Power BI Desktop: TMDL Is a Game-Changer

Key insights

• TMDL (Tabular Model Definition Language): A text format that describes a Power BI semantic model — tables, columns, measures, relationships and parameters.
  It lets you view and edit the model as code instead of only using the visual UI.
• Model-as-code and script edits: Open the TMDL view to write or paste model scripts and make bulk or low-level changes that the UI can’t easily do.
  Use this to switch modes (Import vs DirectQuery), edit many measures at once, or tweak metadata.
• Diff preview for safer changes: The editor shows a before-and-after comparison so you can see additions, removals and edits before applying them.
  This reduces risk and helps you audit model updates quickly.
• Automation and source control: Export and re-import TMDL files to store models in source control and include them in CI/CD pipelines.
  Teams can automate deployments and use external tools or AI to generate model changes.
• Editor improvements and named expressions: The TMDL editor supports DAX and Power Query highlighting, drag-and-drop of object sections, and scripting of named expressions and parameters.
  These features speed up development and make large edits easier to manage.
• GA and how to start: TMDL reached general availability in 2025 and is available in Power BI Desktop’s modeling or semantic model view.
  Open the TMDL view, preview changes with the diff tool, then apply or export scripts for automation or source control.

In a recent YouTube video, Guy in a Cube highlights how TMDL reshapes model work in Power BI Desktop, letting users edit semantic models as text instead of relying solely on the visual interface. He explains practical examples such as switching modes between Import and DirectQuery, editing perspectives, and bulk-changing measures, and then demos how the editor previews differences before applying edits. As a result, the video frames TMDL as a shift that turns desktop users into model-savvy editors, while also raising important questions about risk and governance.

Overview of TMDL and the Video's Focus

Guy in a Cube presents TMDL as a textual, declarative representation of the Power BI semantic model that includes tables, columns, measures, relationships, and more. He demonstrates the built-in TMDL view in Power BI Desktop, showing how you can open the model as editable text, paste scripts, and preview the effects before applying changes. Consequently, the video frames TMDL not just as a convenience but as a new paradigm for how model work can be scripted, versioned, and automated across teams.

What TMDL Lets You Do

In practical terms, the demo covers capabilities such as bulk-editing measures and metadata, flipping connection modes, and dragging model objects into the editor to produce scripted edits. Moreover, the editor supports syntax highlighting for DAX and Power Query expressions, while the diff preview highlights additions, removals, and modifications in a clear, color-coded view. Therefore, users get both power and visibility: they can make complex changes quickly, and they can inspect what those changes will do before committing them to a model.

New Features and General Availability

Guy points out that by 2025 the TMDL view became generally available, and Microsoft added support for named expressions so parameters in Power Query and the semantic model can be scripted. He also highlights integration with modern data sources, including editing scenarios that work with Direct Lake on OneLake and mirrored catalogs, improving parity between cloud and desktop experiences. As a result, teams can adopt more consistent workflows across environments, especially when they combine TMDL edits with external tooling and source control.

Furthermore, the video emphasizes improved robustness in opening models from other tools, and shows how dragging sections from the model explorer into the editor speeds up scripted authoring. These refinements make TMDL feel closer to a lightweight IDE, which supports contextual hints and formatting that reduce friction for authors. Consequently, the feature invites a broader set of users to explore code-based model editing while retaining familiar visual modeling when needed.

Tradeoffs and Practical Benefits

On one hand, TMDL offers unprecedented control: teams can automate repetitive changes, integrate models with CI/CD, and store model definitions in source control for auditability. On the other hand, this extra control introduces risks, because textual edits can unintentionally break relationships or performance settings if not tested properly. Therefore, the real benefit depends on how well teams balance automation with safeguards like staging environments, automated tests, and review processes that catch regressions before they reach production.

Additionally, turning models into code encourages better collaboration by making changes reviewable and repeatable, yet it demands new skills from analysts who may be comfortable in the UI but unfamiliar with model scripting. Thus, organizations will need to invest in training and clear governance so that empowering users does not lead to fragmentation or fragile datasets.

Challenges and Recommended Practices

Guy also addresses common challenges, including the learning curve for non-developers, potential merge conflicts when multiple authors edit the same model, and the need to validate performance after mode changes such as switching to DirectQuery. He suggests practical steps like keeping a copy for experimentation, using the diff preview before applying edits, and integrating edits with source control to track who changed what and why. As a result, teams can adopt a cautious approach that unlocks the benefits of TMDL while limiting accidental disruptions.

Conclusion: A New Era for Model Management

Overall, the Guy in a Cube video portrays TMDL as a meaningful evolution for Power BI Desktop that supports automation, detailed customization, and tighter integration with software development practices. However, the video also makes clear that organizations must weigh flexibility against the need for testing, governance, and upskilling to avoid introducing instability into shared models. In short, TMDL opens powerful possibilities, but teams should adopt it deliberately and with practices that reduce risk while maximizing productivity.

Keywords

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