This page provides you with instructions on how to extract data from Mandrill and analyze it in Tableau. (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.
What is Tableau?
Tableau is one of the world's most popular analysis platforms. The software helps companies model, explore, and visualize their data. It also offers cloud capabilities that allow analyses to be shared via the web or company intranets, and its offerings are available as both installed software and as a SaaS platform. Tableau is widely known for its robust and flexible visualization capabilities, which include dozens of specialized chart types.
In addition to its business software, Tableau also offers a free product called Tableau Public for analyzing open data sets. If you're new to Tableau, this offering is a great way to experience Tableau's capabilities at no cost and share your work publicly.
Getting data out of Mandrill
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.
Loading data into Tableau
Analyzing data in Tableau requires putting it into a format that Tableau can read. Depending on the data source, you may have options for achieving this goal, but the best practice among most businesses is to build a data warehouse that contains the data, and then connect that data warehouse to Tableau.
Tableau provides an easy-to-use Connect menu that allows you to connect data from flat files, direct data sources, and data warehouses. In most cases, connecting these sources is simply a matter of creating and providing credentials to the relevant services.
Once the data is connected, Tableau offers an option for locally caching your data to speed up queries. This can make a big difference when working with slower database platforms or flat files, but is typically not necessary when using a scalable data warehouse platform. Tableau's flexibility and speed in these areas are among its major differentiators in the industry.
Analyzing data in Tableau
Tableau's report-building interface may seem intimidating at first, but it's one of the most powerful and intuitive analytics UIs on the market. Once you understand its workflow, it offers fast and nearly limitless options for building reports and dashboards.
If you're familiar with Pivot Tables in Excel, the Tableau report building experience may feel somewhat familiar. The process involves selecting the rows and columns desired in the resulting data set, along with the aggregate functions used to populate the data cells. Users can also specify filters to be applied to the data and choose a visualization type to use for the report.
You can learn how to build a report from scratch for free (although a sign-in is required) from the Tableau documentation.
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 Tableau 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, Mandrill to Azure Synapse Analytics, Mandrill to PostgreSQL, Mandrill to Panoply, and Mandrill to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Mandrill with Tableau. With just a few clicks, Stitch starts extracting your Mandrill data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Tableau.