A comparison of approaches to large-scale data analysis

Pavlo, Andrew; Paulson, Erik; Rasin, Alexander; Abadi, Daniel J.; DeWitt, David J.; Madden, Samuel; Stonebraker, Michael

There is currently considerable enthusiasm around the MapReduce
(MR) paradigm for large-scale data analysis [17]. Although the
basic control flow of this framework has existed in parallel SQL
database management systems (DBMS) for over 20 years, some
have called MR a dramatically new computing model [8, 17]. In
this paper, we describe and compare both paradigms. Furthermore,
we evaluate both kinds of systems in terms of performance and de-
velopment complexity. To this end, we define a benchmark con-
sisting of a collection of tasks that we have run on an open source


HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads

Abouzeid, Azza; Bajda-Pawlikowski, Kamil; Abadi, Daniel; Silberschatz, Avi; Rasin, Alexander

The production environment for analytical data management ap-
plications is rapidly changing. Many enterprises are shifting away
from deploying their analytical databases on high-end proprietary
machines, and moving towards cheaper, lower-end, commodity
hardware, typically arranged in a shared-nothing MPP architecture,
often in a virtualized environment inside public or private “clouds”.
At the same time, the amount of data that needs to be analyzed is
exploding, requiring hundreds to thousands of machines to work in
parallel to perform the analysis.

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