Scalability: How can the system grow as your data storage needs grow? Which RDBMS and hardware
platform can handle large sets of data most efficiently? To get an idea of this, one needs to determine
the approximate amount of data that is to be kept in the data warehouse system once it's mature, and
base any testing numbers from there.
Parallel Processing Support: The days of multi-million dollar supercomputers with one single
CPU are gone, and nowadays the most powerful computers all use multiple CPUs, where each processor can
perform a part of the task, all at the same time. When I first started working with massively parallel
computers in 1993, I had thought that it would be the best way for any large computations to be done
within 5 years. Indeed, parallel computing is gaining popularity now, although a little slower than
I had originally thought.
RDBMS/Hardware Combination: Because the RDBMS physically sits on the hardware platform, there
are going to be certain parts of the code that is hardware platform-dependent. As a result,
bugs and bug fixes are often hardware dependent.
True Case: One of the projects I have worked on was with a major RDBMS provider paired with a
hardware platform that was not so popular (at least not in the data warehousing world). The DBA
constantly complained about the bug not being fixed because the support level for the particular
type of hardware that client had chosen was Level 3, which basically meant that no one in the
RDBMS support organization will fix any bug particular to that hardware platform.
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