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This tutorial will explain MapReduce and how to develop big data applications in Java and high level languages such as Pig and Hive SQL. Using examples it will cover how to prototype, debug, monitor, test and optimize big data applications for Hadoop. Attendees will get hands-on instruction and will leave with a solid understanding of how to analyze data on Hadoop clusters and practical examples.
by Mark Madsen
There has been an explosion in database technology designed to handle big data and deep analytics from both established vendors and startups. This session will provide a quick tour of the primary technology innovations and systems powering the analytic database landscape.
by JC Herz
This presentation lays bare the dark underbelly of analytics in the enterprise. Drawing on darkly humorous experiences, the speaker will explain why executives treat analytics as an occult phenomenon. The talk will give executives the mental tools to separate strategically valuable analytics projects from fishing expeditions, and provide litmus tests to keep the witch doctors honest.
by Mark Madsen
Big data and analytics have developed a mythology rooted in underlying assumptions. We need to ignore these myths and think clearly about how organizations use data, which means understanding how people use information and make decisions.
by Kevin Weil
Most analytics systems rely on large offline computations, which means results come in hours or days behind. Twitter is all about realtime, but with over 160 million users producing over 90 million tweets per day, we need realtime analytics that scaled horizontally. This talk discusses the development of that infrastructure, as well as the products we are beginning to build on top of it.
1st–3rd February 2011