This talk provides a broad overview of the new features introduced in the latest Spring Data release trains: recent additions in Spring Data Commons and the latest features of individual store modules
In this solutions track talk, sponsored by MongoDB, Matt Asay discusses the differences between some of the NoSQL and SQL databases and when Hadoop makes sense to be used with a NoSQL solution.
Trisha Gee demoes building a web application using Java, HTML5, Angular.js, Mongo.DB, Groovy and microservices in one hour.
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.
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.
Kenny Gorman provides advice on designing systems using MongoDB in order to avoid some of the pitfalls lurking along the way.
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.
Ross Lawley introduces MongoDB, explaining why it is a good solution for cloud deployment.
Richard Kreuter and Kyle Banker on how to avoid classical RDBMS transactional systems by using compensation mechanisms, transactional messaging or transactional procedures.