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

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

Connecting to Mailgun Data

Connecting to Mailgun 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.

Start by setting the Profile connection property to the location of the Mailgun Profile on disk (e.g. C:\profiles\Mailgun.apip). Next, set the ProfileSettings connection property to the connection string for Mailgun (see below).

Mailgun API Profile Settings

Generate an API key in your Mailgun account by selecting Account Settings > API Security > Create Key from the account dropdown menu.

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

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

cnxn = mod.connect("Profile=C:\profiles\Mailgun.apip;ProfileSettings='Password=your_api_key';")

Create a SQL Statement to Query Mailgun

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

sql = "SELECT Type, Value FROM Allowlists WHERE CreatedAt = '2024-01-15T10:30:00Z'"

Extract, Transform, and Load the Mailgun Data

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

Loading Mailgun Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Mailgun 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 API Driver for Python to start building Python apps and scripts with connectivity to Mailgun 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Mailgun.apip;ProfileSettings='Password=your_api_key';")

sql = "SELECT Type, Value FROM Allowlists WHERE CreatedAt = '2024-01-15T10:30:00Z'"

table1 = etl.fromdb(cnxn,sql)

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

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