Here is a link to the transcript of Dana Gardner’s BriefingsDirect podcast on new technical approaches to managing massive data problems using parallel processing and MapReduce technologies. Dana is a principal analyst at Interarbor Solutions.
“Dana Gardner: Hi, this is Dana Gardner, principal analyst at Interarbor Solutions, and you’re listening to BriefingsDirect. Today we present a sponsored podcast discussion on new data-crunching architectures and approaches, ones designed with petabyte data sizes in their sights.
It’s now clear that the Internet-size data gathering, swarms of sensors, and inputs from the mobile device fabric, as well as enterprises piling up ever more kinds of metadata to analyze, have stretched traditional data-management models to the breaking point.
In response, advances in parallel processing, using multi-core chipsets have prompted new software approaches such as MapReduce that can handle these data sets at surprisingly low total cost.
We’ll examine the technical underpinnings that support the new demands being placed on, and by, extreme data sets. We’ll also uncover the means by which powerful new insights are being derived from massive data compilations in near real time.
Here to provide an in-depth look at parallelism, modern data architectures, MapReduce technologies, and how they are coming together, is Joe Hellerstein, professor of computer science at UC Berkeley. Welcome, Joe.”
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