in the Google APIs Console. See the Getting Started section in the data provider help documentation for an authentication guide.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Google Ad Manager

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

sql = "SELECT Id, Name FROM Orders WHERE Id = '2112976978'"

Extract, Transform, and Load the Google Ad Manager Data

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

Loading Google Ad Manager Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Google Ad Manager

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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

With the CData Python Connector for DoubleClick (DFP), you can work with Google Ad Manager 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 DoubleClick (DFP) to start building Python apps and scripts with connectivity to Google Ad Manager data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.googleadsmanager as mod

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, Name FROM Orders WHERE Id = '2112976978'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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