It is not a stretch given that virtually not a single quant desk at any hedge fund or ibank uses sql to handle time series data. There is a reason they use KDB and other columnar specialized data bases. Take my advice or leave it, but tucking gigabytes or terabytes of time series based data into a rdbms/sql is just the wrong way to approach this. I explained why so you have the free choice of course to do anything that pleases you. Good luck with your development.
Who's using that level of data though in a strategy (talking code running constantly in an alpha/risk/trx cost/execution/pc model).
I can't imagine any possible theoretical or evidentially based strategy that needs tick data beyond a few days to run, maybe a few weeks at most.
I'm talking about lugging the data around in an RDMS for backtesting, in which case it doesn't really matter (within reason) what can run circles around what.
I believe it's worth pointing out that the Nasdaq exchange itself runs on SQL Server. So to say that financial applications consuming the data provided by a RDMS can't handle it if they themselves are a RDMS seems like a stretch.