Next Generation DSRs - Scale Out !!

Last week, I posted my thoughts on how new technology enables a simpler and faster database to support your DSR applications.Next Generations DSRs (data handling).  Over the next few posts I'll extend that idea to show how speed and simplicity are essential to your personal productivity, user experience and the ability to apply powerful analytic tools to your data.

In the meantime though I came across a great post in Rob Klopp's "Database Fog Blog" regarding Redshift. Redshift is Amazon's cloud-based, columnar, parallel database.

Remember that my interest in database technology is all about feeding my insatiable desire for data to drive value-added analytics, my own area of expertise. To that end, I have become adept in a number of programming languages and relational database systems and while I'm a lot better than "competent" I am not "expert". Rob clearly is an expert in this field and I will be following his posts carefully.

Here's a highlight from his post Thoughts on AWS Redshift:
... if you can add nodes and scale out to improve query response then why not throw hardware at performance problems rather than build a fragile infrastructure of aggregate tables, cubes, pre-joined/de-normalized marts, materialized views, indexes, etc. Each of these performance workarounds are both expensive to build and expensive to operate.
He goes on to talk about why scale-out has not been generally adopted and how Amazon Redshift changes the game by making it easy to acquire and release processing power on demand.

The answer does not have to be Redshift, perhaps it's Impala or Hekaton or... whatever.  Bottom line for me is that new technology enables DSR's that are simpler and faster and that creates a fundamental shift in system capability.

FYI - I have done some DSR-scale testing with Redshift and the results were very impressive.  More on that soon.

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