Most leaders I speak with assume that modernizing a legacy data platform means a season of shaky dashboards, frozen report requests, and a finance team quietly keeping a spreadsheet on the side, just in case. I think that assumption has it backwards. Reporting continuity is not something you hope survives the cutover; It is an architecture decision you make on day one.
My position is simple: If you are moving a legacy data platform to Azure Databricks and your business already relies on Power BI, treat the semantic model as the contract with the business. Protect the definitions people trust while you modernize the platform underneath. Microsoft Fabric can connect that foundation to reporting and AI without making every workload move to the same platform. The business should notice better answers, not have to relearn which numbers to trust.
Reports are where data meets decisions. The Chief Financial Officer (CFO) closes the quarter on them, sales leaders forecast from them, and operations teams run the week by them. When a migration disrupts reporting, the people who feel it first are the ones who never asked for the migration.
This year raised the stakes further. In July, Databricks and Microsoft expanded their partnership into the 2030s, integrating Azure Databricks more deeply across Power BI, Microsoft OneLake, Microsoft Purview, and Copilot. Then in September, Microsoft made Fabric IQ in Copilot generally available, so the business definitions captured in Power BI semantic models now inform answers inside Microsoft Copilot.
Your semantic model is no longer just the layer behind dashboards; It is becoming the business context your artificial intelligence (AI) reads. Protect it, and you protect both.
A Power BI semantic model holds the measures, relationships, and definitions the business agrees on: What counts as revenue, how on-time delivery is calculated, which hierarchy a region rolls up into. Reports bind to that model, not directly to the warehouse underneath it.
That separation is the whole opportunity. If the model keeps the same names, the same logic, and the same numbers, you can replace the engine beneath it without rewriting a single report. Think of it as renovating the plumbing in a hotel while guests are still checked in: They should notice better water pressure, never the wrench.
Many modernization plans stall on a single question: Azure Databricks or Fabric? I would start somewhere more useful: Which workloads need to move, and which business experiences need to stay steady? The platforms can share data, so coexistence is an option, not an instruction to buy both. Choose the role of each platform around the workload, the team that will run it, and the reporting contract you need to protect.
Fabric can mirror an Azure Databricks Unity Catalog, so its governed tables appear in OneLake without a new Extract, Transform, Load (ETL) pipeline or a second copy of the data. That capability is generally available. In the other direction, Azure Databricks can read data stored in OneLake directly, which has been generally available since June 2026.
In practice, data engineering and machine learning can stay on Azure Databricks, where many teams have built real depth. Power BI, Fabric, and Copilot consume the same governed tables. The Head of Data keeps the engineering investment, the Chief Information Officer (CIO) keeps one governance model, and the CFO keeps numbers that tie out.
Continuity comes from three habits, applied from the first week of the program.
Before converting any code, inventory the reports, semantic models, and measures people rely on, and who relies on them. This tells you which definitions form the contract and which legacy reports can simply retire.
Run the old and new platforms side by side and reconcile results against the source, row by row. A report switches over only once its totals tie out, so trust is earned before it is asked for.
Move workloads in small waves, each with a rollback plan. If one wave needs more work, the rest of the business keeps reporting as usual.
Taken together, these habits serve one idea: A number on a dashboard changes only when the business means it to. This is how we approach Azure Databricks migrations and modernization on Microsoft Fabric at KPI Partners, with accelerator-led conversion and side-by-side validation. Where the reporting layer itself is moving from a legacy tool to Power BI, our BI Modernization Accelerator rebuilds semantic models aligned with Fabric.
If your program is already underway, none of this asks you to start over. Freeze the definitions in your semantic models now and add a reconciliation gate to every remaining wave. If part of your estate sits on Azure Databricks and part is heading to Fabric, mirroring lets the two coexist instead of forcing a final platform decision mid-flight. The plan you have stays; It simply gains a guardrail.
A global supply chain and food service enterprise needed to retire its legacy reporting platform across the US, Japan, and Europe. Over 120 workbooks and 625 sheets had to move to Power BI without disrupting live reporting in any region.
Using a structured, accelerator-led approach, KPI Partners completed the migration before the legacy license expired, and reports now load 40 to 65 percent faster. What I take from it: When the definitions are protected and every wave is validated, continuity holds even across three regions and time zones.
The best compliment a migration can receive is silence from the business. But continuity is the floor, not the ceiling. Once your data runs on a governed foundation, the same semantic model that kept reports steady can ground Copilot and the agents that follow it.
So, change the engine, keep the dashboard, and let the business discover the upgrade through better answers. That is the order I would recommend to any leadership team planning its next move on Azure Databricks and Fabric.