Brandon Philips describes how bringing containers, schedulers, and distributed systems together will create more reliable and greatly more trusted server infrastructures.
Kriti Sharma talks about how Barclays is solving some of the toughest big data challenges in financial services using scalable, open source technology.
Tim Wagner defines server-less computing, examines the key trends and innovative ideas behind the technology, and looks at design patterns for big data, event processing, and mobile using AWS Lambda.
Eleanor McHugh discusses writing virtual machines using hardware emulation, including code snippets in Go and C.
John Allspaw provides a glimpse into how other fields handle incident response, including active steps companies can take to support engineers in those uncertain and ambiguous scenarios.
Kenji Rikitake discusses using Erlang/OTP for IoT, covering communication protocols, design principles and overcoming hardware limitations for endpoint devices in fault-tolerant systems.
Pushpraj Shukla discusses how Microsoft Bing predicts the future based on aggregate human behavior using one of the largest scale data sets, and recent progress in large scale deep learnt models.
David Riddoch talks about the technologies that make high performance networking possible on commodity servers, with a special focus on direct access to the network adapter by bypassing the kernel.
Peter Bakas presents in detail how Netflix has used Kafka, Samza, Docker, and Linux to implement a multi-tenant pipeline processing 700B events/day in the Amazon AWS cloud.
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.
Sid Anand discusses how Agari is applying big data best practices to the problem of securing its customers from email-born threats, presenting a system that leverages big data in the cloud.
Irad Ben-Gal discusses Big Data analytics misconceptions, presenting a technology predicting consumer behavior patterns that can be translated into wins, revenue gains, and localized assortments.