I will readily admit that I am biased, A good deal of my experience has been in large high load web systems with a good deal of legacy environments. When it comes to the final end point for my data I like relational structures. Personally, I find them more adaptable to the unforeseen as far as business insight is concerned, and in the environment, that I have worked in, the unforeseen occurs daily.
For example, a marketing manager wants to aggregate data set X against Y to see the outcome. The more data I have in structures that support these unforeseen and ad-hoc requirements the more insight my organization obtain. So personally for your situation I would front cache the data in an NoSQL type structure and bulk transfer it into a relational structure at set intervals to avoid having to write logic in an application layer for each use case that comes through the door. I know that there are emerging tools in this space for the NoSQL databases, but I still find analytical and reporting easier to do in the relational world, relational models seems to lend themselves better to discovering links between data sets after the fact.
For example, a marketing manager wants to aggregate data set X against Y to see the outcome. The more data I have in structures that support these unforeseen and ad-hoc requirements the more insight my organization obtain. So personally for your situation I would front cache the data in an NoSQL type structure and bulk transfer it into a relational structure at set intervals to avoid having to write logic in an application layer for each use case that comes through the door. I know that there are emerging tools in this space for the NoSQL databases, but I still find analytical and reporting easier to do in the relational world, relational models seems to lend themselves better to discovering links between data sets after the fact.