Michael Ploed talks about the distributed data management challenges that arise in a microservices architecture and how they can be solved using event sourcing in an event-driven architecture.
Petar Tahchiev demos a typical e-commerce project with the Nemesis platform, listing the problems faced and the Spring projects used: Data, Session, Cloud, Boot, MVC, Security, etc.
Roy Clarkson and Greg Turnquist discuss using Spring Data REST to build a back-end for a startup, exemplifying with Spring-A-Gram, an app built with Spring Data REST and secured by Spring Security.
Mark Pollack discusses Spring XD and its integration driven by the Big Data ecosystem at large such as Kafka, Spark, functional programming, integration with Python, and designer/monitoring UIs.
Fátima Casaú discusses applications with Spring, support for ‘Groovy’ and also the use of ‘GORM (Grails Object Relational Mapping)’ as well as ‘Hibernate’ for persistence.
S Aerni, S Ramanujam and J Vawdrey present approaches and open source tools for wrangling and modeling massive datasets, scaling Java applications for NLP on MPP through PL/Java and much more.
Todd Montgomery challenges some of the common myths and misconceptions about high performance streaming data, and takes a look at what is really possible today.
Viktor Klang explores fast data streaming using Akka Streams - how to design robust transformation pipelines with built-in flow control able to take advantage of multicore and go over networks.
Ben Christensen discusses the mental shift from imperative to declarative programming, working with blocking IO such as JDBC and RPC, service composition, debugging and unit testing.
The authors introduce Cybertron, a new tool for reducing I/O operations in data-parallel programs through a constraint-based encoding.
Dave McCrory talks about what is Data Gravity, how it affects performance and portability and why these effects are amplified when there are larger volumes of data.
Julien Le Dem discusses the advantages of a columnar data layout, specifically the features and design choices Apache Parquet uses to achieve goals of interoperability, space and query efficiency.