What you really want for time-series data is a column db such as cassandra (or vertica etc.), perhaps HBase, perhaps a RDBMS, or perhaps a plain old log-file.
What you most definitely don't want is Microsoft Access or MongoDB. Thinking about it, MS Access might still work to a degree.
To expand on the parent, a column store like HBase or Cassandra is perfect as each row can represent a timeslice and then each column can represent a single event or record within that timeslice. As the row gets evicted from it's initial storage in memory, it will be written to disk contiguously, in sorted order, and batch reads of this data is sequential.
It is possible to use MongoDB to store a timeslice as a document, but it is not designed to scale out to store very large numbers of columns within a single document.
What do you mean by "very large numbers of columns" ? I've seen some mongo users with very rich, i.e. large, document models.
Even without tens of thousands of concurrent users, you'll eventually run into a deeply critical performance wall when MongoDB starts reading from disk. It's really best to think of it as an in-memory database.
Vertica was not free, HBase was really heavyweight, & I knew i didn't want RDBMS. The sensor data itself is plain csvs ;)
Cassandra still does look interesting, although it looked like it would take me much longer to get it going; perhaps it will get rewritten. (The mongo solution only took a week to get something usable.)
This was around the end of 2009, and i don't think there were many clients available (i see pycassa dates to 2010-04).
again, ease of development is much more important to us than performance; it'll probably be a long, long time before our db engine is the choke point, at which point we'll have resources to use a heavier tool.