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…

On behalf of the team, I am pleased to announce that Service Release 5 of the Spring Cloud Camden Release Train is available today. The release can be found in our Spring Release repository and Maven Central. The documentation can be found here.

Included in this release is the Spring Boot 1.5 compatibility of all the Spring Cloud projects. Other than the addition of Spring Cloud Task, this release includes primarily bug fixes.

NOTE: This release is not compatible with Spring Boot 1.3. In other words your Spring Boot 1.3 application will not work with Camden.SR5.

The following modules were…

It is my pleasure to announce that the Spring for Apache Kafka 1.1.3 maintenance release is available now.

As usual, thanks to the community for any feedback and contribution as always. Looking forward for more!

This release contains several bug fixes, including proper offset commit handling when using a BatchListener; therefore an upgrade is highly recommended.

Right now master has been switched to the version 2.0 for Java 8 and Spring Framework 5.0 code base. We have some plans for high-level API for Kafka Streams and Reactor Kafka support.

Project Page | GitHub | Help | Documentation

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…

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. 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: 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. 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…