Let’s face it - when most teams talk about modernization, the focus tends to land on infrastructure upgrades, cloud migrations, or reworking legacy applications. But here’s the thing: your semantic models matter just as much.
With more organizations embracing self-service analytics, DevOps workflows, and cloud-native platforms like Microsoft Fabric, the way you design and manage your Power BI models needs to keep up. This isn’t just a technical detail - it’s a core part of delivering trusted, scalable insights across your business.
That’s where Tabular Model Definition Language (TMDL) enters the picture.
TMDL brings code-first, Git-friendly structure to Power BI and Fabric semantic models. And when you evaluate it through the Gartner 7Rs of Modernization, it’s clear that modernizing your semantic layer is no longer a nice-to-have; it’s a must.
Let’s walk through how TMDL fits into each of the 7Rs, and why that should matter to every VP of IT, Director of Operations, and Head of Analytics building the future of BI in their organizations.
1. Retain: Improve What Still Works
You may have legacy SSAS tabular models or Power BI datasets that still deliver business value - but they’re hard to govern or enhance. TMDL allows you to:
- Convert models into clean, readable code
- Expose hidden logic, metadata, and security rules
For IT leadership and analytics managers, this creates immediate transparency without disrupting what’s already working.
2. Rehost: Migrate Without Redesigning
Moving from on-premises SSAS to Microsoft Fabric or Power BI Premium? TMDL makes lift-and-shift easier:
- Export existing models to TMDL format
- Automate deployment using pipelines - no GUI needed
For BI developers and platform teams, this cuts deployment time while aligning with DevOps best practices.
3. Replatform: Shift Runtimes with Confidence
Upgrading to a new tenant or adopting Microsoft Fabric’s lakehouse architecture? TMDL helps by:
- Abstracting the model from its environment
- Supporting flexible deployment to multiple workspaces
For technology leads overseeing platform strategy, this reduces duplication and avoids rework across business units.
4. Refactor: Clean Without Rewriting
Many semantic models accumulate clutter over time—redundant measures, inconsistent folder names, or unused calculations. With TMDL, you can:
- Script cleanup using Git workflows
- Apply model linting rules for consistency
For data governance managers and operations directors, this streamlines support and improves long-term maintainability.
5. Rearchitect: Design for Scale and Reuse
Enterprise BI strategies often require reusable, modular semantic models. TMDL enables:
- Composable model design across domains
- Code-generated models based on metadata catalogs
For enterprise architects and data platform owners, this supports consistency across business lines and accelerates model creation.
6. Rebuild: Enable Business Innovation
Sometimes the model itself no longer fits evolving KPIs or business structures. TMDL enables:
- Collaborative design through version-controlled code
- Rapid iteration aligned with business priorities
For analytics leads and product teams, this enables fast rebuilds that reflect changing goals and metrics.
7. Replace: Leave Legacy Cubes Behind
Still using legacy cubes or outdated BI tooling? TMDL is the on-ramp to modern BI. It supports:
- Greenfield development in Microsoft Fabric
- GitOps and CI/CD for model lifecycle management
For CIOs and digital transformation leaders, this replaces brittle legacy assets with scalable, future-ready architecture.
Why This Matters: Modernization Can’t Skip the Semantic Layer
Power BI and Microsoft Fabric are redefining how organizations build analytics platforms. But your semantic layer is the connective tissue between raw data and decision-making.
TMDL gives your team the control, flexibility, and repeatability they need to keep pace with the rest of your modernization efforts. From infrastructure-as-code to model versioning, TMDL is built for today’s DevOps-driven, multi-cloud environments.
So whether you’re a VP of IT planning cloud migration, a Director of Data Engineering driving pipeline automation, or an Operations Executive looking for better reporting at scale, it’s time to give the semantic layer the attention it deserves.
Coming Up Next:
Stay tuned for more hands-on insights, including:
- Why TMDL is replacing JSON/Model.bim in enterprise BI workflows
- How to structure CI/CD pipelines for semantic models
- Linting and validation techniques for Power BI using Git
- Templates for federated modeling and centralized metrics
Interested in more of our blogs about Microsoft Fabric?
Curious how TMDL fits into your Fabric rollout?
Connect with us anytime - we’re happy to help you move from legacy to future-ready,
one model at a time.
About the Author
With almost thirty years of experience in the IT industry, Duane Colley is a Sr. Solution Architect specializing in the definition and delivery of adaptable, quality systems. Highly analytical, Duane leverages his experience to ensure that technology solutions effectively meet client’s business needs. Duane has experience in the Health, Telecommunications, Agribusiness, Government, Energy and Manufacturing industries.
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