https://store-images.s-microsoft.com/image/apps.46764.f17715bb-dc67-4ca2-89ec-86778403c60c.c7a28561-0b99-45c8-9325-3d54854f71f6.2d32034e-e5b9-478e-b9f6-3ca0e7be0725

Migrate Power BI to Microsoft Fabric

Quadrant Technologies

Current Power BI customers need to migrate their workspace to Microsoft Fabric as part of onboarding process, Quadrant assists in migration and establishing Fabric landing zone effectively.



Migrating from Power BI to Microsoft Fabric

Quadrant Managed Services

Migration from Power BI to Microsoft Fabric unifies datasets, reports, and workspaces under a centralized platform that leverages Azure Cloud Services. Fabric enhances analytics capabilities through real-time data processing, advanced AI integration, and seamless collaboration, aligning with Azure's promise of scalability, performance, and security.


Azure Cloud Services Value Proposition

1. Unified Data Management:
Fabric, supported by Azure Synapse and Azure Data Factory, enables cohesive data handling, ensuring all data assets are accessible for collaboration and decision-making.

2. Real-Time Insights:
Azure Stream Analytics and Fabric’s real-time analytics offer instant access to actionable data, accelerating business outcomes.

3. Scalability and Performance:
Azure’s global data centers and Fabric’s capacity management handle large-scale operations efficiently, ensuring a cost-effective and scalable solution.

4. Security and Governance:
Azure Purview and Microsoft Defender ensure enterprise-grade compliance, robust data security, and automated governance during and after migration.

5. Advanced Analytics and AI Integration:
Azure’s AI and Fabric’s Co-Pilot simplify predictive modeling and machine learning, fostering innovation.


Benefits of Migrating to Fabric

  1. Seamless Transition: Unified workspace reassignment reduces complexity.
  2. Enhanced Collaboration: Teams benefit from a platform designed for integrated BI, data engineering, and analytics.
  3. Cost Optimization: Fabric’s pay-as-you-go model, coupled with Azure credits, ensures financial efficiency.
  4. AI-Powered Insights: Advanced analytics tools in Azure enhance data intelligence.

Migration Considerations

  1. Data Sources: Ensure compatibility with Fabric’s Lakehouse/Warehouse.
  2. Access Controls: Replicate RLS and RBAC to maintain security.
  3. Data Models: Align data transformations with Azure Data Factory workflows.
  4. Reports: Redesign for Fabric’s advanced reporting capabilities.
  5. Testing and Validation: Use Azure Monitor for performance and accuracy checks.

Steps for Migration

1. Pre-Migration Assessment

  • Analyze dependencies using Azure Migrate.
  • Prepare users and identify impacted workflows.

2. Workspace Reassignment

  • Reassign workspaces to Fabric capacity via the Admin Portal or REST API.
  • Confirm reassignments using Fabric monitoring tools.

3. Data Migration

  • Transfer datasets to Fabric using Azure Data Factory pipelines.
  • Configure Lakehouse/Warehouse tables for enhanced performance.

4. Report Migration

  • Migrate reports and reconfigure using Co-Pilot for template generation.

5. Testing

  • Validate migrated data with Azure Monitor and Application Insights.

6. User Training

  • Conduct workshops on Fabric’s features, emphasizing its integration with Azure.

Real-World Use Cases

  1. Retail Demand Forecasting:
    Real-time sales data processed through Azure Stream Analytics improves inventory management.

  2. Financial Fraud Detection:
    Azure Synapse and Fabric enhance fraud detection with predictive analytics and real-time alerts.

  3. Healthcare Monitoring:
    Azure Health Data Services integrate patient data for proactive care insights.

  4. Supply Chain Optimization:
    IoT data from Azure IoT Hub is analyzed for better logistics planning.

  5. Marketing Performance Analysis:
    Azure Cognitive Services centralize campaign analytics for real-time adjustments.


At a glance

https://store-images.s-microsoft.com/image/apps.43481.f17715bb-dc67-4ca2-89ec-86778403c60c.c7a28561-0b99-45c8-9325-3d54854f71f6.31c9d2dd-9a39-482e-bcaf-6c4da20194d1