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The Five Fs of Fabric Rationalization

Fabric rationalization​

Fabric rationalization is the process of evaluating data and analytics assets—such as reports, pipelines, Lakehouses, Warehouses, and data products—to determine the best modernization or migration path into the Microsoft Fabric ecosystem.

Because Microsoft Fabric is a unified SaaS platform, traditional cloud migration approaches (like rehost or rearchitect) require reinterpretation. Fabric rationalization focuses on maximizing reuse, optimizing for Fabric-native capabilities, and aligning workloads with business outcomes.

Rethinking the Five Rs: Introducing the Five Fs for Fabric​

To reflect the nature of Fabric workloads, we propose the Five Fs of Fabric Rationalization—a model to guide assessment and modernization decisions.

1. Fit​

Adopt Fabric-native services with minimal changes.

This approach evaluates whether a current workload (e.g., Power BI report, Synapse pipeline, SQL query) already fits well into Fabric’s architecture.

  • Power BI assets can usually be adopted directly.
  • T-SQL-based models or ELT jobs often map easily into Warehouses or Lakehouses.
  • Semantic models and DAX calculations can remain mostly unchanged.

Ideal when:

  • You want quick wins with minimal transformation.
  • Fabric-native equivalents are available and supported.
  • The focus is on integration, not redesign.

2. Fork​

Split monolithic workloads into domain-aligned, Fabric-optimized components.

Forking enables organizations to reframe centralized platforms into data mesh-aligned domain products—e.g., decoupling reporting from transformation logic.

  • Convert legacy ETL pipelines into multiple Fabric Data Pipelines scoped to business domains.
  • Isolate semantic models per use case.
  • Distribute ownership of reports across product teams.

Ideal when:

  • A workload is too entangled to move as-is.
  • You aim to decentralize for scale or compliance reasons.
  • Data products are reused across many reports or systems.

3. Flow​

Modernize and automate using Fabric-native orchestration and streaming.

Replace legacy scheduled jobs or hand-coded transformations with notebooks, pipelines, and real-time analytics in Fabric.

  • Rebuild batch pipelines using the Data Pipeline or Eventstream editor.
  • Introduce Spark notebooks for scalable transformation.
  • Incorporate Real-Time Analytics where latency is critical.

Ideal when:

  • You seek automation, self-healing data flows, and observability.
  • You want to combine streaming and batch for near real-time solutions.
  • Existing pipelines are brittle or outdated.

4. Flip​

Migrate workloads from legacy BI/warehouse tools to Fabric services.

This is the classic “modernization path” in the Fabric context. Replace SSIS, legacy DWH appliances, or on-prem SQL Server BI stacks with equivalent Fabric services:

  • SSIS → Data Pipelines
  • SSAS → Semantic Models
  • SQL DWH → Fabric Warehouse
  • Excel dashboards → Power BI

Ideal when:

  • Existing tools are nearing end-of-life.
  • Licensing or support costs need reduction.
  • Centralized modernization initiatives are under way.

5. Fade​

Decommission or consolidate workloads that are no longer relevant or duplicative.

Rationalization often reveals reports, queries, or pipelines that no longer deliver value. In Fabric, you can fade out those workloads:

  • Archive unused reports in OneLake.
  • Retire redundant pipelines or notebooks.
  • Redirect effort to high-impact data products.

Ideal when:

  • Reports or jobs show no usage or owner engagement.
  • Data assets are duplicated across domains.
  • Governance requires simplification.

Conclusion​

The Five Fs of Fabric Rationalization help you evaluate each asset’s future within the Fabric platform. They are not mutually exclusive—some workloads may combine elements of Fit, Flow, and Fade. The key is to guide each transformation decision based on business value, technical feasibility, and strategic alignment with Microsoft Fabric’s unified architecture.

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