Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.

Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics.


Chart Details


This chart will provide a fully functional and fully featured Spring Cloud Data Flow installation that can deploy and manage data processing pipelines in the cluster that it is deployed to.

MySQL and Redis are used as the stores for Spring Cloud Data Flow state and RabbitMQ is used for the pipelines’ messaging layer.




  • The Spring Cloud Data Flow server uses Spring Cloud Deployer, to deploy pipelines onto modern runtimes such as Cloud Foundry, Kubernetes, Apache Mesos or Apache YARN.
  • A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation.
  • Custom stream and task applications, targeting different middleware or data services, can be built using the familiar Spring Boot style programming model.
  • A simple stream pipeline DSL makes it easy to specify which apps to deploy and how to connect outputs and inputs. A new composed task DSL was added in v1.2.
  • The dashboard offers a graphical editor for building new pipelines interactively, as well as views of deployable apps and running apps with metrics.
  • The Spring Could Data Flow server exposes a REST API for composing and deploying data pipelines. A separate shell makes it easy to work with the API from the command line.


Spring Cloud Data Flow builds upon several projects and the top-level building blocks of the ecosystem are listed in the following visual representation. Each project represents a core capability and they evolve in isolation, with separate release cadences – follow the links to find more details about each project.

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