I am working on a project that must store very large datasets and associated reference data. I have never come across a project that required tables quite this large. I have proved that at least one development environment cannot cope at the database tier with the processing required by the complex queries against views that the application layer generates (views with multiple inner and outer joins, grouping, summing and averaging against tables with 90 million rows).

The RDBMS that I have tested against is DB2 on AIX. The dev environment that failed was loaded with 1/20th of the volume that will be processed in production. I am assured that the production hardware is superior to the dev and staging hardware but I just don't believe that it will cope with the sheer volume of data and complexity of queries.

Before the dev environment failed, it was taking in excess of 5 minutes to return a small dataset (several hundred rows) that was produced by a complex query (many joins, lots of grouping, summing and averaging) against the large tables.

My gut feeling is that the db architecture must change so that the aggregations currently provided by the views are performed as part of an off-peak batch process.

Now for my question. I am assured by people who claim to have experience of this sort of thing (which I do not) that my fears are unfounded. Are they? Can a modern RDBMS (SQL Server 2008, Oracle, DB2) cope with the volume and complexity I have described (given an appropriate amount of hardware) or are we in the realm of technologies like Google's BigTable?

I'm hoping for answers from folks who have actually had to work with this sort of volume at a non-theoretical level.