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Spring Data Geode 1.0.0.APACHE-GEODE-INCUBATING-M2 Released

I am pleased to announce the release of Spring Data Geode 1.0.0.APACHE-GEODE-INCUBATING-M2, offering support for the recently announced Apache Geode 1.0.0-incubating.M2 release.

Spring Data Geode is a specific version of Spring Data GemFire with support for Apache Geode rather than Pivotal GemFire.

Both Spring Data GemFire and Spring Data Geode are based on the same source repository in GitHub, however, Apache Geode support lives in the apache-geode branch. Therefore, you won’t find a separate Spring Data project specifically for Apache Geode, but this is of little consequence since your existing knowledge of Pivotal GemFire combined with Spring Data GemFire will be immediately useful and transferable to Apache Geode and Spring Data Geode.

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Spring Cloud Data Flow 1.0.0 M3 Released

On behalf of the team, I am pleased to announce the 1.0.0.M3 release of Spring Cloud Data Flow.

Over the last few months, we have added exciting new features and improvements to the overall orchestration of data microservices on a variety of platforms. We have also made some changes that significantly benefit developers, such as exposing Spring Boot Starters for all of the stream and task applications we publish. Following are some of the highlights from this release:

  • Provides the foundation for the following Data Flow Server implementations that have also been released today:
  • Introduces and builds upon the Spring Cloud Deployer Service Provider Interface
    • New multi-platform application deployment model factored out of Spring Cloud Data Flow itself for general purpose use, including AppDeployer and TaskLauncher to deploy long-running and short-lived microservices, respectively.
    • Improved application resolution strategy with support for maintaining a registry of applications as http, file, maven, docker, or hdfs artifacts
  • Builds upon Spring Cloud Stream 1.0.0.RC3
  • Builds upon Spring Cloud Task 1.0.0.M2
  • Improves DSL support for streaming and batch pipelines
  • Adds "tap" support for streaming and batch pipelines
  • Applications
    • Supports out-of-the-box stream applications built from the new Stream Application Starters project (auto-generated apps for both Kafka and RabbitMQ binders)
    • Supports out-of-the-box task applications built from the new Task Application Starters project
    • Adds several new out-of-the-box stream and task applications
    • Improves custom application registration mechanics from the Shell and Dashboard
  • Dashboard
    • New and improved Dashboard
    • Adds Batch and Task support
    • Adds "Apps" tab to monitor and manage out-of-the-box and custom applications
  • Flo for Spring Cloud Data Flow
    • Modern look and feel with several UX improvements around the palette, auto-layouts, auto-linking, canvas, nodes, node connectors, and many more
    • Adds support for a scriptable-transform processor that accepts ruby, groovy, python, or javascript code for runtime compute logic
    • Adds visual distinction between primary and tap’d pipelines
    • Adapts to Angular style tooltips
    • Graph layout optimizations
  • Improved IT and TCK tests
  • Adds new samples
  • Adds new logo
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Spring Cloud Data Flow for Cloud Foundry 1.0.0.M2 released

On behalf of the Spring Cloud Data Flow team, I am pleased to announce the 1.0.0.M2 release of Spring Cloud Data Flow for Cloud Foundry.

Spring Cloud Data Flow for Cloud Foundry allows one to use all the goodness of Spring Cloud Data Flow (like the Shell, UI and Flo) while targeting Cloud Foundry as a backend. Stream components are deployed as individual apps in Cloud Foundry, leveraging the power of the platform to handle scaling and health monitoring.

This second milestone
- builds upon the 1.0.0.M1 release of Cloud Foundry implementation of spring-cloud-deployer
- builds upon the 1.0.0.M3 release of Spring Cloud Data Flow
- builds upon the 2.0.0.M4 release of the Cloud Foundry java client, leveraging its reactive programming style

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Spring Cloud Data Flow for Apache Mesos and Kubernetes 1.0.0.M2 versions released

On behalf of the team, I am pleased to announce 1.0.0.M2 releases of Spring Cloud Data Flow for Apache Mesos and Spring Cloud Data Flow for Kubernetes.

Spring Cloud Data Flow for Apache Mesos allows one to use all the goodness of Spring Cloud Data Flow (like the Shell, UI and Flo) while targeting Apache Mesos as a backend. Stream components are deployed as individual apps using Marathon, leveraging the power of the platform to handle scaling and health monitoring.

This second milestone

  • Builds upon 1.0.0.M1 release of the Spring Cloud Deployer Mesos/Marathon implementation
  • Builds upon 1.0.0.M3 release of Spring Cloud Data Flow
  • Replaces spring-cloud-marathon-connector with environment variables for service connection parameters. We will improve the service discovery in future release and tie in to Mesos/Marathon’s native service discovery mechanism for runtime credentials
  • Adds support to resolve, register, and run OOTB and custom apps as docker images
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Spring Cloud Data Flow for Apache YARN 1.0.0.M2 released

On behalf of the Spring Cloud Data Flow team, I am pleased to announce the 1.0.0.M2 release of Spring Cloud Data Flow for Apache YARN.

Spring Cloud Data Flow for Apache YARN allows one to use all the goodness of Spring Cloud Data Flow (like the Shell, UI and Flo) while targeting Apache YARN as a backend. Stream components are deployed as individual apps in Apache YARN, leveraging the power of the platform to handle scaling and health monitoring.

This second milestone

  • builds upon the 1.0.0.M1 release of Spring Cloud YARN Deployer.
  • builds upon the 1.0.0.M3 release of Spring Cloud Data Flow.
  • Adds Spring Cloud Task support to orchestrate short-lived tasks including Spring Batch jobs in a YARN cluster.
  • Improved IP address discovery for internal coordination.
  • Adds support to host, resolve and register OOTB apps from HDFS.
  • Adds installable RPM bits.
  • Adds Ambari plugin to provision SCDF on YARN.
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Spring Cloud Task 1.0.0.M2 is now available

We are pleased to announce the release of Spring Cloud Task 1.0.0.M2. This is the second milestone of the Spring Cloud Task project with some exciting new features!

What’s New?

With the M1 release of Spring Cloud Task, we introduced the concept of a task, the @EnableTask annotation, and the general concept of a microservice that has an end. We’ve been quite busy since then. Some of the highlights of what is new include:

  • Spring Cloud Task Listeners
  • Integration with Spring Cloud Stream
  • Integration with Spring Batch
  • More samples
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Spring REST Docs 1.1.0.RC1

Following 1.1.0.M1, it’s my pleasure to announce that Spring REST Docs 1.1.0.RC1 has been released and is available from https://repo.spring.io/milestone/.

What’s new?

HTTPie request snippet

A new HTTPie request snippet has been introduced. Similar to the existing curl request snippet, the new snippet contains the HTTPie command for a request. My thanks to Raman Gupta who contributed this new feature.

Reusable snippets

Snippets can now be created once with some common configuration and then reused. This reduces repetition when documenting common parts on an API, such as self links.

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Spring Session 1.2.0 RC3 Released

On behalf of the community, I’m pleased to announce the release of Spring Session 1.2.0.RC3. The release can be found in the Spring Milestone Repository (https://repo.spring.io/milestone/).

This release contains some fixes for the previous release.

Some highlights of the issues in this release include:

  • JDBC Support persists session attributes on a separate table. This was in response to the community feedback (thanks!)
  • Redis Session optimization
  • Preparations for improved Spring Boot auto configuration
  • Updated to Spring Data Hopper
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Webinar: Data Microservices with Spring Cloud Data Flow

Speakers: Mark Fisher & Mark Pollack, Pivotal
The future of scalable data processing is microservices! Building on the ease of development and deployment provided by Spring Boot and the cloud native capabilities of Spring Cloud, the Spring Cloud Stream and Spring Cloud Task projects provide a simple and powerful framework for microservice stream and batch processing.
At a higher level of abstraction, Spring Cloud Data Flow is an integrated orchestration layer that provides a highly productive experience for deploying and managing sophisticated data pipelines consisting of standalone microservices. Streams and tasks are defined using a DSL abstraction and can be managed via shell and a web UI. Furthermore, a pluggable runtime SPI allows Spring Cloud Data Flow to coordinate these applications across a variety of distributed runtime platforms such as Cloud Foundry, Apache Mesos and Apache YARN.
During this webinar you’ll see an overview of Spring Cloud Data Flow, with live demos of streaming and batch apps, on different platforms ranging from local cluster to a remote Cloud to show the simplicity of the developer experience.

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Webinar: Introducing Spring Cloud Task

Speaker: Michael Minella, Pivotal

One of the major promises of the cloud is that of flexibility. Today, most applications deployed to the cloud are long running processes that use the flexibility of cloud scaling. But computing is full of short lived tasks that start up, do their work, and then terminate. These tasks are excellent cloud use cases since resources can quickly be provisioned - and reclaimed.

In this webinar, we’ll explore a new project in the Spring Cloud portfolio, Spring Cloud Task, a new framework for developing and orchestrating short-lived microservices. We’ll explore various use cases, build your first task, and discuss how to orchestrate tasks using various techniques. Finally, we’ll peek into the roadmap for the Spring Cloud Task project.

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