Eugene Mandel discusses challenges of conforming data sources and compares processing stacks: Hadoop+Redshift vs Spark, showing how the technology drives the way the problem is modeled.
Michael Minella uses Spring XD and Spring Batch to orchestrate the full lifecycle of Hadoop processing and uses Apache Mahout to provide the audience with the recommendation processing.
Matt Stine presents how combine Spring Boot, Spring Data, Spring Reactor, Spring XD, Hadoop and run them in the cloud.
Armon Dadgar presents Consul, a distributed control plane for the datacenter. Armon demonstrates how Consul can be used to build, configure, monitor, and orchestrate distributed systems.
Chris Beams shares his findings from over two years of research into bitcoin and related technologies.
Steve Pember discusses the tenants of the Reactive Pattern and the importance of moving away from Monolithic to Reactive architectures.
Eugene Dvorkin provides an introduction to Storm framework, explains how to build real-time applications on top of Storm with Groovy, how to process data from Twitter in real-time, etc.
Thomas Risberg introduces the Spring for Apache Hadoop project and discusses integration with Spring XD, batch jobs and external data sources.
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.
Roman Shaposhnik discusses more advanced features of HDFS, in addition to how YARN has enabled businesses to massively scale their systems beyond what was previously possible.
Yann Yu discusses how Solr and Hadoop complement each other, and how to use Solr as a real-time, analytical, full-text search front-end to data stored in Hadoop.
Jeremy Stieglitz discusses best practices for a data-centric security , compliance and data governance approach, with a particular focus on two customer use cases.