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

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

Connecting to Postmark Data

Connecting to Postmark 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.

Using API Key Authentication

Postmark uses server API tokens to authenticate requests. Each Postmark server has its own API token, which controls access to messages, bounces, templates, and statistics associated with that server.

To obtain your Server API Token, log in to your Postmark account and navigate to the server you want to connect to. Go to API Tokens under the server settings and copy the token labeled Server API token.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Postmark Server API Token. This value is sent as the X-Postmark-Server-Token header on every request.

Example connection string:

Profile=C:\profiles\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"

Connecting to Postmark

Once the authentication is configured, you can connect to Postmark and query data from any of the available tables such as OutboundMessages, Bounces, and Templates.

After installing the CData Postmark Connector, follow the procedure below to install the other required modules and start accessing Postmark 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 Postmark 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 Postmark Connector to create a connection for working with Postmark data.

cnxn = mod.connect("Profile=C:\profiles\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"")

Create a SQL Statement to Query Postmark

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

sql = "SELECT ,  FROM Bounces WHERE  = ''"

Extract, Transform, and Load the Postmark Data

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

Loading Postmark Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Postmark 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 Postmark 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\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"")

sql = "SELECT ,  FROM Bounces WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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