Extract, Transform, and Load Twitter Ads Data in Python

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Twitter Ads Python Connector

Python Connector Libraries for Twitter Ads Data Connectivity. Integrate Twitter Ads with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Twitter Ads Data

Connecting to Twitter 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.

All tables require authentication. You must use OAuth to authenticate with Twitter. OAuth requires the authenticating user to interact with Twitter using the browser. For more information, refer to the OAuth section in the Help documentation.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Twitter Ads

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

sql = "SELECT EntityId, Entity FROM AdStats WHERE Entity = 'ORGANIC_TWEET'"

Extract, Transform, and Load the Twitter Ads Data

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

Loading Twitter Ads Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT EntityId, Entity FROM AdStats WHERE Entity = 'ORGANIC_TWEET'"

table1 = etl.fromdb(cnxn,sql)

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

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