Mandrill to Google Data Studio

This page provides you with instructions on how to extract data from Mandrill and analyze it in Google Data Studio. (If the mechanics of extracting data from Mandrill seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Mandrill?

Mandrill is a transactional email API for MailChimp users. MailChimp, as you may know, is a marketing automation platform that businesses use to send out more than a billion email messages every day. The Mandrill service is a MailChimp add-on that businesses can use to send personalized, one-to-one ecommerce email messages or automated transactional email. The Mandrill API lets developers not only send email programmatically, but also access reporting data.

Getting data out of Mandrill

The Mandrill API has clients or wrappers for Ruby, Python, Node.js, PHP, and JavaScript. Suppose you want to use Python to extract the data from Mandrill and load it into a data warehouse such as Amazon Redshift. Your first step is to use pip to install the Mandrill API client with a command like sudo pip install mandrill.

Once you have a copy of the Mandrill library, you can start coding with it. Import the library module and instantiate the Mandrill class with this code:

import mandrill
mandrill_client = mandrill.Mandrill('YOUR_API_KEY')

You can then begin accessing data with calls like:

    mandrill_client = mandrill.Mandrill('YOUR_API_KEY')
    result = mandrill_client.exports.info(id='example id')

The returned data will include a URL you can use to fetch the results, which are returned as a ZIP archive. You must then unzip the results to generate a CSV file. You may have to run multiple export commands to get all the data you want, in multiple files.

Keeping Mandrill data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Mandrill.

And remember, as with any code, once you write it, you have to maintain it. If Mandrill modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Mandrill to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Mandrill data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Mandrill to Redshift, Mandrill to BigQuery, and Mandrill to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Mandrill data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.