אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Over the past few years, Hadoop has been exploding in popularity. Ryan Hennig will discuss an overview of the MapReduce paradigm used to process data in Hadoop 1.0, and how it's shortcomings have motivated the new YARN architecture in Hadoop 2.0, which is better suited to scenarios like graph processing and machine learning. Chris Severs will discuss how functional programming is particularly well-suited to computation in the Big Data world, including the only four functions you really need, and how to effectively use concepts from algebra and linear algebra in Big Data computation.

Bio:
Chris Severs, Ph.D. -Chris works as an Applied Researcher/Software Engineer at eBay. He uses Hadoop daily to run jobs on large eBay data sets. He is an official contributor on Twitter's Scalding project and author of the scalding-avro module for reading Avro files. Chris’ passion for Hadoop, scalding and Scala has been shared in and outside eBay in numerous talks. Prior to joining eBay, Chris was a postdoctoral researcher in mathematics at Reykjavík University in Iceland and at The Mathematical Sciences Research Institute in Berkeley.

Ryan Hennig -Ryan Hennig has studied Computer Science and Physics at the University of Washington. His work experience includes companies such as Classmates.com, Amazon and Microsoft. In early 2012, he joined eBay's Hadoop Platform Team, which operates some of the largest Hadoop clusters in the world. In this capacity, he won eBay’s senior executive award for innovation. In addition to his day job, Ryan has partnered with outside organizations to sponsor hackathons around the US targeted at passionate young technologists at the University and High-school levels. His interests for the future of Big Data include easier manageability via virtualization and tools, and support for alternate workloads such as Graph Processing.