On behalf of the team, I am pleased to announce the GA release of Spring Cloud Data Flow 1.1. Follow the links in the getting started guide to download the local server implementation and shell to create Stream and Tasks.

General highlights of the 1.1 GA Release include:

• Builds upon Spring Boot 1.4, Spring Cloud Camden SR2, Spring Integration 4.3 and Spring Cloud Task 1.1 release improvements.

• Adds LDAP, Basic and File based backend authentication

• Improvements to OAUTH backed authentication

• LDAP authentication is now supported with SSL

• Adds a form-based login page for non-OAUTH backend authentication methods such as the LDAP, Basic and File-based options

• Adds the ability to pass application specific properties via YAML file. This is particularly useful when deploying streams that require many deployment properties to be set.

• Portable deployment properties for memory, disk and cpu are in place for support across various runtime implementations.

• Documentation for the core REST API

• Updated reference documentation showing how to integrate with Spring Boot Admin to visualize server metrics and how to export Stream/Task application metrics to external application monitoring tools.

UI Highlights include:

• Bulk import and registration of stream and task app-starters

• Bulk import support for Task definitions. This allows importing task definitions from a file or the ability to add multiple task-definitions using the Flo-editor

• Adds Flo’s visual representation of running streams

• Add visual representation for related streams. This representation also includes nested TAPs and the downstream processing nodes in an overall topology view.

• List pages now support sorting

• Server-side search support for stream and task list pages

For the complete list of features, bug-fixes, and improvements, please refer to the closed 1.1 family of GitHub issues.

We welcome feedback and contributions! If you have any questions, comments or suggestions, please let us know via GitHub Issues, StackOverflow, or using the #SpringCloudDataFlow hashtag on Twitter.