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Extract, Transform, and Load Bing Ads Data in Python

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

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

Connecting to Bing Ads Data

Connecting to Bing 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.

The Bing Ads APIs use the OAuth 2 standard. To authenticate, you will need valid Bing Ads OAuth credentials and you will need to obtain a developer token. See the Getting Started section in the Bing Ads data provider help documentation for an authentication guide.

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

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

cnxn = mod.connect(" OAuthClientId=MyOAuthClientId; OAuthClientSecret=MyOAuthClientSecret; CallbackURL=http://localhost:portNumber; AccountId=442311; CustomerId=5521444; DeveloperToken=11112332233;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Bing Ads

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

sql = "SELECT Id, Name FROM AdGroups WHERE CampaignId = '234505536'"

Extract, Transform, and Load the Bing Ads Data

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

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect(" OAuthClientId=MyOAuthClientId; OAuthClientSecret=MyOAuthClientSecret; CallbackURL=http://localhost:portNumber; AccountId=442311; CustomerId=5521444; DeveloperToken=11112332233;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Name FROM AdGroups WHERE CampaignId = '234505536'"

table1 = etl.fromdb(cnxn,sql)

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

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