Andrew Psaltis talks about Apache Beam, which aims to provide a unified stream processing model for defining and executing complex data processing, data ingestion and integration workflows.
Saul Caganoff discusses the different use cases for API consumption and the technical affordances API designers can provide to support those use cases.
Chien Huey evaluates Marathon running on DC/OS as a replacement for Elastic Beanstalk and/or ECS in terms of functionality, ease of use as well as cost.
Kief Morris discusses building and maintaining a testing and hosting infrastructure for microservices, explaining the creation of a cloud-based infrastructure with Packer, Terraform, and Ansible.
Peter Bourgon and Matthias Radestock explain the theory behind Weave Mesh, some of the important key features, and demonstrate some exciting use cases, like distributed caching and state replication.
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.