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

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

Connecting to Facebook Data

Connecting to Facebook 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 Connector, follow the procedure below to install the other required modules and start accessing Facebook 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 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.facebook as mod

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

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

Create a SQL Statement to Query Facebook

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

sql = "SELECT FromName, LikesCount FROM Posts WHERE Target = 'thesimpsons'"

Extract, Transform, and Load the Facebook Data

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

Loading Facebook Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Facebook

table1 = [ ['FromName','LikesCount'], ['NewFromName1','NewLikesCount1'], ['NewFromName2','NewLikesCount2'], ['NewFromName3','NewLikesCount3'] ]

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

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

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

sql = "SELECT FromName, LikesCount FROM Posts WHERE Target = 'thesimpsons'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['FromName','LikesCount'], ['NewFromName1','NewLikesCount1'], ['NewFromName2','NewLikesCount2'], ['NewFromName3','NewLikesCount3'] ]

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