Replacing SQL with Kotlin's 'dataframe' on the Las Vegas Strip

In this talk, Andrew Goldberg explores how to replace SQL-based workflows with Kotlin's 'dataframe' library, particularly in the context of casino and hospitality industries for customer spend tracking and marketing campaigns. He discusses the advantages of using Kotlin's dataframe over Python's pandas, highlighting its strong typing, chainable methods, and seamless integration with Kotlin code. The presentation includes a live coding demonstration using Jupyter notebooks to showcase data manipulation, transformations, and the creation of customized marketing offers.

Andrew also touches upon the limitations of the current beta version of the library, such as potential speed issues compared to pandas and R due to the lack of a vector API in the JVM, but emphasizes its suitability for most business processes. He looks forward to future improvements, including API expansion and better interop with projects like Apache Arrow and DuckDB.