Mandrill to Snowflake

This page provides you with instructions on how to extract data from Mandrill and load it into Snowflake. (If this manual process sounds onerous, 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 Snowflake?

Snowflake is a cloud-based data warehouse implemented as a managed service running on Amazon Web Services EC2 and S3 instances. Snowflake separates compute and storage resources, enabling users to scale the two independently and pay only for resources used. It provides native support for JSON, Avro, XML, and Parquet data, and can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.

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.

Preparing data for Snowflake

You may need to prepare your data before loading it. Check Snowflake's supported data types and make sure that your data maps neatly to them.

Note that you won't need to define a schema in advance when loading JSON or XML data into Snowflake.

Loading data into Snowflake

Snowflake's documentation outlines a Data Loading Overview that can help you with the task of loading your data. If you're not loading a lot of data, look into the data loading wizard in the Snowflake web UI, but for many organizations, the limitations on that tool will make it a non-starter as a reliable ETL solution. Instead:

  • Use the PUT command to stage files.
  • Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

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.

Other data warehouse options

Snowflake is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, 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 your Snowflake data warehouse.