Speaker: Scott Deeg

Slides: http://www.slideshare.net/sdeeg/spring-polymer-webinar-46514389

Polymer is the latest web framework out of Google. Designed completely around the emerging Web Components standards, it has the lofty goal of making it easy to build apps based on these low level primitives. Along with Polymer comes a new set of Elements (buttons, dialog boxes and such) based on the ideas of "Material Design". These technologies together make it easy to build responsive, componentized "Single Page" web applications that work for browsers on PCs or mobile devices. But what about the backend, and how do we make these apps secure? In this talk Scott Deeg will take you through an introduction to Polmyer and its related technologies, and then through the build out of a full blown cloud based app with a secure, RESTful backend based on Spring REST, Spring Cloud, and Spring Security and using Thymeleaf for backend rendering jobs. At the end he will show the principles applied in a tool he's currently building. The talk will be mainly code walk through and demo, and assumes familiarity with Java/Spring and JavaScript.

Learn about about Spring Security at http://projects.spring.io/spring-security

Learn about about Spring Cloud at http://projects.spring.io/spring-cloud

Speaker: Julien Dubois

Slides:http://www.slideshare.net/julien.dubois/jhipster-for-spring-boot-webinar

JHipster focuses on generating a high quality application with a Java back-end using an extensive set of Spring technologies; Spring Boot, Spring Security, Spring Data, Spring MVC (providing a framework for websockets, REST and MVC), etc. an Angular.js front-end and a suite of pre-configured development tools like Yeoman, Maven, Gradle, Grunt, Gulp.js and Bower. JHipster creates a fully configured Spring Boot application with a set of pre-defined screens for user management, monitoring, and logging. The generated Spring Boot application is specifically tailored to make working with Angular.js a smoother experience. Join Julien for a quick-live coding session to build a simple application, and deploy it to Cloud Foundry.

Learn about about Spring Boot at http://projects.spring.io/spring-boot

Original author: DISID Corporation

Dear Spring community,

The DISID Spring Roo team is busily working towards the 2.0 release. With Spring Roo 2.0 we would like recover the essence that defines the Roo project:

• Be a rapid application development tool, Roo isn't either a framework nor Maven plugins.
• Intelligent code generation to provide improved productivity.
  • Developers often are forced to spend too much of their time doing things that add too little value, Spring Roo's code generation helps them stay focused.
• Create applications based on the extensive set of Spring technologies: Spring Boot, Spring Data, Spring MVC, Spring Security, etc.
• Generate applications based on best architecture practices like: Service Layer, Repository Layer
• New scaffolding model that generates a fully functional responsive UI based on jQuery and Bootstrap.
• Improve extensibility and increase the collaboration of the Spring Roo project:
  • We will create the “Roo Marketplace”: the alternative to Roobot, easier to maintain and available for everyone, a place to find and keep track on third party addons and Roo Addon Suites.
  • A “Roo Addon Suite” is a great way to package and distribute a set of add-ons together, for example if you want to distribute Roo custom distributions. Roo Addon Suite is based on OSGi R5 Subsystems that provides a really convenient deployment model, without compromising the modularity of Roo.

The new Roo 2.0 is a beginning, in future versions we would like to design a new way to define the view layer components to get a true independence from view layer technologies letting the developers to try the technology that better adjust to their requirements: JSP, Thymeleaf, etc. without losing any benefit.

We have scheduled the work of the relase 2.0 in 3 main milestones:

Milestone 1: Create a smaller and easier to maintain Spring Roo distribution. From 2.0, Roo will contain the runtime and the main addons only, so new versions could be released more frequently and being kept up to date with latest Spring projects:

• Make a better separation between the Spring Roo runtime and the core addons. The Roo runtime APIs (classpath, metadata, support, etc.) must be kept as stable and backwards compatible as possible.
• Move the addons not classified as ”core addon” (GWT, JSF, ...) to their own projects to be maintained by Roo community. If your organization would like to contribute to Roo project by maintaining these addons please contact to: springroo@ disid.com
• Migrate current user guide from docbook to ASCIIdoc.

Milestone 2: Update code generation to use latest Spring technologies

• Update code generation to use the latest Spring framework versions (4.x)
  • Move from Spring framework dependencies to Spring IO Platform
• User Managed POM (ROO-3465, ROO-3478)
• Spring Profiles support
• Annotation based configuration:
  • Move to class configuration in spite of XML configuration
  • Improve application maintanability by using Spring Boot annotations
• Remove Active Record data model in favor of the Repository (Spring Data) based one.
• Generate application arquitecture based on service layer pattern by default

Milestone 3: Move web layer to HTML5

• Responsive UI – View layer based on jQuery, Bootstrap and CSS3

Important: Roo 2.0 might contain API changes and less add-ons than previous version so this release probably won't be backward compatible with 1.3. You can see a more specific roadmap in the SpringRoo JIRA:

As always, you’ll also find Roo on Twitter - either follow @SpringRoo or just include #SpringRoo in your tweets. Stay tuned to Spring Roo news!

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.

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.

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.

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: Matt Stine

Developing for the Cloud Track

Slides: http://www.slideshare.net/SpringCentral/apps-data-cloud-what-does-it-all-mean

Big Data. Fast Data. NoSQL. NewSQL. We've experienced something of a renaissance in the storage and processing of data in the last decade of computing after years of "Relational Winter." We're now entering into the next phase of this evolution: the convergence of data and the cloud. Much of this revolution has arrived on the coattails of data fabrics designed for horizontal scale-out on commodity hardware. Cloud platforms, especially PaaS platforms like Cloud Foundry, allow us to provision the requisite virtual hardware on-demand, removing the last mile of overhead in assembling scale-out data platforms. Coupling PaaS with microservice architectures and polyglot persistence allows developers to design systems utilizing stores uniquely designed for specific write, process, and query patterns. Leveraging the Lambda Architecture combination of real-time analytics platforms coupled with scale-out batch processing systems like Hadoop give us the ability to always ask questions of all of our data. In this talk will look at various Spring projects that allow us, coupled with Cloud Foundry, to uniquely position ourselves to take advantage of this convergence: Spring Boot: the opinionated framework for microservice development Spring Data: the access layer for SQL, NoSQL, NewSQL, and Hadoop Reactor: the foundation for reactive fast data applications on the JVM Spring XD: the platform for data ingest, real-time analytics, batch processing, and data export We'll tie all of these projects together in a suite of applications running on Cloud Foundry and Hadoop, closing the Apps/Data/Cloud loop.

Learn more about Cloud Foundry: http://www.cloudfoundry.org

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

Learn more about Spring Data: http://projects.spring.io/spring-data

Recorded at SpringOne2GX 2014.

Speaker: Peter Harrington

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014peterharrington

Does your organization collect data? Lots of data? Does your organization make use of all that data they have collected? In this session you will learn what you do with machine learning, and what are the building blocks for an application that uses machine learning. This session will show you how to go from data you have collected to creating predictions for customers. You will learn how valuable insights into your data can be gleaned while building the code to make predictions.

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