Spring Cloud Data Flow first emerged in 2015 as an evolution of the innovative Spring XD. Since then, hundreds of organizations such as CoreLogic have adopted this open source technology, which is available on Cloud Foundry and Kubernetes.
Generally available today, Spring Cloud Data Flow for Kubernetes addresses the needs of streaming and batch processing in the enterprise. This new offering is only available through the VMware Spring Runtime subscription. Read on for more details, dig into the documentation, download the bits, and watch the videos (linked at the end of this post) on how to get started with Spring Cloud Data Flow on Kubernetes.
Recap: Spring Cloud Data Flow
Spring Cloud Data Flow is ideal for modernizing existing batch workloads and implementing modern streaming data applications on Apache Kafka, RabbitMQ, and other popular messaging platforms. The microservices-based architecture enables full CI/CD deployment of your applications across a range of use cases, from ETL, import/export, and event streaming to predictive analysis and IoT.
Spring Cloud Data Flow provides a productive authoring environment for building and deploying data-intensive workloads to Kubernetes that includes:
Familiar Unix-inspired pipes
A filter-based DSL (domain-specific language) for data pipeline definitions
More than 60 pre-built data integration apps as well as custom apps
Lifecycle management APIs for data pipelines
An interactive visual interface for composing data pipelines
With so many data pipeline workloads now moving to production at scale, we’ve heard the calls for commercial-grade support and features specific to enterprise production scenarios.
Spring Cloud Data Flow for Kubernetes 1.0 builds on the popular open source project, addressing the specific needs of enterprise deployments with:
24/7 enterprise support
Certified container images
Compatibility with any Kubernetes distribution
A Day 2, continuously updatable platform
Peeling back the covers
Spring Cloud Data Flow for Kubernetes is a brand-new, VMware-certified distribution of Spring Cloud Data Flow that runs on any Kubernetes distribution. You still get all the features available in open source, but we’ve added commercial support along with new, exclusive, production-grade features to meet your expectations for installing, operating, and upgrading the platform in an enterprise setting.
Out of the gate, Version 1.0 satisfies three common customer requests.
Continuously updatable platform for day 2 operations
It’s no fun installing software only to watch it go stale and become potentially vulnerable due to challenges upgrading to the latest release.
Since the toolkit’s inception, the Spring Cloud Data Flow team has carefully curated its upgrade paths and app compatibility to enable minimal disruption to customer operations. With the release of Spring Cloud Data Flow for Kubernetes, we are automating the upgrade experience so you can automatically drive your CI/CD systems to continuously patch or upgrade to the latest versions. Any heavy lifting related to migration paths, dependencies, and “dev” and “production” environment overlays are handled without user intervention.
Instead, you get to focus on delivering business value, confident that you’re using the latest and most stable Spring Cloud Data Flow components and taking advantage of the newest features and performance improvements.
With this release we are tackling air-gapped deployments—where no internet connectivity is available—by providing an efficient path to provisioning Spring Cloud Data Flow’s software components on any upstream-compatible Kubernetes, on any private/public cloud platform.
Spring Cloud Data Flow’s abstractions and pluggable services give you maximum flexibility, allowing you to choose and change pluggable service implementations without having to change any code. This release continues to follow the pluggable model by providing support for message brokers, enterprise identity, and relational databases.
The messaging abstraction has implementations for RabbitMQ, Apache Kafka, and many other message brokers that power both the event-driven and streaming architectures. Batch processing also supports the pluggable model framework for relational databases.
We’re just getting started! The VMware Spring Runtime subscription already provides commercial support for your Java stack—including JDK, Tomcat, Spring Boot, and more. Now it also includes support for all the unique capabilities in Spring Cloud Data Flow for Kubernetes.
Get started with Spring Cloud Data Flow for Kubernetes
Download Spring Cloud Data Flow for Kubernetes today, and start building batch and streaming workloads with Spring Boot with the help of our video series:
Additional Spring Cloud Data Flow resources:
About the AuthorMore Content by Sabby Anandan