Ben Christensen describes Netflix API's evolution to a web service platform serving all devices and users, the challenges met in operations, deployment, performance, fault-tolerance, and innovation.
Mike Krieger discusses Instagram's best and worst infrastructure decisions, building and deploying scalable and extensible services.
Nick Kolegraff discusses common problems and architecture to support all the phases of data science and how to start a data science initiative, sharing lessons from Accenture, Best Buy, and Rackspace.
Tamar Bercovici presents Box’s transition from a single MySQL database to a fully sharded MySQL architecture, all the while serving 2 billion queries per day.
Zoltan Toth-Czifra shares scalability lessons learned at Softonic, a company that has developed and grew along with the Internet for over 15 years.
Joshua Suereth designs a scalable distributed search service with Akka and Scala using actors, and covering practical aspects of how to scale out with Akka’s clustering API.
Michael Hausenblas introduces Apache Drill, a distributed system for interactive analysis of large-scale datasets, including its architecture and typical use cases.
Peter Boros discusses a MySQL architecture useful for the majority of projects, backup, online schema changes, reliability and scalability issues, and basics of sharding.
Marton Anka shares lessons learned and technical details scaling LogMeIn over a decade.
Sid Anand uses examples from LinkedIn, Netflix, and eBay to discuss some common causes of outages and scaling issues. He also discusses modern practices in availability and scaling in web sites today.
Jesper Richter-Reichhelm shares lessons learned from failures while scaling Wooga games to millions of daily users.
Robin Johnson discusses using a data management model for games that can be scaled, and the bottlenecks and challenges met by OMGPOP scaling to millions of users.