Jeff Beck describes how Grails fits into a larger polyglot architecture and goes through his team's experiences building and maintaining these micro services.
Daniel Tunkelang focuses on the data science mindset for successfully applying machine learning to solve problems: express, explain, experiment.
Neha Narkhede of Kafka fame shares the experience of building LinkedIn's powerful and efficient data pipeline infrastructure around Apache Kafka and Samza to process billions of events every day.
The authors discuss Netflix's new stream processing system that supports a reactive programming model, allows auto scaling, and is capable of processing millions of messages per second.
Terence Yim from Continuuity showcases a transactional stream processing system that supports full ACID properties without compromising scalability and high throughput.
Janne Valkealahti shows how Spring provides a simple programming model to develop applications that can easily be tested and deployed as either a YARN application or a traditional application.
The speakers explore the role of ZooKeeper, Spring Integration, and Spring Boot through beautiful panoramas, code samples, and demonstrations.
Carlos Queiroz introduces the lambda architecture and showcases how it can be implemented with SpringXD, GemFireXD and Hadoop in a CDR(Call Detail Record) mining application.
The authors explain how the Pivotal team leveraged familiar SQL-based queries to analyze fine-grained cluster utilization using Spring XD.
Mohammad Quraishi presents implementing a Big Data initiative, detailing preparation, goal evaluation, convincing executives, and post implementation evaluation.
The authors present basic concepts about Spring Boot and Netflix OSS software and how to integrate Netflix OSS technologies into Spring Boot.
The authors introduce the Reactive Streams project, demonstrating how to build applications that can connect to other Reactive Streams implementations in a completely non-blocking way.