Recorded at EclipseCon NA 2015, Jay Jay Billings talks to Alex Blewitt about the recently created Eclipse Science working group, including the challenges involved in data visualisation of petabytes of data and on highly parallel computing that might have hundreds of thousands of processes all processing data.
Juergen Hoeller has been leading the development of the Spring core framework for over 10 years. In this interview, we get a glimpse of the passion and the insight that drive Spring. Some of the topics covered include Spring 4, adoption of Java 8, moving Spring forward, Spring Boot, enterprise features, Spring XD, and much more.
Amber Case explains how Esri handles GIS data, how to integrate small teams and startups into established companies, handling competent jerks and other types of team members, and much more.
Chris Mattmann explains the type and magnitude of data produced in scientific projects like the Square Kilometer Array Telescope, the tools to use for scientific data processing and much more.
Nathan Marz explains the ideas behind the Lambda Architecture and how it combines the strengths of both batch and realtime processing as well as immutability. Also: Storm, Clojure, and much more.
Hadoop, the distributive file system and MapReduce are just a few of the topics covered in this interview recorded live at QCon San Francisco 2013. Industry-standard Agile implementation and a lot of testing, assures the development team at Ancestry.com that they have an app that can handle the large traffic demands of the popular genealogy site.
Cliff Click explains 0xdata's H20, a clustering and in-memory math and statistics solution (available for Hadoop and standalone), writing H20's memory representation and compression in Java, low latency Java vs GCs, and much more.
Xavier Amatriain discusses how Netflix uses specialized roles, including that of the Data Scientist and Machine Learning Engineer, to deliver valuable data at the right time to Netflix' customer base through a mixture of offline, online, and nearline data processes. Xavier also discusses what it takes to become a Machine Learning Engineer and how to gain real experience in the field.
Eva Andreasson explains the various Hadoop technologies and how they interact, real-time queries with Impala, the Hadoop ecosystem including Hue, Oozie, YARN, and much more.
Etsy's approach to big data has been to give the entire organization visibility to different sources of data generated by their product as well as access to the experts who know how to use it. Nell Thomas explains her role at Etsy and how Etsy's view of big data has shaped its product's evolution.
Big Data means more than just the size of a dataset. Pavlo Baron explains different ways of applying Big Data concepts in various situations: from analytics, to delivering content, to medical applications. His larger vision for Big Data ranges from specialized Data Scientists, to learning Decision Support Systems, to helping mankind itself.
Erik Meijer explains the various aspects needed to categorise data stores, how reactive programming fits in with databases, the return to data transformation, denotational semantics, and much more.