This past November, we announced the general availability of our Data Mapper, a tool for developers to perform data transformation tasks inside of Azure Logic Apps. Through customer engagements, we have gathered valuable feedback and are ready to share some enhancement plans to address this feedback. These updates streamline data mapping tasks, rendering your workflow more intuitive and efficient. Let’s explore the new features and discuss how these changes can improve your data mapping experience.
If you are interested in joining our Private Preview early this summer to provide feedback on these new capabilities, please fill out the form in the feedback section. Your opinions help shape future updates.
Improvements
- Create data map
Upload a new schema or select an existing one. The source schema, previously floating, now docks on the opposite side of the destination schema, enhancing visibility.
- Map a property to another property
Use drag-and-drop to assign source properties to destinations.
- Understand a property type
Hover over a property to discover its data type.
- Add a function then map to properties
- Function chaining
Address more complex requirements by chaining functions together. Collapse the functions together to save valuable real estate.
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Rename functions and add notes
To reduce complexity, we now allow function renaming for clarity and the option to add notes. This prevents confusion and makes editing and reviewing more straightforward. -
Reorder source properties
Add static values and reorder properties to refine output at destination -
Expand/collapse hierarchy
Support for complex schemas includes starting with nested properties in a collapsed state and expand as required to access deeper properties. -
Adjust width of side panel
Modify a side panel’s width to address scaling for deep schema trees. - Search within a schema
Search functionality to discover specific elements - Favorite function
Pin frequently used functions for quick access. - View underlying code
Open the YAML file in read-only mode to read the code that powers the mapping process. - Test map
Select an existing source payload matching your schema type and check whether the mapper yields desired output. - Understand if there has been an error
Easily detect and address errors during mapping
Conditional mapping and looping improvements to follow in next part soon.
Feedback
Please use this questionnaire to provide detailed feedback or file a feature request using the Data mapper tag on our GitHub Issues.