Joe Stein introduces Mesos and managing data services on it, presenting use cases for replacing classic solutions (like cold storage) with new functionality based on these technology.
Matt Ranney explains the Uber architecture overall, with a focus on the dispatch systems, the geospatial index, handling failure, and dealing with the distributed traveling salesman problem.
Reid Draper shows how real world distributed database work, communicate and are tested, trading RPC for messaging, unit-tests for QuickCheck, and micro-benchmarks for multi-week stress tests.
John Leach explains using HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source, showing how Hadoop/HBase can replace traditional RDBMS solutions.
Alvaro Videla shows how to build a system that can ingest data produced at separate locations and replicate it across regions using RabbitMQ.
Jeff Johnson introduces Apollo, a hierarchical NoSQL data system meant to deal with Facebook's distributed storage needs.
Alvaro Videla presents the more advanced features of RabbitMQ: federated brokers, HA queues and support for many protocols and languages.
Cliff Click introduces a coding style & API for in-memory analytics that handles datasets from 1K to 1TB without changing a line of code and clusters with TB of RAM and hundreds of CPUs.
Sebastian Kanthak details how Spanner relies on GPS and atomic clocks to provide two of its innovative features: Lock-free strong reads and global snapshots consistent with external events.
Ian Plosker shares a number of techniques for establishing the data query patterns from the outset of application development, designing a data model to fit those patterns.
Adrian Cockcroft presents Netflix globally distributed architecture, the benchmarks used, scalability issues, and the open source components their implementation is based upon.
Hairong Kuang explains how Facebook uses HDFS to store and analyze over 100PB of user log data.