MAD skills: New analysis practices for big data

355Citations
Citations of this article
792Readers
Mendeley users who have this article in their library.
Get full text

Abstract

As massive data acquisition and storage becomes increasingly affordable, a wide variety of enterprises are employing statisticians to engage in sophisticated data analysis. In this paper we highlight the emerging practice of Magnetic, Agile, Deep (MAD) data analysis as a radical departure from traditional Enterprise Data Warehouses and Business Intelligence. We present our design philosophy, techniques and experience providing MAD analytics for one of the world's largest advertising networks at Fox Audience Network, using the Greenplum parallel database system. We describe database design methodologies that support the agile working style of analysts in these settings. We present dataparallel algorithms for sophisticated statistical techniques, with a focus on density methods. Finally, we reect on database system features that enable agile design and exible algorithm developmentusing both SQL and MapReduce interfaces over a variety of storage mechanisms. © 2009 VLDB Endowment.

Cite

CITATION STYLE

APA

Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD skills: New analysis practices for big data. Proceedings of the VLDB Endowment, 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free