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.
Jeff Scott Brown introduces GORM, a super powerful ORM tool that makes ORM simple by leveraging the flexibility and expressiveness of a dynamic language like Groovy.
Simon Marlow explains how to use Haxl to automatically batch and overlap requests for data from multiple data sources.
Matt Stine presents how combine Spring Boot, Spring Data, Spring Reactor, Spring XD, Hadoop and run them in the cloud.