In the data warehouse / OLAP space, I think we are heading towards a world where the underlying data storage consists of partitioned Arrow arrays, and you can r

In the data warehouse / OLAP space, I think we are heading towards a world where the underlying data storage consists of partitioned Arrow arrays, and you can run arbitrary UDFs in arbitrary languages on the (read-only) raw data with zero copy and essentially no overhead other than the language runtime itself and marshalling of the data that is emitted from the UDF. Something like the Snowflake data storage model + DuckDB as an execution engine + a Pandas/Polars-like API. There is no reason why we have to be stuck with "database == SQL" all the time. SQL is extremely powerful, but sometimes you need a bit more, and in that case we shouldn't be so constrained. But in general yes, the world is gradually waking up to the idea that performance matters, and that data locality can be extremely important for performance, and that doing your computations and data processing on your warehouse data in-place is going to be a huge time and money saver in the longer term.