Greg Murphy describes how GameSparks has designed their platform to be tolerant of many things: unreliable and slow internet connectivity, cloud resources that can fail without warning, and more.
Greg Hawkins discusses how Starling Bank, part of the new movement in FinTech challenger banks, is innovating while addressing the need for resilience in a world where failure is everywhere.
A.Tomb describes the capabilities of some open source tools that allow us to automatically determine whether a low-level cryptographic implementation matches a higher-level mathematical specification
Matt Heath discusses why Go is suited for microservices, what makes it attractive to high volume, low latency, distributed apps, and how easy it is to adopt into existing systems and organisations.
Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems.
Joe Duffy talks about the concurrency's explosion onto the mainstream over the past 15 years and attempts to predict what lies ahead for distributed programming, from now til 15 years into the future.
Sophia Voychehovski discusses all the factors that cause complexity, the three key ways one can wrangle it and object-oriented UX.
Michael Barker discusses several low-latency APIs used for financial trading, what makes them fast and how they compare to HTTP/REST/JSON/XML APIs.
David Talby walks through building a natural language annotations pipeline with domain-specific annotators, and using deep learning to automatically expand and update taxonomies.
Angie Terrell discusses the current state of conversational interfaces and human-centered design principles to guide the design of conversational apps.
Chinmay Soman and Yi Pan discuss how Uber and LinkedIn use Apache Samza, Calcite and Pinot along with the analytics platform AthenaX to transform data to make it available for querying in minutes.
Elliot Chow discusses the data pipeline that they built with Kafka, Spark Streaming, and Cassandra to process Netflix user activities in real time for the Trending Now row.