On behalf of the Spring XD team, I am very pleased to announce the
first maintenance release of Spring XD 1.1.

Download Link: 1.1.1.RELEASE: zip and brew.

The 1.1.1 release includes bug fixes and some enhancements. The full list of tickets is available in our JIRA.

Here are some highlights.

• Improved performance and offset management when using Kafka message bus.
• Support for Spark Streaming's reliable receiver.
• Kryo serialization improvements.
• Option to clean up Rabbit queue/topics when deleting the stream.
• Add a Sqoop example

Feedback is very important, so please get in touch with…

Dear Spring community,

It’s my pleasure to announce that Spring Framework 4.1.6 is available now. This is almost exclusively a bugfix release and therefore a strongly recommended upgrade for all current 4.x users. In particular, we recommend an upgrade from the superseded 4.0.x line as well.

Release Notes | Project Page | GitHub | Issues | Documentation

Spring Framework 4.1.6 is also the first release to be formally compatible with the recently released JDK 8 update 40. Spring 4.1.x supports a wide range of Java runtimes now, from 2010-era JDK 6 variants up until the latest 2015-era JDK…

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.

Welcome to another installment of This Week in Spring! This week, I'm in São Paulo, Brazil, where I presented a workshop on building cloud-native applications - specifically microservices - with Spring Boot, Spring Cloud and Cloud Foundry (via Pivotal Web Services).

• Spring Data lead Oliver Gierke has just announced Spring Data Fowler has gone GA, packed with lots of advanced Java 8 features, as well!
• The agenda for the Spring IO show in Barcelona, Spain is now available! Check it out!
• Done with the microservices hype? Then let's get practical on April 21st. I'll be talking about how Spring Cloud integrates service registration (e.g.: Eureka, Consul, or Zookeeper), declarative REST clients (with Netflix's Feign), reactive programming and the circuit breaker pattern with Hystrix to support easy, robust service-to-service invocations.
• Marius Bogoevici and Mark Pollack are teaming up for a webinar on Tues April 28th about Reactive data-pipelines with Spring XD and Kafka…

Note: the source code and test for this blog continue to evolve, but the changes to the text are not being maintained here. Please see the tutorial version for the most up to date content.

In this article we continue our discussion of how to use Spring Security with Angular JS in a "single page application". Here we show how to use Spring Session together with Spring Cloud to combine the features of the systems we built in parts II and IV, and actually end up building 3 single page applications with quite different responsibilities. The aim is to build a Gateway (like in part IV) that is used not only for API resources but also to load the UI from a backend server. We simplify the token-wrangling bits of part II by using the Gateway to pass through the authentication to the backends. Then we extend the system to show how we can make local, granular access decisions in the backends, while still controlling identity and authentication at the Gateway. This is a very powerful model for building…

On behalf of the Spring Data team I'd like to announce the availability of the GA version of Spring Data release train Fowler.The release ships almost 400 tickets solved.

The most core theme of the train is advanced support for Java 8: the MongoDB and JPA modules now support Streams as return type for repository methods and we now support non-time-zoned JDK 8 date/time types our of the box and also integrate with the Java 7 back-port of that API.

Spring Data MongoDB ships with compatibility for the 3.0 version of the server and the corresponding driver version. Spring Data Gemfire now supports…