Arthur Charpentier shows us the math behind using MapReduce to parallelize a linear regression:
Sometimes, with big data, matrices are too big to handle, and it is possible to use tricks to numerically still do the map. Map-Reduce is one of those. With several cores, it is possible to split the problem, to map on each machine, and then to aggregate it back at the end.
Arthur gives us an interesting example in R to boot.