Summary of Feature Request
Provide the ability to rename or repoint logical table source object within the FDI Semantic Model Extensions / Sandbox framework, with automated cascading updates to all dependent joins, column mappings, and presentation folders.
Current Limitation & Pain Point
When building custom dimensions or facts in FDI, developers frequently establish or refine/align to enterprise naming conventions during the implementation lifecycle.
Currently, once a custom object is created and saved, its primary object name / logical identifier is permanently locked in. If a developer needs to align an object with updated enterprise naming standards or correct a naming error, they face three frustrating choices:
Option A (Manual Rework 😵): Delete the table and recreate it from scratch—losing all defined logical joins, selected columns, data type configurations, and subject area folder mappings.
Option B (Workaround Hack 🚫): Export the Sandbox JSON payload or .aab bundle, manually execute global string replacements in a text editor, and re-import the file - a risky process!
Option C (Do Nothing & Accept Technical Debt 💸): The reality for most enterprise teams. Because fixing a table name requires too much manual effort, teams simply choose to live with incorrect or non-standard naming conventions indefinitely.
Proposed Functionality 💡
Editable Table Identifiers: Unlock the Logical Table Source / Object Name field in the FDI Semantic Model Extensions and Sandbox UI for custom-built objects.
Automated Cascade Updates: When an object is renamed, automatically update all downstream dependencies in the background, including:
Logical Join conditions / Logical Table references.
Presentation folder sources in the Subject Area. Internal Sandbox extension step payloads.
Dependency Impact Preview 🤩: Display a brief confirmation dialog listing the dependent joins and subject area folders that will be automatically updated as a result of the rename.
Business Value & Impact 🚀
Eliminates Technical Debt: Allows teams to maintain clean, professional, and governed data models without accumulating legacy naming errors.
Dramatically Reduced Rework: Saves hours of tedious, repetitive manual recreation per customization when naming standards evolve.
Lower Implementation Risk: Removes the reliance on manual JSON refactoring hacks or .ab bundle editing to update backend metadata.
This rigidity forces technical debt into enterprise environments, degrades data model governance, and creates long-term confusion for future developers and business analysts who must maintain these semantic extensions.
