I've also struggled with the definition of Big Data. I typically see "size" as being the line of demarcation people throw around when thinking about Big Data, b

I've also struggled with the definition of Big Data. I typically see "size" as being the line of demarcation people throw around when thinking about Big Data, but I think it's it's actually size + latency that's the real definition. If I have a 12TB dataset, and I need to be able to find a single record in that dataset, and return the results to a process in less than one second, I don't believe that needs to be considered "big data". The dataset is "large", but the latency / access pattern surrounding that data is low enough that you can solve that with a handful of hard drives and your data sorted on disk. If I have a 12TB dataset and I need < 10ms latency on random lookups, that is now probably big data. To guarantee those sort of lookup times for random IO, I either need very expensive hardware, or a software based solution to distribute my data to multiple machines. If I have 10GB of census data, and I need to run an analysis that shows what are the 100 most similar cities (non approximation) to Los Angeles in terms of demographic make up, and I need an answer in less than 1 second, that is also probably big data. The analysis is easily parallelized, but if done serially on a single machine is a "slow" process. I realize this is a moderately unconventional way of approaching Big Data, but it's how I've started to structure some of my thinking and explanations around why it's not just size, but size + latency (which is inherently a question of what you're trying to do with the data).