An open-source data mapping solution with a web UI for configuring integrations between Java, XML, and JSON data sources.
AtlasMap is an open-source data mapping solution that provides a web-based user interface for configuring integrations between Java, XML, and JSON data sources. It allows users to visually design data mappings on a canvas and execute them via a runtime engine, simplifying complex data transformation tasks. The project is designed to work within integration platforms like Syndesis while also offering a standalone experience.
Developers and integration specialists working on data transformation projects involving Java, XML, or JSON formats, particularly those using or building integration platforms like Syndesis.
AtlasMap offers a visual, low-code approach to data mapping, reducing the need for manual configuration and accelerating integration development. Its standalone runtime and web UI provide flexibility for both embedded and independent use cases.
AtlasMap project repository
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Provides an interactive web-based UI for designing data mappings visually, reducing manual coding and accelerating integration workflows, as emphasized in the README.
Seamlessly handles transformations between Java, XML, and JSON data sources, covering common integration scenarios without needing separate tools.
Mappings can be executed via a dedicated runtime, allowing deployment in production environments independent of the UI, as shown with the downloadable jar.
Optimized for use within the Syndesis integration platform, offering a cohesive experience for users in that ecosystem, as stated in the README.
Supports live updates during UI development with React and storybook demos, facilitating iterative design and testing, as detailed in the build instructions.
Building and running the project requires multiple steps with Maven and yarn, including separate terminal windows for server and UI, which can be cumbersome and error-prone.
The README admits that the standalone UI is still being improved, indicating potential instability or missing features for independent use cases.
Visual mapping engines may not be as efficient as custom-coded transformations for large or complex datasets, introducing additional processing overhead.
Requires specific tools like Java, Maven, and yarn, which might not be standard in all development environments and add to the learning curve.