Can Big Data Make You a Better Cyclist?

by Will Benton, Red Hat Senior Software Engineer

Last year’s Tour de France winner, Chris Froome, is a freak of nature. His endurance, performance and recovery are unmatched. However, given recent events in the sport, his consistent success over the past couple years was questioned by some. To keep the skeptics at bay, Froome’s team did something unprecedented – they released two years of Froome’s bio and performance data. The analysis not only exonerated Froome of any doping allegations, but also established that “exceptional aerobic potential” and “excellent recovery”, among other things, were factors behind his success.

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Inktank Ceph Enterprise 1.2: A chat with Colleen Corrice

Please note: Inktank Ceph has a new name: Red Hat Ceph Storage. Learn more about it, and Red Hat Gluster Storage, here: http://red.ht/1Hw7gYb

We’re here today with Colleen Corrice, marketing communications director at Inktank, to talk about the much anticipated, recently released Inktank Ceph Enterprise 1.2.

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Red Hat Storage Server + Splunk Enterprise

For any business, understanding and increasing operational efficiency is a major concern for management. In recent years, as connectivity and computing power have grown, the amount of machine-generated data companies must contend with has ballooned as well. Businesses are turning to robust software solutions to analyze this data, determine patterns, and make important decisions; these solutions require a storage platform that can handle enormous amounts of throughput and provide easy- to-scale capacity while remaining affordable.

We configured two IBM System x3650 M4 BD servers with Red Hat Storage Server 2.1, operating as a storage back end to a Splunk Enterprise infrastructure. We then ran SplunkIt 2.0 indexing and search workloads, which target Splunk hot/warm data buckets requiring fast I/O capabilities.

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You Won’t Believe Where You’d Find Big Data at the Soccer World Cup

The vast range of possibilities and outcomes in sport makes it fertile ground for number crunchers. Analysts use sophisticated algorithms to factor in everything from temperature and humidity to the materials used in the manufacturing of tournament equipment in order to predict results.

Many of these factors are prone to volatility over the course of a sporting event, making real time analysis even more challenging. A case in point is the Goldman Sachs model built on regression analysis of all team results leading up to the tournament that had to be updated mid-tournament. They predict a 2-1 victory for Brazil over the Dutch in the finals (which of course we now know is impossible after Brazil’s stunning defeat against Germany in the semi-finals).

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