This page provides you with instructions on how to extract data from Mandrill and load it into Redshift. (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.
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 Redshift
Once you've identified the columns you want to insert, you can use the Redshift CREATE TABLE statement to set up a table to receive all of the data.
To populate that table, you might be tempted to use INSERT statements to add data to your Redshift table row by row. Don't do that; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to load the data into Amazon S3 and use the COPY command to migrate it into Redshift.
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
Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, To Panoply, To Azure SQL Data Warehouse, and To S3.
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 move data from Mandrill to Redshift 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 Redshift data warehouse.