Details on Pinterest's architeture, its systems -Pinball, Frontdoor-, and stack - MongoDB, Cassandra, Memcache, Redis, Flume, Kafka, EMR, Qubole, Redshift, Python, Java, Go, Nutcracker, Puppet, etc.
Garrett Eardley explores how Riot Games is leveraging Riak for their stats system, discussing why they chose Riak, the data model and indexes, and strategies for working with eventually consistent data.
Paul King presents working with databases in Groovy, covering datasets, GMongo, Neo4J, raw JDBC, Groovy-SQL, CRUD, Hibernate, caching, Spring Data technologies, etc.
Tom Coupland discusses some of the various technologies investigated, and in many cases deployed at Nokia including Gradle, Spring, MongoDB and Clojure.
Tony Tam shares tips for modeling data with MongoDB for a fast and scalable system based on his experience migrating billions of records from MySQL to MongoDB.
Andy Gross reflects on five years of involvement with Riak and distributed databases and discusses what went right, what went wrong, and what the next five years may hold for Riak.
Shanley Kane discusses Dynamo - consistent hashing, vector clocks, hinted handoff, gossip protocol - advances in each area, and how querying and application development has changed as a result of them.
Kenny Gorman provides advice on designing systems using MongoDB in order to avoid some of the pitfalls lurking along the way.
Jesse Newland discusses how GitHub pages were re-written with Erlang, Riak and Webmachine in order to improve their performance.
Justin Sheehy discusses designing reliable distributed systems that can scale in order to deal with concurrency problems and the tradeoffs required by such systems.
Andy Gross reports on how Basho used Riak and Erlang to build their cloud storage service.
Peter Bell introduces 4 NoSQL categories –Key-Value, Document, Column, Graph - and explains how one can use Spring Data to work with such data stores.