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.
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.
Serkan Piantino discusses news feeds at Facebook: the basics, infrastructure used, how feed data is stored, and Centrifuge – a storage solution.
Michael Stonebraker compares how RDBMS, NoSQL and NewSQL support today’s big data transaction processing needs. He also introduces VoltDB, an in-memory NewSQL database.