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

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

Connecting to Twitter Data

Connecting to Twitter 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 can connect using your User and Password or OAuth. To authenticate using OAuth, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can register an app to obtain your own.

If you intend to communicate with Twitter only as the currently authenticated user, then you can obtain the OAuthAccessToken and OAuthAccessTokenSecret directly by registering an app.

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

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

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

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

Create a SQL Statement to Query Twitter

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

sql = "SELECT From_User_Name, Retweet_Count FROM Tweets WHERE From_User_Name = 'twitter'"

Extract, Transform, and Load the Twitter Data

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

Loading Twitter Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Twitter

table1 = [ ['From_User_Name','Retweet_Count'], ['NewFrom_User_Name1','NewRetweet_Count1'], ['NewFrom_User_Name2','NewRetweet_Count2'], ['NewFrom_User_Name3','NewRetweet_Count3'] ]

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

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

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

sql = "SELECT From_User_Name, Retweet_Count FROM Tweets WHERE From_User_Name = 'twitter'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['From_User_Name','Retweet_Count'], ['NewFrom_User_Name1','NewRetweet_Count1'], ['NewFrom_User_Name2','NewRetweet_Count2'], ['NewFrom_User_Name3','NewRetweet_Count3'] ]

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