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.

PostgreSQL, a.k.a. Postgres, proclaims itself "the world's most advanced open source database." The popular open source object-relational database management system (ORDBMS) offers enterprise-grade features with a strong emphasis on extensibility and standards compliance.

PostgreSQL runs on all major operating systems, including Linux, Unix, and Windows. It's fully ACID-compliant and supports a roster of features: foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL serves as the back end for many web systems and software tools, and is available in cloud-based deployments from most major cloud vendors. PostgreSQL's syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless, and makes Postgres a good stepping-stone for developers who may later use Redshift's data warehouse platform.

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.

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.

For simple, day-to-day data insertion, running INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.

For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.

The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.

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.

PostgreSQL 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, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.