Ian Fyfe discusses the different options for implementing speed-of-thought business analytics and machine learning tools directly on top of Hadoop.
Mathieu Bastian explores the mechanics of unit, integration, data and performance testing for large, complex data workflows, along with the tools for Hadoop, Pig and Spark.
The authors discuss how Spring for Apache Hadoop can make developing workflows with Map Reduce, Spark, Hive and Pig jobs easier, and using Spring Cloud to build distributed apps for YARN.
Christian Tzolov shows different integration approaches between HAWQ and GemFire, showing using Spring XD to ingest GemFire data into HDFS and using Spring Boot to implement a RESTful proxy for HAWQ.
Vaclav Petricek discusses how to train models, architect and build a scalable system powered by Storm, Hadoop, Spark, Spring Boot and Vowpal Wabbit that meets SLAs measured in tens of milliseconds.
Piotr Kołaczkowski discusses how they integrated Spark with Cassandra, how it was done, how it works in practice and why it is better than using a Hadoop intermediate layer.
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.
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.