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.

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On behalf of the Spring Cloud team it is my pleasure to announce a new milestone release of Spring Cloud Pipelines - 1.0.0.M3.

What’s new?

Apart from some bug and documentation fixes it’s providing an out of the box support for blue green deployment on Cloud Foundry (both for Concourse and Jenkins)! This is how we do it.

When you click deploy to prod

• we’re renaming the current instance of the app e.g. fooService to fooService-venerable

• we’re deploying the new instance of the app under the fooService name

• now two instances of the same application are running on production

When you click on the Complete switch over…

Speaker: Josh Long

Hi Spring fans! In this tip, we'll quickly look at how to integrate distributed tracing with Spring Cloud Sleuth and the OpenZipkin project.

Welcome to another installment of This Week in Spring! We've got a lot to cover this week so let's get to it.

• Fred Melo is one of Pivotal's brightest and when he talks about data, people listen. Here's your chance to watch his SpringOne Platform talk on data microservices
• The RabbitMQ Team has been busy! The RMQ Java Client has some very nice new (non-breaking) new features in Metrics, logging and Java NIO, among other things.
• Expanding on the metrics support in RMQ Java Client 4.0, the Rabbit team blogs about using JMX, Spring Boot Actuator and Coda Hale's awesome DropWizard Metrics library.
• I liked this SpringOne Platform talk, from Confluent's Joe Kutner, I Can't Believe it's not a Queue: using Apache Kafka with Spring…

Recorded at SpringOne Platform 2016. Speaker: Joe Kutner, Confluent Slides: http://www.slideshare.net/SpringCentral/i-cant-believe-its-not-a-queue-using-kafka-with-spring

Your existing message system is great, until it gets overloaded. Then what? That's when you should try Kafka.

Kafka is designed to be resilient. It takes the stress out of moving from a Spring monolith into a scalable system of microservices. Since you can capture every event that happens in your app, it's great for logging. You can even use Kafka's distributed, ordered log to simulate production load in your staging…

Recorded at SpringOne Platform 2016. Speaker: Fred Melo Slides: http://www.slideshare.net/SpringCentral/architecting-for-cloud-native-data-data-microservices-done-right-using-spring-cloud-64889916

Microservices are definitely offering best practice guidance for those architecting cloud native applications. The ability to quickly create small services that can be individually deployed, configured and scaled, as building blocks for scalable, highly distributed and fault-tolerant systems has been causing every company to rethink on how to architect modern systems and making Spring Boot shine in…

Recorded at SpringOne Platform 2016. Speakers: Gary Russell Slides: http://www.slideshare.net/SpringCentral/spring-for-apache-kafka

With the advent of the Kafka 0.9+ pure Java client, the Spring Team has created the new spring-kafka project with familiar Spring abstractions such as the KafkaTemplate, message listener container and POJO listener annotation @KafkaListener.

In this talk we'll take a look at the features of the project as well as the new version (2.0) of spring-integration-kafka which is now based on the Spring for Apache Kafka project.

Recorded at SpringOne Platform 2016. Speaker: Thomas Risberg Slides: http://www.slideshare.net/SpringCentral/spring-and-big-data

In this talk we will discuss ways to develop big data pipelines using Spring technologies. Learn how we can stream data into HDFS, run a Spark or a Hive job and extract the results from HDFS or Cassandra for presentation. The solution we develop will be a cloud-native pipeline that we will be able to run both locally and in the cloud.

The presentation focuses around the code for our solution and we also cover how to set up a test environment both locally and in the…

Recorded at SpringOne Platform 2016. Speaker: Vinicius Carvalho Slides: http://www.slideshare.net/SpringCentral/building-resilient-and-evolutionary-data-microservices

How can we build data pipelines that are resilient to change? Data usually outlives application code, and we have to be prepared to deploy streams that can cope with the evolution of that data that is in motion. This talk will discuss the approach and supporting patterns to write resilient data microservices with Spring Cloud Stream and Spring Cloud Dataflow. We will discuss the role of a centralized Schema repository, and how…