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

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

Connecting to Email Data

Connecting to Email 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.

The User and Password properties, under the Authentication section, must be set to valid credentials. The Server must be specified to retrieve emails and the SMTPServer must be specified to send emails.

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

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

cnxn = mod.connect("User=username@gmail.com;Password=password;Server=imap.gmail.com;Port=993;SMTP Server=smtp.gmail.com;SMTP Port=465;SSL Mode=EXPLICIT;Protocol=IMAP;Mailbox=Inbox;")

Create a SQL Statement to Query Email

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

sql = "SELECT Mailbox, RecentMessagesCount FROM Mailboxes WHERE Mailbox = 'Spam'"

Extract, Transform, and Load the Email Data

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

Loading Email Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Email

table1 = [ ['Mailbox','RecentMessagesCount'], ['NewMailbox1','NewRecentMessagesCount1'], ['NewMailbox2','NewRecentMessagesCount2'], ['NewMailbox3','NewRecentMessagesCount3'] ]

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

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

cnxn = mod.connect("User=username@gmail.com;Password=password;Server=imap.gmail.com;Port=993;SMTP Server=smtp.gmail.com;SMTP Port=465;SSL Mode=EXPLICIT;Protocol=IMAP;Mailbox=Inbox;")

sql = "SELECT Mailbox, RecentMessagesCount FROM Mailboxes WHERE Mailbox = 'Spam'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Mailbox','RecentMessagesCount'], ['NewMailbox1','NewRecentMessagesCount1'], ['NewMailbox2','NewRecentMessagesCount2'], ['NewMailbox3','NewRecentMessagesCount3'] ]

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