Raffi Krikorian explains the architecture used by Twitter to deal with thousands of events per sec - tweets, social graph mutations, and direct messages-.
Mike Solomon shares some of the experiences and lessons learned scaling YouTube over the years.
Yashwanth Nelapati and Marty Weiner share lessons learned growing Pinterest: sharding MySQL, caching, server management, all on Amazon EC2.
Hairong Kuang explains how Facebook uses HDFS to store and analyze over 100PB of user log data.
Neha Narula provides advice on choosing a data store for a web applications and executing distributed queries.
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.
Andrew Gerrand introduces Go, demoing some of its main features through examples: a concurrent echo server, chat, channels, error handling, etc.
Jonas Bonér explains solving scalability issues, including adaptive automatic load-balancing, cluster rebalancing, replication and partitioning, with Akka 2.
Erik Happi Stenman discusses 4 scalability basic requirements: the right business model, the right technology, the right people, and the right (amount of) process.
Blake Matheny discusses the current status of Tumblr, its evolution and lessons learned along the way, 3 types of concurrency -Macro, Mecro and Micro-, and Motherboy –a dashboard system-.
Justin Sheehy discusses designing reliable distributed systems that can scale in order to deal with concurrency problems and the tradeoffs required by such systems.