Alois Reitbauer discusses challenges and solutions on the organizational, development and operational side, deploying faster, decoupling a monolith without breaking the logic and dynamically scaling.
Jon Harding discusses design patterns and best practices to create scalable applications, build processes to prepare for production and tips to prepare for transitioning to Angular 2.
Todd Montgomery investigates whether WebSockets, HTTP/2, Reactive Streams and microservices can deliver the high scalability, resiliency, and ease of development promised.
Alexander Grosse discusses the principles behind building successful organizations that are growing with examples from SoundCloud and Twitter.
Jay Marshall and Vic Iglesias talk about how GCP was built for the enterprise, enabling users to deploy their applications on the same infrastructure Google uses for search, YouTube or GMail.
Anthony McCulley describes The Home Depot’s first year with Cloud Foundry, adopting the platform, scaling to hundreds of developers across multiple data centers, and mistakes made along the way.
David Greenberg discusses how Two Sigma was able to scale up their research to harness tens of thousands of CPUs and the challenges faced.
Matt Ranney talks about Uber’s growth and how they’ve embraced microservices. This has led to an explosion of new services, crossing over 1,000 production services in early March 2016.
Tony Grout and Chris Matts share stories from their several year multi-company journey towards scaled agile, showing how to look at Agile from an organizational perspective and not through tools.
Peter Bourgon presents some of the idioms, design patterns, and practices that have proven themselves developing successful, scalable, and sustainable code using Go.
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines - horizontal scaling of worker pools as a function of pipeline input rate.