![]() Use the pip utility to install the required modules and frameworks: pip install petl See the Getting Started chapter of the help documentation for a guide to using OAuth.Īfter installing the CData Facebook Ads Connector, follow the procedure below to install the other required modules and start accessing Facebook Ads through Python objects. ![]() ![]() 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. Facebook uses the OAuth authentication standard. Most tables require user authentication as well as application authentication. For this article, you will pass the connection string as a parameter to the create_engine function. Create a connection string using the required connection properties. 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).Ĭonnecting to Facebook Ads data looks just like connecting to any relational data source. With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Facebook Ads data in Python. 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 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. The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively.
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