Cassandra is the obvious choice. With MongoDB or any RDBMS, you will hold all ticks in a table (collection in Mongo-speak) and index by ticker. This means that when you want to retrieve data for a ticker, the data will not be contiguously stored, and you will have a massive usage of index and random reads. Even with SSDs this is slow. For 500k ticks into Python from Mongo it takes me well over 200 seconds on a single 16GB SSD-equipped I7. Yes you can cluster, but the starting point is poor. Imagine if you need to bring in 100 tickers?
With Cassandra, everything is stored in column families. Big dicts of dicts, basically. You get entirely sequential storage of each ticker, meaning you can go back to spinning HDs if you want, but with SSDs it positively screams for data retrieval. I'm talking under 15 seconds for the same as the above. I'm not even going to go into the ease with which adding nodes to Cassandra speeds this up. The 15-second average is on a single computer. Lots of the "reasons for Cassandra" on the web talk about its easy scale-out using multiple nodes, and it's true this will make it even faster / safer, but for me this sequential columnar storage is what makes it perfect for finance. It starts out geared for timeseries, wherease Mongo does not. The design gives you at least an order of magnitude out-of-the-box before you even start messing with clusters and/or optimization. If you know Python, the analogy is simple. Cassandra is to Mongo (or an RDBMS) as Numpy is to Python lists. Chained pointers to scattered datapoints as opposed to a big chunk of contiguous data.
The converse of course is also true. If your use case is single (few) record(s) amongst millions (typical in web applications), Mongo makes this much easier than Cassandra. Cassandra is for big data, whereas Mongo/RDBMS map better to website-userID style workloads. Parodoxically, while SQL for example is considered quite rigid compared with "NoSQL", in my opinion, Cassandra is even more rigid than relational databases. But boy do you get performance if you accept its structures.
BTW Hbase is another columnar choice but I have no experience with it. The main message is, forget about RDBMS or "traditional" NoSQL. These are bad choices for multiple time series. What you want is a column database, of which Cassandra is an excellent example.