Chuck Rossi unveils some of the tools and processes used by Facebook for pushing new updates every day.
Leo A. Meyerovich explains how social adoption patterns can help language designers make new languages that are inherently attractive and desirable by developers.
David Mortenson details how Facebook maintained efficiency while increasing the number of engineers by reducing the n00b time sink, keeping development fast and avoiding unintended consequences.
Johan Oskarsson explains how Twitter is using Zipkin to trace a pages in order to see their execution path and to determine the time spent for loading for performance monitoring and analysis.
Craig Walls explains how Spring Social can be used to create social applications or connect to existing ones using their APIs.
Dmitriy Ryaboy shares some of the lessons learned scaling Twitter’s analytics infrastructure: Data loves a schema, Make data sources discoverable, and Make costs visible.
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
Dhruba Borthakur discusses the different types of data used by Facebook and how they are stored, including graph data, semi-OLTP data, immutable data for pictures, and Hadoop/Hive for analytics.
James Pearce discusses the current trends in social applications and some of the challenges and solutions in creating HTML5 applications for mobile devices.
Amit Rathore describes the architecture of Zolodeck, a virtual relationship manager built on Clojure, Datomic, and Storm.
Serkan Piantino discusses news feeds at Facebook: the basics, infrastructure used, how feed data is stored, and Centrifuge – a storage solution.
Raffi Krikorian details Twitter’s timeline architecture, its “write path” and “read path”, making it possible to deliver 300k tweets/sec.