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SpringOne Platform 2016 Replay: Migrating from Spring XD to Spring Data Cloud Flow

Recorded at SpringOne Platform 2016.
Speakers: Katie Mooney; Director, Marketing Operations, zData; Cahlen Humphreys; Big Data Solutions Architect, zData & Dillon Woods; Chief Technology Officer, zData
Slides: http://www.slideshare.net/SpringCentral/lessons-learned-migrating-from-spring-xd-to-spring-data-cloud-flow-katie-mooney-dillon-woods-cahlen-humphreys

The goal of this session is to teach companies how they can use real-time data to make predictions with Spring Cloud Data Flow, Spark 2.0 and Spark ML. It will cover how to train your model using Spark ML in Spark 2.0. Once trained we will show you how to make real-time predictions using the model and Spring Cloud Data Flow.

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SpringOne Platform 2016 Replay: Reactive Kafka

Recorded at SpringOne Platform 2016.
Speaker: Rajini Sivaram
Slides: http://www.slideshare.net/SpringCentral/reactive-kafka

Apache Kafka is a distributed, scalable, high-throughput messaging bus. Over the last few years, Kafka has emerged as a key building block for data-intensive distributed applications. As a high performance message bus, Kafka enables the development of distributed applications using the microservices architecture.

Reactive Streams simplifies the development of asynchronous systems using non-blocking back pressure. The reactive framework enables the development of asynchronous microservices by providing a side-effect free functional API with minimal overhead that supports low latency, non-blocking end-to-end data flows.

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SpringOne Platform 2016 Replay: Task Madness - Modern on demand processing

Recorded at SpringOne Platform 2016.
Speakers: Michael Minella,Glenn Renfro
Slides: http://www.slideshare.net/SpringCentral/task-madness-modern-on-demand-processing

The flexibility promised by cloud computing is something that hasn’t been fully available in many use cases yet. Most application models are still based on long running containers, robbing users of that very flexibility. Spring Cloud Task, a new project in the Spring portfolio provides both functional and non-functional capabilities for building short lived, cloud-native, microservices. This talk will introduce the project as well as look at example applications. Since we’re in Vegas, we’re going to put that on demand processing to good use by demonstrating how we used it to generate a March Madness NCAA Men’s Basketball Tournament bracket. We’ll review how it worked, why Spring Cloud Task was the best fit for this type of application, and how we did this past March!

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SpringOne Platform 2016 Replay: Secure & Dynamic App Config at GapTech with Spring Cloud, Vault and Consul

Recorded at SpringOne Platform 2016.
Speaker: Nivesh Gopathi, Gap Tech
Slides: http://www.slideshare.net/SpringCentral/secure-dynamic-app-config-at-gaptech

As organizations move to audit trails over managed processes, continuous delivery with aggressive MTTR, security first doctrines and ephemeral instances, a key enabler is a platform ability for dynamic application configuration and securely bootstrapping application secrets. In this session, we will go over what the use cases for dynamic configuration and application secrets management are with some high level requirements, how we are collaboratively solving for these at enterprise scale using Spring Cloud Config, Vault and Consul and what’s coming next.

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Spring Boot 1.4.4 Available Now

I am pleased to announce that Spring Boot 1.4.4 has been released and is available now from repo.spring.io and Maven Central.

Spring Boot 1.4.4 includes over 60 fixes, improvements and 3rd party dependency updates! Thanks to all that have contributed!

If you’re interested in helping out, check out the “ideal for contribution” tag in the issue repository. If you have general questions, please ask at stackoverflow.com using the spring-boot tag.

Project Page | GitHub | Issues | Documentation

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Spring Data Release Train Ingalls Goes GA

On behalf of the Spring Data team I’d like to announce the general availability of our Ingalls release train. After nine months of development, this release not only contains a huge set of new features, improvements and bug fixes, it also marks the conclusion of a branch of Spring Data that has helped developers dealing with persistence for more than 5 years — even 8, if you count in some module’s predecessors.

The release ships more than 630 tickets fixed in total. Here’s a very truncated list of the most important features shipping with the release:

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Spring Data Release Train Hopper SR7 Released

In preparation of the upcoming Spring Boot 1.4.4 release, I am pleased to announce the availability of the seventh service release of Spring Data release train Hopper. The release ships 39 tickets fixed and is a recommended upgrade for all users.

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Spring Team at Devoxx US 2017

Join the Spring Team at the 1st ever Devoxx event in the United States on March 21-23, 2017!
Pivotal is sponsoring and will be at booth # 104 from 10am-8pm on Tuesday March 21st, and 9:45am - 4:30pm on Wednesday March 22rd.

We will have experts available to talk to you about Spring Boot, Spring Cloud, Spring Cloud Data Flow, Cloud Foundry, and much more. Stop by and learn about Cloud Native Java from the leaders at Pivotal.

Don’t miss talks from Spring leaders on Cloud-Native, Reactive Spring, Eclipse and Big Data!

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Spring Integration 4.3.7 is Available

On behalf of the Spring Integration team, I am pleased to announce that the 4.3.7.RELEASE of Spring Integration is now available.

This release contains a few important bug fixes as well as a couple of improvements.

In particular, this version uses the recently announced Spring Framework 4.3.6.

Please be aware that some performance issues were detected when using Spring Integration with Spring Framework versions 4.3.4 and 4.3.5. We recommend that all users upgrade to this version, but this could be particularly important for users of Spring Integration versions 4.3.5 and 4.3.6, which used those versions of Spring Framework respectively. See this issue for details to see if you are affected

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