Jeremy Edberg shares some of the lessons learned scaling Reddit, advising on pitfalls to avoid.
Marc Pacheco tells how Songkick made radical changes to increase the performance of the site while retaining a productive development team.
Jay Kreps discusses the evolution of LinkedIn's architecture and lessons learned scaling from a monolithic application to a distributed set of services, from one database to distributed data stores.
Nicolas Spiegelberg discusses Facebook Messages built on top of HBase, the systems involved and the scaling challenges for handling 500TB of new data per month.
Alex Papadimoulis discusses various deployment strategies, scalable delivery, with examples from real-world organizations such as AllRecipes.com, Twitter, and Google.
Adrian Cockcroft presents Netflix globally distributed architecture, the benchmarks used, scalability issues, and the open source components their implementation is based upon.
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.
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.