Extract, Transform, and Load Twilio Data in Python

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Twilio Python Connector

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



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

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

Connecting to Twilio Data

Connecting to Twilio 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.

Use the AccountSid and AuthToken connection properties to access data from your account. You obtain your live credentials on your Twilio account dashboard. Click Account -> Account Settings to obtain your test credentials.

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

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

cnxn = mod.connect("AccountSid=MyAccountSid;AuthToken=MyAuthToken;")

Create a SQL Statement to Query Twilio

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

sql = "SELECT To, Duration FROM Calls WHERE StartTime = '1/1/2016'"

Extract, Transform, and Load the Twilio Data

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

Loading Twilio Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Twilio

table1 = [ ['To','Duration'], ['NewTo1','NewDuration1'], ['NewTo2','NewDuration2'], ['NewTo3','NewDuration3'] ]

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

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

cnxn = mod.connect("AccountSid=MyAccountSid;AuthToken=MyAuthToken;")

sql = "SELECT To, Duration FROM Calls WHERE StartTime = '1/1/2016'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['To','Duration'], ['NewTo1','NewDuration1'], ['NewTo2','NewDuration2'], ['NewTo3','NewDuration3'] ]

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