Microsoft Fabric now lets you mirror Azure Databricks Unity Catalog (UC) into OneLake. That means BI teams can build Power BI Direct Lake models over your existing Delta tables, without copying data and you get an auto-generated SQL analytics endpoint for ad-hoc analysis. Net effect: weeks-to-days onboarding for Fabric, while your engineering and ML stay on Databricks.
What just changed (and why it matters)
- Mirrored UC → OneLake: Fabric mirrors UC metadata (catalogs/schemas/tables) and creates managed shortcuts to the same Delta data—so updates in Databricks show up in Fabric immediately, with no ETL.
- Built-in analytics surface: Each mirrored item gets a read-only SQL analytics endpoint; BI can also use Direct Lake for low-latency dashboards without scheduled refresh.
- GA status: Microsoft announced general availability in July 2025, positioning it as the “unified by design” bridge between Databricks and Fabric.
- Governance alignment: OneLake security now secures Mirrored Azure Databricks Catalog items (table/column/row levels), letting you map access for analytics consumers in Fabric. (Note: Unity Catalog permissions don’t auto-carry; you re-apply them in Fabric.)
Where Mirroring slots into your landscape
| Scenario | Best fit | Why |
|---|---|---|
| Keep Databricks for data engineering & ML; light up Fabric for BI | Mirrored UC + Direct Lake | Zero-ETL path for BI, sub-second dashboards, and an instant SQL surface—without moving pipelines. |
| UC-only governance, no Fabric | Power BI → Databricks SQL | All policy in UC, but you pay Databricks SQL for every query and accept higher latency/overheads. |
| Ad hoc links to raw storage | OneLake Shortcuts | Lightweight, but lacks the managed, end-to-end BI conveniences that mirroring adds. |
Mirroring gives you a bimodal operating model, Databricks remains your engine of record; Fabric becomes your governed consumption layer for BI now, not after a migration.
Security & governance: What to plan
- Acknowledge the split: UC permissions do not replicate to Fabric. Treat Fabric as an analytics perimeter.
- Map identities: Reuse the same Entra ID groups; assign OneLake security roles (table/column/row) on the mirrored item; layer Power BI RLS if needed.
- Network posture: If your ADLS is firewalled, enable Trusted workspace access so Fabric can securely reach data behind private networks—no need to open storage to public.
Cost & performance benefits
- Runtime cost: Direct Lake removes the Databricks SQL hop for BI queries; fewer compute minutes per dashboard interaction. (Databricks remains for pipelines/ML.)
- Refresh cost: Direct Lake reduces or eliminates scheduled refresh for many models, cutting both compute and operational toil.
- Scale cost: As BI users grow, mirroring scales primarily on Fabric/Power BI capacities rather than Databricks SQL. (Still choose Databricks SQL when UC-only policy enforcement is the top priority.)
Risks & mitigations
| Risk | Cause | Mitigation |
|---|---|---|
| “Views/streaming tables don’t show up.” | Mirroring focuses on Delta tables; some object types aren’t supported. | Publish physical Delta tables (Gold) for BI; keep advanced constructs inside engineering. |
| “Permissions drift between UC and Fabric.” | True; you must reapply access in Fabric. | Mirror Entra groups; codify access via infra-as-code where possible; add periodic access reviews. |
| “Firewall blocks Fabric.” | Standard in many enterprises. | Use Trusted workspace access; avoid broad exceptions on storage. |
A pragmatic 45-day rollout plan
Weeks 1–2: Readiness & design
- Validate region & tenancy; confirm data stores, firewall rules, and workspace trust.
- Pick 2–3 Gold Delta tables (clear ownership, strong business value).
- Define the access matrix (Entra groups → OneLake roles; RLS rules for BI).
Weeks 3–4: Stand-up & first value
- Create a Mirrored Azure Databricks Catalog item; enable auto metadata sync so new schemas/tables appear automatically.
- Publish a Direct Lake semantic model + executive dashboard; validate with the SQL analytics endpoint for ad-hoc.
Week 5–6: Harden & scale
- Extend governance to more domains; finalize naming/workspace strategy (Dev/Test/Prod).
- Define operational guardrails: data product SLAs, cost alerts, and incident playbooks.
FAQs
Is this a migration from Databricks to Fabric?
No. It’s a bridge that lets BI adopt Fabric immediately while engineering/ML remain on Databricks indefinitely—or transition later, service by service.
Do we lose Unity Catalog governance?
Not for engineering/ML. For analytics, you re-establish governance in Fabric using OneLake security and RLS so business users see only what they should.
Can we stay Databricks-first for analytics?
Yes—Power BI can query Databricks SQL directly. Mirroring simply adds a faster, cheaper consumption path for many BI scenarios via Direct Lake.
Conclusion
If your goal is rapid, governed BI on Fabric—without disturbing proven Databricks pipelines—mirroring UC is the fastest, lowest-risk path to value. It lets you show the business real dashboards now and choose migration later, not under pressure.
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