Power BI to Microsoft Fabric Migration | LevelShift

As organizations move toward unified, AI-ready analytics, migrating from Power BI to Microsoft Fabric ensures Datamarts, Dataflows, and workspaces evolve into a modern platform built for scale, performance, and long-term compatibility with Microsoft’s roadmap.

Why Migrate Power BI to Fabric

• Unified Architecture: Consolidate datasets, models, and pipelines into OneLake with Fabric’s integrated workloads.

• Lower Costs: Retire fragmented components and benefit from Fabric’s flexible consumption model.

• Enhanced Self-Service: Give business teams faster, more interactive reporting with governed access.

• AI-Driven Insights: Unlock Copilot, advanced data engineering, and built-in machine learning capabilities.

• Performance Gains: Achieve faster processing, optimized pipelines, and more reliable refresh cycles.

Levelshift’s Key Migration Pillars (*6-8 weeks)

• Pre-Migration Assessment: Conduct a thorough assessment of your current Power BI environment, including dependencies, data sources, and user workflows. Identify potential challenges and plan effectively.

• Change Management: Implement strategies to ensure a smooth transition, including user training, communication of benefits, and addressing resistance to change.

• Data Compatibility: Ensure your data sources align with Microsoft Fabric, transform models, and redesign reports to utilize Fabric’s advanced capabilities.

• Workspace Reassignment: Optimize workspace assignments within Fabric capacity through manual or automated migration tools.

• Testing & Validation: Leverage Azure Monitor and other tools to test performance, validate accuracy, and ensure functionality post-migration.

• Security & Governance: Maintain robust security with role-based access controls (RBAC) and governance policies for compliance and data protection.

LevelShift brings hands-on experience from 10+ successful Power BI to Fabric migrations, supported by proven playbooks, templates, and accelerators that reduce risk and shorten timelines.

*The typical implementation duration is 4–6 weeks. Actual timeline and cost may vary based on the existing data architecture, data volume, and scope of work (SOW).