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

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

Connecting to Facebook Ads Data

Connecting to Facebook 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.

Most tables require user authentication as well as application authentication. Facebook uses the OAuth authentication standard. To authenticate to Facebook, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app with Facebook.

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

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

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

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

Create a SQL Statement to Query Facebook Ads

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

sql = "SELECT AccountId, Name FROM AdAccounts WHERE Name = 'Acct Name'"

Extract, Transform, and Load the Facebook Ads Data

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

Loading Facebook Ads Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Facebook Ads

table1 = [ ['AccountId','Name'], ['NewAccountId1','NewName1'], ['NewAccountId2','NewName2'], ['NewAccountId3','NewName3'] ]

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

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

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

sql = "SELECT AccountId, Name FROM AdAccounts WHERE Name = 'Acct Name'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['AccountId','Name'], ['NewAccountId1','NewName1'], ['NewAccountId2','NewName2'], ['NewAccountId3','NewName3'] ]

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