Recorded at SpringOne2GX 2014.

Speaker: Paul King

More Groovy Track

Slides: http://www.slideshare.net/SpringCentral/groovy-asttransforms-paulkingsep2014b

In this talk you'll learn about some of Groovy's AST transformations.

Recorded at SpringOne2GX 2014.

Speaker: Kenneth Kousen

Core Groovy Track

Slides: http://www.slideshare.net/SpringCentral/advanced-groovy-tips-and-tricks

Groovy has a very easy learning curve for Java developers, so many people become Groovy users without realizing all it can do. This presentation will examine features of Groovy that can make your life easier once you're past the initial adoption stage. Prerequisite: Some Groovy knowledge

Recorded at SpringOne2GX 2014.

Speaker: Dan Woods

Core Groovy Track

Slides: http://www.slideshare.net/SpringCentral/4pieter-springone2gx2014distributedplatformdev

Building an Enterprise Application Stack means the necessity to have a service tier that can scale to the demands of business. This talk will discuss the approach to developing a scalable enterprise architecture, and will demonstrate implementations based on the variety of technologies available from the Groovy ecosystem, including Grails, Spring, Spring Boot, and Spring Integration.

Recorded at SpringOne2GX 2014.

Speaker: Colin Harrington

Core Groovy Track

Slides: http://www.slideshare.net/SpringCentral/groovybrowserautomation-2gx2014

GEB (pronounced 'jeb') is a browser automation solution. It brings together the power of WebDriver, the elegance of jQuery content selection, the robustness of Page Object modelling and the expressiveness of the Groovy language.

Recorded at SpringOne2GX 2014.

Speaker: Peter Ledbrook

Core Groovy Track

Slides: http://www.slideshare.net/SpringCentral/groovy-forjavadeveloperss2gx2014

Java is a good all-purpose programming language, but does that mean it's the best tool for all jobs?

Recorded at SpringOne2GX 2014.

Speakers: Derek Beauregard, Phil Berman, Michael Minella, Darrel Sharpe

Big Data Track Slides: http://www.slideshare.net/SpringCentral/iot-realized-the-connected-car

For this session we will explore the power of Spring XD in the context of the Internet of Things (IoT). We will look at a solution developed with Spring XD to stream real time analytics from a moving car using open standards. Ingestion of the real time data (location, speed, engine diagnostics, etc), analyzing it to provide highly accurate MPG and vehicle range prediction, as well as providing real time dashboards will all be covered. Coming out of this session, you’ll understand how Spring XD can serve as “Legos®” for the IoT.

Learn more about Spring XD: http://projects.spring.io/spring-xd

Learn more about Spring Integration: http://projects.spring.io/spring-integration

Learn more about Spring Batch: http://projects.spring.io/spring-batch

Recorded at SpringOne2GX 2014. Big Data in Memory

Speaker: John Davies, C24

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014-john-davies-41130273

OK so everyone’s into big data but they’re usually talking about persistence, disk or more recently SSD, how about memory? We could simply add a few terabytes of RAM but even at $100 per GB that’s going to cost a LOT. What if we could reduce the size of the data by 50 fold and effectively bring the cost RAM down towards cost of disk? Keep Spring Integration, Spring Batch, GemFire in-memory cache, RabbitMQ messaging but reduce your data down to binary, yes bits and bytes rather than objects. Less garbage, less network overhead, same APIs but big-data in memory. John will show a Spring work-flow consuming 7.4kB XML messages, binding them to 25kB Java but storing them in just 450 bytes each, 10 million derivative contracts in-memory on a laptop.

Learn more about Spring XD: http://projects.spring.io/spring-xd

Learn more about Spring Integration: http://projects.spring.io/spring-integration

Learn more about Spring Batch: http://projects.spring.io/spring-batch

Recorded at SpringOne2GX 2014.

Speaker: Stuart Williams

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/williams-1m-events

An application designer usually has to choose where to trade flexibility for specificity (and thus usually performance); knowing when and where to do so is an art and requires experience. This talk will share over a decades worth of experience making these decisions and the learnings from developing Pivotal's successful Real Time Intelligence (RTI) product using the latest versions of Spring projects: Integration, Data, Boot, MVC/REST and XD. A walk through the RTI architecture will provide the base for an explanation about how Spring performs at hundreds (and millions) of events/operations per second and the techniques that you can use right now in your own Spring applications to minimise resource utilisation and gain performance.

Recorded at SpringOne2GX 2014.

Speaker: Michael Minella

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/building-a-recommendation-engine-with-spring-and-hadoop

The Amazon’s and Google’s of the world have had Ph.D.’s locked up in back rooms for years creating algorithms to get you to click on things and subsequently buy stuff. One of the big things that those smart people have been working on are recommendation engines. Today, a recommendation engine isn’t something that only the Amazon’s of the world can have. With an hour, and a handful of open source tools, we’ll build a recommendation engine based on the data from the website we probably spend the most time on…StackOverflow. We’ll use Spring XD and Spring Batch to orchestrate the full lifecycle of Hadoop processing (ingest, process, export) and use Apache Mahout to provide us with the recommendation processing. A basic understanding of Hadoop concepts (what Map/Reduce is) and Spring (basic D/I configuration) is expected for this talk.

Learn more about Spring Batch: http://projects.spring.io/spring-batch