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.
Aryeh Selekman discusses current trends in the mobile space, some of the technologies useful to integrate Facebook functionality into mobile applications and the latest W3C mobile standards under dev.
Nathan Marz discusses Storm concepts –streams, spouts, bolts, topologies-, explaining how to use Storms’ Clojure DSL for real-time stream processing, distributed RPS and continuous computations.
Ashish Thusoo presents the data scalability issues at Facebook and the data architecture evolution from EDW to Hadoop to Puma.
Arya Asemanfar presents Twitter’s timeline architecture, the entire sequence of steps a tweet goes through until it reaches the timeline of each user following the person who tweeted.
Attila Szegedi shares lessons learned tuning the JVM at Twitter, spending most of his talk discussing memory tuning, CPU usage tuning, and lock contention tuning.