Casinos have long been pioneers in brick & mortar customer spend tracking and subsequent 1-to-1 customized marketing. Gamble more, and you'll receive more free meals, rooms, suites, event tickets, limos and of course, free money to gamble with. Most internal workflows for executing these marketing campaigns are SQL-based, often hidden underneath extremely expensive "marketing automation systems" or complex ETL processes.
Seeking a simpler, more flexible alternative, I wanted to migrate as much as possible from SQL to code. Specifically, Kotlin code. Fortunately, Kotlin's dataframe library, although still pre-beta, was in active development and was gaining features. This turned out to be the perfect solution -- and a major improvement over the most obvious alternative, Python's 'pandas.'
We'll walk through how I reduced reliance on SQL by about 80-90% and replaced it with a faster, easier, more flexible, integratable process based upon dataframe. We'll also review some of dataframe's top features in compiled code and in Jupyter notebooks and discuss why it is so much easier, less error-prone, and just way more fun than 'pandas' can ever hope to be.