
Release 2.0.11 February 2009 Big Data: Technologies and Techniques for
Large-Scale Data
Roger Magoulas and Ben Lorica
36 :
finance, network monitoring, manufacturing, retail management, and sensor networks. Conventional database systems are not
particularly suited to responding to queries that involve continuous and unbounded streams of data. In most traditional data
warehouses, data is first loaded, and then indexed, before finally being queried. Fortunately, a different set of tools are being
developed and introduced to handle problems involving continuous data streams and queries. A promising academic effort
from Stanford University led to a prototype DSMS (Data Stream Management System), built to handle declarative continuous
queries over data streams and traditional static data sets.
More recently, several startups have attempted to tackle real-time analytics. Silicon Valley-based Truviso has unveiled a scal-
able platform that extends standard SQL to query live streams of data. Queries run continuously and concurrently, producing
instant results. Blog search engine Technorati found that with Truviso’s technology, it could easily redraw its live channel tag
clouds every few minutes. To help enable supply-chain and logistics optimization, Truviso has also rolled out products designed
to help retailers monitor demand in real time. Founded by Michael Stonebraker, Massachusetts company ...