The speakers show how to provide a scalable runtime environment, that is easily configured and assembled via a simple DSL.
Gabriel Gonzalez introduces TSAR (TimeSeries AggregatoR), a service for real-time event aggregation designed to deal with tens of billions of events per day at Twitter.
Steve Hoffman, Ken Dallmeyer share their experience integrating Hadoop into the existing environment at Orbitz, creating a reusable data pipeline, ingesting, transporting, consuming and storing data.
Claudia Perlich discusses privacy-preserving representations, robust high-dimensional modeling, large-scale automated learning systems, transfer learning, and fraud detection.
Mitchell Hashimoto introduces Vagrant, Packer, Consul, Serf, explaining how they can help DevOps streamline the entire process from development through to production.
Paco Nathan keynotes on how Spark fits into the big data landscape, describing what other systems work with Spark, and explaining why Spark is needed in the future.
Jim Scott keynotes on the history of Hadoop, the difficulties that this technology has gone through, exploring the reasons why enterprises need to evaluate their targets and prepare for the future.
Jimmy Bogard takes a look at how Octopus enables continuous delivery and what it offers over standard tooling.
Barry Jaspan describes how they test Acquia Cloud, a large PaaS and DevOps project, and what they have learned over several years of developing those tests.
John Zamierowski discusses the business benefits of big data coming from the Internet of Everything, focusing on the "Why" and "How" of big data and current developments in sensor technology.
Gareth Rushgrove explores patterns and practices useful to implement continuous integration in an infrastructure-as-code environment.
Stefan Edlich discusses big data systems -Spanner, Presto- and the future of data persistence, data analytics, data formats and of NoSQL/NewSQL in general.