Replacing SQL with Kotlin's 'dataframe' on the Las Vegas Strip
summary
A working Kotlin pipeline replaces a mostly SQL/Excel process and reduces reliance on SQL by about 80–90%, using Kotlin dataframe as the transformation engine inside Kotlin dataframe in Jupyter notebooks with Kotlin kernel, joining 6 sources into 16 tiers (exploded to 32 rows), producing per-player rows and 10 gaming periods, and writing CSVs or calling APIs via HTTP4K/Ktor. Performance limits appear for multi‑gigabyte workloads because the JVM lacks a vector API; functionality is solid for typical business ETL.
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chapters (13)
- 0:00Welcome
- 1:29Kotlin in casino tech
- 2:33Introducing Kotlin DataFrame
- 5:22DataFrame advantages
- 9:18DataFrame transformations
- 13:33Replacing Excel pipelines
- 18:53Live coding marketing pipeline
- 24:12Pivoting and merging data
- 27:05Personalized offer messages
- 31:26Date formatting edge cases
- 33:55Production integration
- 35:20Limitations and future
- 37:51Closing remarks
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