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By pairing the elasticity and pay-as-you-go nature of the cloud with the flexibility and scalability of Hadoop, Amazon Elastic MapReduce has brought Big Data analytics to an even wider array of companies looking to maximize the value of their data. Each day, thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size—from University students to Fortune 50 companies—exposing the Elastic MapReduce team to an unparalleled number of use cases. In this session, we will contrast how three of these users, Amazon.com, Yelp, and Etsy, leverage the marriage of Hadoop and the cloud to drive their businesses in the face of explosive growth, including generating customer insights, powering recommendations, and managing core operations.
Peter Sirota is the General Manager of Amazon Elastic MapReduce, a managed Hadoop web service that enables businesses, researchers, data analysts, and developers to easily and cost-effectively process vast amounts of data. Before starting Amazon Elastic MapReduce, Peter served as Sr. Manager of Software Development at Amazon Web Services leading billing, authentication, and portal teams and was responsible for launching the Amazon DevPay service. Peter holds a bachelor’s degree in computer science from Northeastern University.
Justin is a member of the Entities team at Facebook where he helps curate and build from their rich structured object and social graphs, with a focus on location. Before joining Facebook, Justin ran the Data team at foursquare. In addition to building their core data-driven products Explore and Radar, he built a team from the ground up that consisted of Engineers and Data Scientists to solve large scale data problems as foursquare’s dataset grew from half a million check-ins to over 1.5 billion. Before that, Justin worked at a hedge fund as a quantitative analyst, building custom portfolios for their asset management division and doing modeling and analysis for their risk team, specializing in high-frequency, derivatives, and commodities trading. Prior to that, he worked for Bear Stearns as a Vice-President in their fixed income analyst group, building applications and models to help value agency pass-thru securities and building loan-level pricing applications and models. Justin holds a BS in Computer Science with a minor in Mathematics from the University of Rochester and has studied graduate-level Math and Computer Science at Columbia University. He is constantly chasing the biggest and most interesting datasets and trying to make amazing things happen with them.
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