BigQuery (Dremel) vs. MapReduce

http://highscalability.com/blog/2010/8/4/dremel-interactive-analysis-of-web-scale-datasets-data-as-a.html

You may be asking: Why use Dremel when you have MapReduce? I think Kevin McCurley, a Google Research Scientist, answers this nicely:
The first step in research is to form a speculative hypothesis. The real power of Dremel is that you can refine these hypotheses in an interactive mode, constantly poking at massive amounts of data. Once you come up with a plausible hypothesis, you might want to run a more complicated computation on the data, and this is where the power of MapReduce comes in. These tools are complementary, and together they make a toolkit for rapid exploratory data intensive research.

So, Dremel is a higher level of abstraction than MapReduce and it fits as part of an entire data slice and dice stack. Another pancake in the stack is Pregel, a distributed graph processing engine. Dremel can be used against raw data, like log data, or together with MapReduce, where MapReduce is used to select a view of the data for deeper exploration.
Dremel occupies the interactivity niche because MapReduce, at least for Google, isn't tuned to return results in seconds. Compared to MapReduce, Dremel's query latency is two orders of magnitude faster. MapReduce is "slow" because it operates on records spread across a distributed file system comprised of many thousands of nodes.