That's why data warehouses rely upon cubes/OLAP for analysis. It is a specialized solution that serves the need very well.
>One great point about MongoDB is that it makes the ETL process a lot easier (you don't have to prepare tables with the right schema and it supports large amounts of data).
So does a CSV. In fact, so does the last silver bullet, which is XML. XML is a loose or as strict as you want it to be.
OLAP on SQL comes at a cost, too (which is why some people are diving into analytics and reporting with NoSQL tools, where you can add one server without large expenses).
On CSV/XML, my point wasn't clear enough: I wanted to underline the fact that it's a lot easier to load dimensions data then load facts data and achieve foreign keys lookups when working with MongoDB (it's not about the file format, it's about the loading/lookup part which is a large part of ETL in my cases).
For the people who need OLAP analysis, the relevant expense range is seldom that much of a consideration. I'm looking at storage systems right now that costs $800,000. It's considered mid-range and is merely the starter system.
Note that OLAP is, in many regards, NoSQL. It is really the most successful variant of NoSQL.
However I'm very curious what sort of analytics people are doing with NoSQL. I have seen people essentially generating reports to MongoDB, for instance, but I have never seen anything remotely approaching flexible analytics on such a system.
That's why data warehouses rely upon cubes/OLAP for analysis. It is a specialized solution that serves the need very well.
>One great point about MongoDB is that it makes the ETL process a lot easier (you don't have to prepare tables with the right schema and it supports large amounts of data).
So does a CSV. In fact, so does the last silver bullet, which is XML. XML is a loose or as strict as you want it to be.