Data integration often involves connecting several processing steps, from importing records to transforming and forwarding events. Spring Cloud Data Flow provides a platform for assembling and operating those pipelines from Spring applications, including task and streaming workloads. It coordinates the applications rather than replacing their processing code. Its open-source maintenance stream ended in 2025, with future releases moving to a commercial model, so an existing installation needs a support decision alongside any technical upgrade or redesign.
Spring announced the end of open-source maintenance for Spring Cloud Data Flow in April 2025, with future releases under a commercial model. This status applies to the OSS maintenance stream; it does not mean the product vanished.
Review an existing deployment
Identify the installed version, support entitlement, deployed task and stream applications, broker dependencies and upgrade constraints. A historical Helm chart can remain downloadable after its support assumptions have changed. Confirm your supported path before upgrading or rebuilding from an old tutorial.
Choose by workload
Assess continued commercial support against a deliberate redesign using supported batch, workflow or streaming components. Replacing an orchestration platform is not a chart rename: inventory job state, schedules, application packaging and failure recovery. Test representative jobs and data flows before retiring the existing control plane.
Historical upstream link check · 2026-10-09
The recorded upstream address returned HTTP 404 on 2026-10-09; that URL was unavailable in this check. Link availability does not certify the historical installation instructions or current security support. Current Spring Data Flow documentation is available. Spring's April 2025 announcement ended open-source maintenance of Data Flow; current commercial support must be assessed separately.
Website availability is separate from project, chart and image support. Use the current guidance and primary sources on this page to assess the distribution.
Historical Kubedex content
Preserved for context. Commands, versions, prices and results below reflect the original research.
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.
Overview
- 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.
Sources & further reading
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