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Spring Cloud Data Flow 1.2 M1 released

On behalf of the team, I am excited to announce the release of the first milestone of Spring Cloud Data Flow 1.2.

Note: A great way to start using this new release(s) is to follow the release matrix on the project page, which includes the download coordinates and the links to the reference guide.

Over the last few weeks, we have added new features and improvements to the overall orchestration of data microservices. The following new features were included in the 1.2.0.M1 release:

Core

  • Introduce dedicated prefixes for deployment properties. Using the deployer properties is as simple as deployer.<appname>.xxx as opposed to app.<appname>.spring.cloud.deployer.xxx
  • Introduce a new REST-API controller and shell support to cleanup Task Executions
  • Foundation work to consolidate the use of controllers between Task deployments and Task Executions
  • Consolidate REST-API call traces and return codes for consistency
  • Adds role-based access control to define who has access to create, deploy, destroy, or view streams/tasks. This works seamlessly in coordination with the supported authentication methods.
  • Performance optimizations to “stream list” operation. Instead of making individual calls for each app associated with the stream, the newly introduced MultiStateAppDeployer SPI operation invokes a call per stream that queries all the application statuses in a single network call
  • Improves error reporting for “stream list” operation
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This Week in Spring - February 14th, 2017

Welcome to another installment of This Week in Spring! It’s Valentines Day for some, and so happy Valentines day to you! This week I’m in Memphis, Tennessee for business and then it’s off to Saint Louis where I’ll be presenting on Reactive Spring with my buddy Mark Heckler (join us!), then it’s off to Atlanta, Georgia, ahead of next week’s big DevNexus show. Check out what Pivotal is up to at the event!

As usual, we’ve got a lot to cover so let’s get going!

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SpringOne Platform 2016 Replay: Spring Data Hazelcast: Fluently Accessing Distributed Repositories

Recorded at SpringOne Platform 2016.
Speaker: Victor Gamov, Neil Stevenson, Hazelcast
Slides: http://www.slideshare.net/SpringCentral/spring-data-hazelcast-fluently-accessing-distributed-repositories

The primary goal of the Spring Data project is to make it easier to build Spring-powered applications that use data access technologies.

In this talk, Neil and Viktor will present using a new Spring Data for Hazelcast project and demonstrate how using the Spring Data paradigm gains the power of a distributed data repository.

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SpringOne Platform 2016 Replay: Streaming Live Data and the Hadoop Ecosystem

Recorded at SpringOne Platform 2016.
Slides: http://www.slideshare.net/SpringCentral/streaming-live-data-and-the-hadoop-ecosystem

It’s not always easy to get the data you need for analysis. And it becomes even more challenging if it is live streaming data you are working with. Learn how you can make Hadoop work for you in the most effective way possible, especially when it comes to adapting to the agile business requirements of today’s competitive environment. We will cover the Hadoop ecosystem – what is Hadoop, HDFS, MapReduce, Yarn, and then how leading open source projects such as Hive, Ambari, Ranger, Atlas, NiFi interact and integrate to support the variety of data used for analytics today.

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SpringOne Platform 2016 Replay: Spring with ApacheNiFi

Recorded at SpringOne Platform 2016.
Speaker: Oleg Zhurakousky
Slides: http://www.slideshare.net/SpringCentral/spring-with-apachenifi

Spring Integration has long captured the hearts and minds of the developers world wide for its emphasis on simplicity, modularity and productivity when it comes to all things related to work-flow orchestration and complex event processing and is successfully used in a variety of Big Data solutions. Apache NiFi, on the other hand, is a new addition to the already rich Big Data technology stack.

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SpringOne Platform 2016 Replay: Writing comprehensive and guaranteed up-to-date REST API documentation

Recorded at SpringOne Platform 2016.
Speaker: Andreas Evers, Ordina JWorks
Slides: http://www.slideshare.net/SpringCentral/writing-comprehensive-and-guaranteed-uptodate-rest-api-documentation

RESTful APIs are eating the world, yet all too often the documentation can cause indigestion for the APIs’ developers and their users. Developers have to deal with annotation overload, repetition, and an unpleasant writing environment. Users are then left with documentation that’s inaccurate and difficult to use. It doesn’t have to be this way.

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SpringOne Platform 2016 Replay: Real World Microservices with Spring Cloud, Netflix OSS and Kubernetes

Recorded at SpringOne Platform 2016.
Speaker: Christian Posta, Redhat
Slides: http://www.slideshare.net/SpringCentral/real-world-microservices-with-spring-cloud-netflix-oss-and-kubernetes

Building distributed systems - whether we call them SOA or microservices - is not easy. Open source communities like Spring, NetflixOSS and Kubernetes bring decades of experience building these systems, but the question always begs to be asked “do we implement these patterns in the application layer or in the infrastructure layer”?

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SpringOne Platform 2016 Replay: Orchestrate All the Things! with Spring Cloud Data Flow

Recorded at SpringOne Platform 2016.
Speakers: Eric Bottard & Ilayaperumal Gopinathan
Slides: http://www.slideshare.net/SpringCentral/orchestrate-all-the-things-with-spring-cloud-data-flow

What do things like Minecraft, a light bulb and your music library have in common? Well, nothing really. Until you come up with this crazy idea to link them together. This is where application orchestration comes in.

In this session, you’ll learn how Spring Cloud Data Flow allows easy composition of microservices together. As the spiritual successor to Spring XD and the natural sidekick of Spring Cloud Stream, Data Flow has been thought as a way to deploy, run and manage loosely coupled apps in the cloud.

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Spring Cloud Task 1.2.0.M1 is now available

We are pleased to announce that Spring Cloud Task 1.2.0.M1 is now available via Github and the Pivotal download repository. Many thanks to all of those who contributed to this release.

Spring Cloud Task 1.2.0.M1 offers the following features:

This is the first milestone for the 1.2.x line of Spring Cloud Task. Intended to continue the integrations required for Spring Cloud Data Flow, this release provides the following new features:

  • Better DataSource integration between task and batch - This release makes configuring the DataSource used by the task/batch integration easier.

  • Allows an external process to update the external execution id - Prior to this release, the external execution id (the execution id for the underlying platform) had to be updated by the task itself. In some use cases, this is not possible. This release exposes the ability to update the external execution id outside the scope of the task itself.

  • Allow the user to configure a prefix for the task tables - Similar to how Spring Batch allows a user to configure a prefix for the batch repository tables, Spring Cloud Task now exposes the ability to configure a prefix for task repository tables as well.

  • Add support for parent execution ids - In complex use cases, the ability for one task to launch multiple other tasks is very common. Spring Cloud Task has already supported this via partitioned batch jobs launching worker nodes as tasks. This release provides the ability within the task repository to associate the parent child relationship that occurs from that capability (both in the batch use case and with raw tasks).

  • Upgrade to Spring Cloud Camden SR5 - This release is compatible with Spring Cloud Camden’s recent SR5 release.

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