Many (if not most) companies reach a point when data becomes a priority. This implies building out an internal practice to integrate into existing systems and processes to deliver the sought insights. In a field so wide, relatively recent and infamous for its buzzwords-per-second count, formalising problems and making explainable decisions is the only route that won't see you run out of resources and people's patience.
Metabase, a brilliant open-source BI tool, allows you to define your data model on top of the database schema, which includes descriptions, special types and table relationships. However, updating everything manually is not repeatable and prone to errors, so I will demonstrate how to export your existing dbt schema into Metabase automatically with a tool I created.
Kotlin continues to conquer new areas of software development, but some are still firmly held by one or two languages. Data science, statistical analysis and machine learning are largely Python domains, but with many performance-focused implementations based on the JVM, Kotlin has a good chance to break into this scene too. The intention of this talk is to give a shallow overview of what you can do today.