Mermaid JS: Export Diagrams to Dataverse

Key insights

• Mermaid JS converts text-based diagrams into a readable model format so you can define data structures with simple code instead of drawing shapes.
  
• Mermaid to Dataverse Converter parses Mermaid diagrams and provisions publishers, solutions, entities, attributes, and relationships directly into Dataverse, automating schema creation.
  
• ERD workflows: write an entity relationship diagram in Mermaid, run the converter, and the tool creates one-to-many, many-to-many and attribute definitions in your environment.
  
• CI/CD and version control: storing Mermaid files in Git lets teams track changes, review pull requests, and include schema deployments in automated pipelines to reduce human error.
  
• Common Data Model support: the converter can map diagrams to standard model elements, global choice sets, and reusable components to keep documentation and production aligned.
  
• Power Platform fit: the approach delivers repeatable, secure deployments for Power Apps and Power Automate projects, helps standardize data models, and speeds up low-code development.
  

Overview of the Demo

The Microsoft community call on September 11, 2025 showcased a practical demo that links diagramming with data provisioning. In the video, presenter Luise Freese walked viewers through a tool that converts Mermaid JS entity relationship diagrams directly into Dataverse tables, columns, and relationships. Consequently, the demo highlighted how simple text diagrams can move from documentation into a live data platform with fewer manual steps.

Moreover, the session framed this approach as a way to bring data modeling into source control and automation workflows. Freese emphasized repeatable deployments and security as key outcomes, noting that the conversion reduces human error and improves traceability. As a result, teams can align diagrams, code, and deployed schemas more tightly than before.

How the Conversion Process Works

First, modelers write ER diagrams using Mermaid JS syntax, which is plain text and easy to version. Then, an automated converter parses that text and generates the necessary artifacts for Dataverse, including entities, fields, and relationships. Finally, the tool provisions those artifacts into an environment so the diagram and the live schema match.

Freese demonstrated that the converter can handle common relationship types and many attribute patterns, and she showed how it maps diagrams to the platform’s schema constructs. She also explained that the converter is deployed as a web service, enabling integration with continuous integration and continuous delivery pipelines. Therefore, teams can automate deployments and include schema changes in code reviews and release processes.

Benefits and Tradeoffs

One major benefit is improved governance because schema changes live in version control and pass through the same review steps as application code. In addition, automation reduces manual configuration time and the risk of misalignment between documentation and production databases. Consequently, organizations adopting this method can scale modeling practices while maintaining consistency across environments.

However, tradeoffs exist. While converting simple entities and relationships is straightforward, modeling advanced Dataverse features such as business rules, plugin logic, or complex choice sets may require extra manual work or additional tooling. Thus, teams must balance the speed of automated provisioning against the effort to extend the converter for richer platform-specific behaviors. In practice, this means deciding which parts of the model to automate and which to manage with targeted manual or scripted interventions.

Technical Challenges and Limitations

Schema evolution poses a notable challenge because refactoring a live data model often involves data migration and backward compatibility concerns. Although the converter creates schema objects reliably, it does not automatically resolve existing data migration scenarios or complex attribute transformations. Therefore, teams need migration strategies, testing, and clear rollback plans when deploying changes to production environments.

Another limitation is governance and security nuances that go beyond basic schema deployment, such as role-based access, environment-specific settings, and managed solution layering. The demo addressed security at a high level, but integrating fine-grained permissions and organizational policies still requires careful configuration. Consequently, this approach best serves teams that pair automated provisioning with robust deployment controls and environment governance practices.

Adoption Considerations and Next Steps

Early adopters should start by modeling noncritical or new projects to validate the workflow and discover edge cases. Moreover, integrating the converter into existing CI/CD pipelines lets teams test deployments in sandbox environments before production, which reduces risk and builds confidence. Over time, teams can expand the scope to include more complex patterns once migration and governance processes mature.

Finally, community contributions and iterative improvements will shape how broadly this pattern is useful across organizations. As teams report back on real-world constraints—such as custom code hooks, performance tuning, and compliance needs—tooling can evolve to handle those scenarios. In short, the demo points to a practical path for bringing Infrastructure as Code principles into low-code platforms, but it also requires careful planning to manage tradeoffs and operational complexity.

Keywords

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