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Define and prioritize workloads for a Microsoft Fabric adoption plan

Last updated: 2025-07-07

Establishing clear, actionable priorities is one of the keys to successful Microsoft Fabric adoption. It might be tempting to define all datasets, pipelines, Power BI reports, Lakehouses, and other Fabric artifacts that could be impacted. However, exhaustive early planning often stalls progress and limits learning.

We recommend your team prioritize and document the first 10 Fabric workloads. After implementation begins, the team should maintain a backlog of the next 10 highest-priority workloads. This agile approach enables responsive planning, continuous learning, and better alignment with business value as priorities evolve.

What is a Fabric workload?​

In Microsoft Fabric, a workload refers to a set of interconnected Fabric artifacts—like Data Pipelines, Lakehouses, Notebooks, Semantic Models, Reports, Real-Time Intelligence components, or Eventstreams—that support a specific business process or analytical domain. A workload may be self-contained or depend on other Fabric items (e.g., shared Lakehouses or Warehouses).

Prerequisites​

Before prioritizing workloads, ensure you’ve completed the strategic inputs from the prerequisites stage:

  • Defined motivations for Fabric adoption (e.g., centralized analytics, cost savings, real-time insights)
  • Aligned on business outcomes and justification
  • Identified key business sponsors and stakeholder groups

The Power of 10: Initial workload prioritization​

Use the Power of 10 approach to focus your initial Fabric efforts:

  • Select a mix of simple and complex workloads
    • Simple: e.g., a Power BI dashboard with a small semantic model
    • Complex: e.g., an integrated data platform with Eventstreams, Lakehouse, Synapse Data Warehouse, and Data Activator
  • Prioritize based on business impact, data availability, user demand, and alignment with Fabric capabilities

This method provides early wins and supports rapid iteration.

Add Fabric workloads to your adoption plan​

Once your workloads are prioritized, document them in a structured backlog. If you're using Azure DevOps or GitHub Projects, tag each Fabric workload and its components (e.g., pipelines, notebooks, datasets) with identifiers like:

  • workload-fabric:MarketingDashboard
  • workload-fabric:SupplyChainAnalytics

Track associated tasks like onboarding users to Microsoft Fabric, provisioning capacities, setting up CI/CD pipelines for notebooks or reports, or implementing data governance with Microsoft Purview.

Define each workload​

Before implementation, define each prioritized workload using both business and technical data points.

Business Inputs​

Data pointDescription
Workload nameShort, descriptive name
Workload descriptionWhat does this workload deliver?
Adoption motivationsE.g., "centralized reporting", "real-time alerting", "self-service BI"
Primary sponsorBusiness owner or executive champion
Business impactQualitative and quantitative value (e.g., cost savings, efficiency, insight latency)
Application/Data impactWhich existing apps or datasets are affected?
Business unitWho owns this workload?
SustainabilityAny green IT targets (e.g., capacity pausing, resource reuse)?
GeographiesWhere are users or data located?
Outcomes & KPIsHow will success be measured?

Technical Inputs​

Data pointDescription
Adoption approachMigrate existing assets vs. build native Fabric workloads
Workload typeReporting, Data Engineering, Real-time Analytics, etc.
Fabric items involvedLakehouses, Pipelines, Eventstreams, Power BI, etc.
Capacity usageExpected size, peak loads, pausing schedules
StorageLakehouse size, OneLake location, Azure Storage Shortcuts
DependenciesExternal APIs, databases, filesystems
SLAs & criticalityUptime, freshness, security classification
Security/complianceEntra ID setup, sensitivity labels, region constraints

Confirm priorities​

Review your prioritized Fabric workloads regularly with both business and technical stakeholders. Use the following criteria:

  • Business value and urgency
  • Fabric readiness (licenses, capacity setup, skill readiness)
  • Dependencies or blockers
  • Opportunity for early wins and learning

Document and lock in the initial Fabric adoption backlog based on confirmed and aligned priorities.

Next steps​

Move forward with implementation planning:

  1. Define Fabric environment setup and governance
  2. Schedule capacity provisioning and workload onboarding
  3. Establish DevOps and deployment practices
  4. Assign ownership and define success criteria per workload

For each workload, ensure monitoring, observability, and FinOps tracking are in place to support iterative scaling and optimization.

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