I'm an engineer (the non software kind) that works in an industrial plant. I often have to do ad-hoc querying manipulation etc of data from a lot of diverse sou

I'm an engineer (the non software kind) that works in an industrial plant. I often have to do ad-hoc querying manipulation etc of data from a lot of diverse sources. Usually to do fault finding and analytics - sometimes also for reporting. I don't know about transpiling but I use SAS to work around SQL's clunkiness. I can embed SQL seamlessly into my SAS code so it works well. I use SQL for things that are simpler in SQL (selecting, filtering, aggregating data and "case when" statements etc) and I use SAS's data step language for when I have to do more complicated stuff with the data like Arrays, loops, macros, weighted averaging, time series based stuff and regressions. The thing I love about SQL is it is the lowest common denominator for all of my organisations data it doesn't mater if data is stored in an Oracle table, a Microsoft SQL Server table, or even Access DB or spreadsheet. The language to extract/merge/join all the data is the same and standardized. We also have things called "Data historians" which are basically time-series datastores (usually they are linked to PLC vendors) and nothing frustrates me more when they use some proprietary "sql like" language which seems similar to ANSI SQL except things like aggregations (SUM, AVG, MAX etc) don't work or they do weird things with data types (especially dates) or similar everyone of these seem to be subtly different - annoying.