12 Critical Migration Hiccups from Power BI to Fabric — A Senior Architect’s Guide (2025)

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Microsoft Fabric is not simply an upgrade to Power BI—it represents a strategic shift from BI-as-a-service to a unified, lake-centric analytics fabric. This shift impacts data architecture, security, lineage, governance, engineering processes, CI/CD, and the entire analytical supply chain.

From an enterprise architect’s viewpoint, the migration cannot be treated as a “move your PBIX files and continue” exercise. It requires a reorientation of the analytical operating model, new architectural patterns, and a redefinition of ownership across data engineering and BI teams.

Below are the actual, high-impact hiccups senior architects encounter—along with the recommended strategic guidance that has now become industry best practice.

1. Workspace Mode Misalignment: The Root of Architectural Drift

Fabric workspaces behave fundamentally differently from classic Power BI Premium and PPU workspaces.
Switching a workspace to Fabric mode changes:

  • Object lifecycle

  • Capacity consumption

  • Inventory of available runtimes

  • Deployment scope

Architect Warning:

Many organisations unknowingly switch workspaces to Fabric mode and break refresh pipelines, embed scenarios, or downstream APIs.

Architect Recommendation:

Create a Workspace Readiness Matrix aligned to Fabric SKUs (F64/F128/F256+), mapping:

  • Object types

  • Refresh dependencies

  • API surface usage

  • Data engineering workloads

  • Semantic model compute cost

This matrix must be approved in architecture governance before migration.

2. Semantic Model Re-Engineering: Not a One-to-One Dataset Port

Migrating datasets into Fabric’s DirectLake-enabled Semantic Models introduces new constraints:

  • DAX behaviour changes

  • Materialisation rules differ

  • Relationship enforcement tightens

  • RLS becomes lake-aware

Architect Warning:

Treating semantic models as “datasets with new names” results in broken relationships, inconsistent refresh behaviour, and incorrect report outputs.

Architect Recommendation:

Run a Semantic Model Compatibility Assessment, evaluating:

  • Unsupported DAX patterns

  • Column cardinality issues under DirectLake

  • Aggregation tables and composite model behaviour

  • RLS/OLS remapping

High-end architects re-engineer the model rather than attempting a direct migration.

3. Metadata Extraction Gaps: Tools Break Without Warning

Fabric introduces new workloads such as Lakehouses, Warehouses, Pipelines, Notebooks, and Real-Time Analysis.

Architect Warning:

Legacy Power BI metadata extraction scripts and vendor tools (including regression systems) may fail or show incomplete lineage. Unsupported visuals result in complete export failures.

Architect Recommendation:

Architects must adopt Fabric-level metadata APIs and implement a Metadata Abstraction Layer to ensure consistent lineage extraction before migrating—especially for regulated industries.

4. OneLake Misgovernance Leads to Data Sprawl

Fabric’s OneLake centralises storage, but without governance:

  • Shortcuts proliferate

  • Delta tables duplicate

  • Data domains overlap

  • Costs become unpredictable

Architect Warning:

Teams treating OneLake like a file system rather than a governed logical data lake create irreversible architectural debt.

Architect Recommendation:

Publish a OneLake Governance Blueprint covering:

  • Domain-driven folder structures

  • Shortcut ownership

  • Delta table lifecycle policies

  • Data product certification paths

Architects define OneLake standards before onboarding even a single Lakehouse.

5. Dataflow Gen1 → Gen2 Migration is Not a Lift-and-Shift

Gen1 and Gen2 differ in:

  • Execution engines

  • Storage layers

  • Dependency chains

  • Logging semantics

Architect Warning:

Teams assume Gen1 flows will “just work.” In practice, they often break due to missing connectors, gateway changes, and transformation complexity.

Architect Recommendation:

Senior architects re-platform heavy transformations into:

  • Fabric Pipelines

  • Data Factory workflows

  • Spark Notebooks

Gen2 should only be used for lightweight transformations, consistent with Fabric’s medallion architecture.

6. RLS/OLS Behavior Diverges Under DirectLake

DirectLake bypasses traditional import semantics.

Architect Warning:

RLS rules applied at the BI layer may not propagate correctly when data is referenced from multiple lakehouses or warehouse endpoints.

Architect Recommendation:

Architects deploy an End-to-End Security Simulation Suite that validates:

  • Row filters

  • Object permissions

  • Shortcut-driven access

  • Cross-domain lineage

This must be automated before go-live.

7. Refresh Pipelines Break Without Capacity-Aware Scheduling

Fabric introduces:

  • New refresh engines

  • CU-driven throttling

  • Priority-based resource allocation

Architect Warning:

Running existing Power BI schedules inside Fabric can overload capacity, causing cascading refresh failures.

Architect Recommendation:

Architects must design a Capacity-Aware Refresh Architecture, including:

  • Pattern-based refresh intervals

  • Workload isolation (semantic models vs pipelines)

  • Incremental segmentation aligned with Delta logs

  • Failure escalation logic

8. Embedding Scenarios Change Significantly in Fabric Mode

ISVs using embedded analytics face:

  • Token generation changes

  • Service principal restrictions

  • Workspace permission inconsistencies

  • API incompatibilities with DirectLake

Architect Warning:

Switching a workspace to Fabric mode without reconsidering embedding architecture leads to production outages.

Architect Recommendation:

Architects adopt the Fabric Embedded Architecture Decision Framework, defining:

  • Supported object types

  • Token refresh patterns

  • Multi-tenant workspace isolation model

  • Governance of semantic model roles

9. Cost Overruns Due to Misinterpreted Capacity Units (CU)

Fabric does not mirror Power BI Premium’s v-cores.

Architect Warning:

Assuming “P1 ≈ F64” is a costly mistake. Real-time analytics and Spark workloads consume CU exponentially during bursts.

Architect Recommendation:

Run a Fabric Workload Heatmap Simulation, ensuring capacity aligns with:

  • Expected ingestion volume

  • Semantic model refresh windows

  • Spark job concurrency

  • Data engineering peak usage

10. Schema Evolution Risks with Delta Tables

Delta tables introduce governed schema enforcement, which Power BI teams are unfamiliar with.

Architect Warning:

Uncontrolled schema evolution breaks downstream semantic models and SQL endpoints.

Architect Recommendation:

Architects enforce:

  • Contract-first schema design

  • Schema evolution alerts

  • Controlled merge patterns

  • Lakehouse-to-semantic model versioning rules

11. Developer Maturity Gap: BI Teams Are Forced Into Engineering

Fabric requires:

  • Git discipline

  • Spark proficiency

  • Lakehouse modelling

  • Medallion patterns

  • CI/CD pipelines

Architect Warning:

BI teams migrating without readiness fall back to anti-patterns (manual fixes, siloed PBIX updates, ungoverned shortcuts).

Architect Recommendation:

Launch a Capability Uplift Program covering:

  • Spark fundamentals

  • Version control with Git

  • Modular semantic model design

  • Enterprise CICD with YAML pipelines

12. Lineage & Governance Discontinuity During Migration

Fabric expands lineage dramatically across:

  • Lakehouses

  • Pipelines

  • Notebooks

  • Semantic models

  • Reports

Architect Warning:

Existing Purview configurations rarely capture Fabric objects correctly.

Architect Recommendation:

Architects define a Unified Governance & Metadata Operating Model:

  • Continuous scanning

  • Domain-level stewardship

  • Cross-workload dependency maps

  • Materialized lineage contracts

Practical Migration Blueprint for Senior Architects

[Assessment] → [Fabric Mode Planning] → [Semantic Model Redesign]
→ [OneLake Governance Setup] → [Pipeline Modernization]
→ [Security Simulation] → [CICD Fabric Enablement]
→ [Fabric Governance Rollout] → [Enterprise Adoption]

This is not a technical migration.
It is a strategic platform transformation.

High-Level Case Study – How Successful Architects Approach Migration

A Fortune 200 enterprise attempted a straightforward Power BI → Fabric migration.
Outcome:

  • 40% refresh failures

  • RLS mismatches across domains

  • Governance loss in lineage

  • Engineering teams overwhelmed by Spark workloads

Architect Intervention:

  1. Established a Fabric governance council

  2. Rebuilt semantic models for DirectLake

  3. Implemented a domain-oriented OneLake structure

  4. Enabled CI/CD with Git-integrated semantic model deployment

  5. Enforced schema contracts & shortcut ownership

Result:

  • 97% refresh success

  • 50% cost reduction via capacity optimization

  • Full lineage restored and automated

  • Lakehouse adoption scaled across six domains

Why Architects Must Treat This Migration as a Platform Redesign

  • Fabric changes data ownership, not just data storage

  • It unifies engineering, BI, governance, and operations

  • It shifts the BI team from report creators to data product owners

  • It introduces engineering rigor into previously business-owned workloads

Senior architects must set the standards — or Fabric adoption becomes fragmented and expensive.

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