I don't think of the options stream as big data. It is more of a "fast" data problem, where at the open you need to handle 100,000 ticks per second, doing a implied volatility calculation on each tick. It is pretty demanding, when, for example, you consider that when the price of IBM moves by one penny, you get 400 options quote right then. You end up with a billion quotes in a typical day, if memory serves correctly.
I didn't think of this as Big Data, somehow. It seems to me that big data is more complex than that. You most often just process the data, say for backtesting, in a strict series, from the start to the end, or for an interval. I think of Big Data having more structure.
MongoDB was bad for this case because it devours memory and it lacks a primary key (so saving a tick that's already creates a second tick).
cassandra is better (it has a PK, although it's a bit weird because of its distributed-first attitude) but in the long run I think postgres would be even better (because it's space efficient).
Apart from that I use some Java libraries and clojure/incanter and program the rest around it myself.
All of them are basically time series with some master data, none of them is more than a few dozen GB
So in any case, I think time series data is worth a look.