Simon Wardley talks about Amazon and it's competitive landscape, including Google, OpenStack, telcos and the hardware manufactures. Looking at how Amazon got to be so dominant in the IaaS space, the missteps by established vendors in letting it, and where future competition might come from. With a short detour to discuss Cloud Foundry and platform strategy.
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.
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.
Ben Christensen explains how Netflix manages to stay online even with millions of users, the Hystrix fault tolerance library, how Netflix discovered reactive programming and why it ported Rx to Java.
Enterprise cloud specialist Brian McCallion talks about what's really holding back enterprises from adopting the cloud, how they should address their legacy applications, ways to avoid introducing complexity in distributed environments, the value of Amazon Redshift, and how technologists should broaden their knowledge and avoid specialization.
Cloud leader George Reese answers questions across a wide range of topics. He shares his thoughts on pitfalls of enterprise cloud strategies, overrated technologies, whether IaaS standards matter yet, the relevance of private clouds, and the need for common sense when designing a API.
IT thought leader Jeff Sussna answers a range of questions about operational efficiency and cloud trends. He discusses new thinking around production freezes and adopting continuous delivery. Sussna explains how companies should understand the entire lifecyle of a customer’s cloud experience. Finally, he shares insight into AWS and their leading position in the cloud.
Hive co-creator Ashish Thusoo describes the Big Data challenges Facebook faced and presents solutions in 2 areas: Reduction in the data footprint and CPU utilization. Generating 300 to 400 terabytes per day, they store RC files as blocks, but store as columns within a block to get better compression. He also talks about the current Big Data ecosystem and trends for companies going forward.
Gil Tene talks to Charles Humble about different garbage collection techniques, and specific collectors including Azul's C4, IBM's Balanced GC, and Oracle's Garbage First, before moving on to discuss both the JCP and OpenJDK.
Two of ThoughtWorks’ finest, Martin Fowler and Jez Humble, talk about the notion of Continuous Delivery, which enables organizations to build software that is production ready at all times. To do this, enterprises automate the build, deployment, and testing process, and improve collaboration between developers, testers, and operations. The duo discusses a variety of related issues.
Cliff Click discusses the Pauseless GC algorithm and how Azul's Zing implements it on plain x86 CPUs. Also: what keeps dynamic languages slow on the JVM, invokedynamic, concurrency and much more.
Hilary Mason, interviewed by Ryan Slobojan, discuss the engineering behind bit.ly and their use of machine learning in their system architecture. Hilary also talks about their use of MySQL and MongoDB to manage terabytes of information about users and clicks and their implications on performing real-time analysis of anthropology on the human condition.