By basing your system on a journaled event stream, you start with a foundation of complete history retention, and you can build exactly the sort of reporting views you need at any time (say, by creating a SQL database for other applications to query).
FTFY...
It's not hard to build history into a SQL table design. You can even store events in a...wait for it... SQL database. I have built numerous systems backed by SQL databases that have complete history retention. Answering questions like 'who had id 'X' on this date 3 years ago' are easily solvable with basic standard sql.
I certainly don't believe SQL databases are perfect or the tool for every job, but in many cases they work just fine until you get into very large datasets. Admittedly, I only deal with databases in the 100s of GB range so I have yet to personally run into the scaling problems that a Google or Facebook have and the SQL backed systems I have built work just fine.
If your system operates in this journaled/event-sourcing way at the most basic level then you have the ultimate future-proof storage layer. You could decide to completely change the way the data is stored and represented (in-memory or otherwise) at any time, as long as you have that raw history.
For example if you only want customers to create orders you can set a foreign key between customer and order. If you don't delete orders and customers, then the database will allow you to insert new orders against old customers, unless you apply a more complex constraint.
That leaves tracking the remaining inserts and updates, which is a well understood problem. It's called Auditing. Here is a simple script that will auto-audit a SQL Server database... variations in other SQL dialects are likely just as straighforward.
http://www.geekzilla.co.uk/ViewECBC0CC3-1C7E-4E7E-B243-F2F25...
But I wouldn't write off the noDB approach for various transitional data, or data that isn't mean to live long anyway, like tweets.
There are a lot of questions that make the choice of data store a difficult one, but I'm not sure that plays into it at all.
I suppose it would be equally possible to think of the main data tables as a "reporting view" in the sense you use here, except that the application was 1000:1 or more in update frequency to read frequency, and all the reads were performed on the main data tables, so that's kind of a "tail wagging the dog" view of the app.
For an application with different requirements, your view of things might be quite useful, of course.