Supercharge AI, BI, and data engineering with Semantic Link (Generally Available)
Great technology does not succeed on design alone—it succeeds when it helps people solve real problems. Semantic Link is one of those transformative capabilities in Microsoft Fabric: it brings AI, BI, and data engineering together through a shared semantic layer, enabling teams to work faster and more intelligently on the data they already trust.
From an idea to a multi‑persona capability
Semantic Link was created to enable data scientists to use semantic models directly in their notebooks. It has since evolved into a cross-Fabric capability that streamlines workflows for data scientists, BI engineers, data engineers, and admins.
Accelerate Data Science on trusted Semantic Models
Data scientists can use Semantic Link to connect to existing semantic models, run advanced analytics and predictions, and write enriched results back to OneLake, instantly updating downstream Power BI reports.
- Watch the demo: Semantic Link for data science
Automate Power BI tasks
BI engineers rely on Semantic Link to augment semantic models, validate data quality, translate models and reports, and migrate workloads across environments or capacities—all programmatically.
- Watch the demo: Semantic Link for Power BI automation
Streamline Data Engineering operations
Data engineers and admins can use Semantic Link to automate SQL and Spark tasks, optimize Lakehouse tables, and assess Fabric resources more efficiently, reducing operational overhead while improving reliability.
- Watch the demo: Semantic Link for data engineering
Driven by community, built to evolve
Much of Semantic Link’s momentum comes from community-led innovation from Semantic Link Labs, where early ideas from contributors like Michael Kovalsky have shaped features now supported natively in Fabric today.
With Spark Runtime 2.0 and Sempy 0.13.0, Semantic Link continues to expand across more Fabric artifacts—unlocking scenarios such as SPN‑based automation, report cloning and rebinding, semantic model translation, and Lakehouse optimization. As Fabric grows, Semantic Link scales with it, evolving alongside real customer scenarios across data science, BI, data engineering, and more.
Semantic Link isn’t just another feature—it’s becoming a foundational part of how data gets modeled, automated, and operationalized in Fabric. By trying it, you’re not only improving your own workflows; you’re contributing to a fast-growing community that is redefining how AI, BI, and data engineering come together. This is your chance to help shape one of Fabric’s most dynamic capabilities—jump in, experiment, and influence its future.
Together, we are building Semantic Link with the community—and for the community.
Get involved
We would love for you to help influence what comes next:
- Try it out: learn how to get started and explore supported scenarios from Semantic Link Documentation.
- Have an idea? Submit a feature request or contribute directly to Semantic Link Labs.
- See something valuable in Labs? Vote to help prioritize which capabilities should graduate into the core Semantic Link experience.
