Illustration by Marius Watz
Parallel DBMSs excel at efficient querying of large data sets; MapReduce-style systems excel at complex analytics and ETL tasks. Neither is good at what the other does well. Hence, the two technologies are complementary.
The finding that Vertica is faster than Hadoop or DBMS-X would be more credible if the article's author were not CTO and co-founder of Vertica, a fact nowhere mentioned in the article or author listing.
See http://www.vertica.com/leadership
It is interesting to see both of the articles come up. Anyway, the Google MapReduce is not as the same as Hadoop, which leaves us a mysterious comparison. Also I think the invention of MapReduce itself is not for research but for solving their own problems, not elegant in an academic way.
This is an improvement over Stonebraker's other writings related to MapReduce and NoSQL, but still a very slanted view. The authors pit Hadoop, a specific (and imperfect) implementation of MapReduce against the idealized conception of parallel DBMS's. Even their tests are slanted to show Vertica in a good light (an important fact to consider is Stonebraker's vested interest in Vertica coming out ahead). The article by Dean and Ghemawat nicely illustrates the fallacies in the comparison paper and show just where Stonebraker went wrong (again).