Thanks, I hadn't mentally partitioned OLAP/OLTP that way. I still think that the read/write distinction more correctly forms the dividing line.
That's because the classical OLTP approach is to use relational databases. Relational databases do well in writes because in a properly normalised DB there is one, and only one, place for each datum to go. There is no fan out and potentially no coordination. A place for everything and everything in its place. That reduces the amount of write traffic required to support complex models of the problem domain by a lot.
But of course the relational view of a problem domain doesn't really look like the humanistic view of a problem domain. And building the humanistic view of the problem domain usually means denormalising and breaking the things that made OLTP useful. Enter ETL pipes and OLAP systems.
From there it's very humanistic and it requires no understanding of the relational model. Tools can easily turn the dimensional tables into dropdown filter lists that look like a spreadsheet -- or even run inside a spreadsheet.
That's because the classical OLTP approach is to use relational databases. Relational databases do well in writes because in a properly normalised DB there is one, and only one, place for each datum to go. There is no fan out and potentially no coordination. A place for everything and everything in its place. That reduces the amount of write traffic required to support complex models of the problem domain by a lot.
But of course the relational view of a problem domain doesn't really look like the humanistic view of a problem domain. And building the humanistic view of the problem domain usually means denormalising and breaking the things that made OLTP useful. Enter ETL pipes and OLAP systems.
From there it's very humanistic and it requires no understanding of the relational model. Tools can easily turn the dimensional tables into dropdown filter lists that look like a spreadsheet -- or even run inside a spreadsheet.