Migration from Synapse to Microsoft Fabric: Strategy and Steps

Do you work as a C-suite leader, or as a data strategy leader struggling with the intricacies of your existing analytics infrastructure? Your organization may have invested a significant amount in Azure Synapse Analytics, and now you are experiencing the pain of managing different services and scaling resources across the data engineering team, warehousing teams, and BI teams.

You’re not alone. The cloud data environment is never static, and the necessity to simplify operations and speed up the time-to-insight is contributing to a significant strategic change.

In this environment, many enterprises using Synapse are realizing that to achieve truly unified, AI-driven analytics, they must prepare for a critical transition. Microsoft’s introduction of Fabric represents not just an incremental update, but a fundamental reimagining of the data stack.

This paradigm shift requires a strategic approach, moving past the perceived safety of PaaS infrastructure toward the streamlined power of a Software-as-a-Service (SaaS) data platform.

In this blog, we will conduct a detailed analysis of the shift from Azure Synapse to Microsoft Fabric, outlining the strategic benefits, comparing the technical architectures, providing a step-by-step Synapse migration plan, and sharing best practices to ensure a smooth transition.

Why Organizations are Migrating to Microsoft Fabric?

The migrating decision is essentially a business decision that is initiated to get maximum efficiency, ease, and strategic advantage. Although Azure Synapse Analytics offers powerful services, its PaaS (Platform-as-a-Service) architecture has your teams dealing with infrastructure components such as SQL pools, Spark clusters, and integration runtimes. Microsoft Fabric alters this by providing an integrated SaaS.

1. Unified Experience

Fabric combines all of the underlying workloads of analytics, such as Data Engineering, Data Factory (integration), Data Warehouse, Data Science, Real-Time Analytics, and Power BI into one product. This merger saves an immense amount of friction, as your data engineers will not have to compile individual services. All components work seamlessly on a single copy of data stored in OneLake.

2. Cost Optimization And Scalability Improvements

Fabric’s unified capacity model eliminates resource waste caused by over-provisioning siloed services. Unused compute automatically shifts between workloads, leading to more predictable budgeting and significant cost optimization.

A Forrester Total Economic Impact study projects that organizations deploying Microsoft Fabric are likely to realize an impressive 379% Return on Investment over three years, primarily through increased productivity and infrastructure cost savings.

3. Better Data Governance With Purview Integration and a Single Data Copy Model

At the core of the Microsoft Fabric architecture is OneLake, the single, cloud-native data lake for your entire organization. Since all data resides in one place (in open Delta Lake format), security and governance are centralized. Purview is natively integrated, providing unified lineage tracking and compliance monitoring without complex configurations.

4. Built-In Copilot and AI-Driven Insights

Fabric embeds AI functionality throughout the platform. This lowers the skill barrier, accelerates experts, and rapidly shortens the time-to-insight for your organization.

5. Key Business Outcomes

By standardizing on Fabric, organizations see quantifiable improvements. Data engineers, for instance, can slash the time spent searching, integrating, and debugging data by up to 90%. This massive productivity gain translates directly to faster project delivery and a lower Total Cost of Ownership (TCO).

Technical Architecture Comparison – Synapse vs Microsoft Fabric

Understanding the architectural shift is vital for planning your Synapse Analytics to Fabric transition. The move is essentially from a collection of interconnected PaaS services to a holistic SaaS platform centered around a single, universal data lake.

Step-by-Step Migration Plan from Synapse to Microsoft Fabric

A successful Microsoft Fabric migration is not a “lift and shift” but a strategic re-platforming exercise. We recommend breaking down the process into five distinct phases, using a phased approach for maximum control.

1. Assessment and Planning

The first step determines the scope of the migration from Synapse to Microsoft Fabric.

• Inventory Synapse assets: Document every active component: pipelines (Azure Data Factory linked services), datasets, dedicated and serverless SQL pools, Spark notebooks, and Power BI connections.
• Evaluate compatibility with Fabric workloads: Map each asset to its Fabric counterpart (e.g., Synapse SQL Pool -> Fabric Warehouse, Synapse Pipeline -> Fabric Data Factory). Pay special attention to identifying unsupported features like Mapping Data Flows and proprietary T-SQL syntax.
• Establish migration goals (performance, cost, consolidation): Define measurable success criteria aligned with your executive mandate.

2. Data Lake Reconfiguration

Fabric’s single-copy architecture relies on OneLake. This phase focuses on data readiness.

• Map Synapse Data Lake to OneLake: Use OneLake Shortcuts to create virtual access to your existing ADLS Gen2 data without physically moving it initially. This provides a bridge between the two platforms.
• Migrate Parquet/Delta tables for Fabric compatibility: Fabric defaults to the Delta Lake format. Ensure your Parquet data is converted or wrapped in Delta tables, ensuring proper structure for the Fabric Lakehouse.
• Handle schema evolution and data lineage: Establish a clear data governance plan to manage schema changes, ensuring consistency across the unified Lakehouse.

3. Pipeline Migration

Transitioning your data ingestion and orchestration layers.

• Convert Synapse Pipelines to Fabric Data Factory: Recreate orchestration pipelines using the new Fabric Data Factory interface.
• Leverage Fabric Copy Data Tool for automated migration: Use the Copy Data activity for high-volume data movement from Synapse SQL pools or external sources into the Fabric Lakehouse.
• Validate triggers, parameters, and linked services: Ensure all scheduled triggers, pipeline parameters, and connection strings are correctly configured in the new Fabric environment.

4. Validation & Optimization

The critical phase of quality assurance and tuning.

• Validate row counts, schema, and job performance: Run comparative queries between the Synapse and Fabric environments to ensure data integrity and accuracy. Performance check is crucial to meet or exceed previous SLAs.
• Optimize workspace configuration (capacities, security, cost management): Fine-tune your Fabric Capacity usage based on actual workload demands. Implement workspace roles and granular permissions to ensure security and compliance.

5. Enable Copilot & Governance

Ensuring the platform is optimized for modern governance and AI features.

• Activate Copilot in Fabric for query generation, summaries, and insights: Train users on leveraging AI for complex tasks.
• Configure Purview for lineage tracking and compliance: Complete the governance loop by ensuring data sensitivity labels and end-to-end lineage tracking are active and enforced.

Common Migration Challenges and How to Overcome Them?

Discover the most frequent migration hurdles and practical strategies to ensure a smooth, successful transition.

1. Schema Compatibility and Performance Tuning

Synapse dedicated SQL pools and Fabric’s Warehouse/Lakehouse handle data slightly differently. You may encounter issues with unsupported T-SQL commands or complex data types.

• Solution: Conduct a comprehensive T-SQL audit. For performance tuning, utilize Fabric’s Query Insights feature to optimize poorly performing queries and leverage the Direct Lake mode for immediate reporting on massive data sets.

2. Authentication And Access Model Differences Between Synapse And Fabric

Synapse access was managed primarily through Azure-level roles and ADLS Gen2 permissions. Fabric utilizes a Power BI-centric, workspace-based model with OneLake security.

• Solution: Treat the migration as an opportunity to simplify. Define new workspace roles (Admin, Member, Contributor) tailored to the persona-specific experiences (e.g., Microsoft Fabric Data Engineering vs. Data Science) and leverage the simplified security inheritance in OneLake.

3. Copilot Permissions And Data Governance Pitfalls

While powerful, Copilot’s effectiveness depends on the quality and governance of the underlying data. Without clear lineage and sensitivity labels, AI insights can be misleading or expose confidential information.

• Solution: Implement robust Purview governance before extensive Copilot rollout. Ensure all semantic models and Lakehouse tables are certified and endorsed to provide a trusted foundation for AI-driven insights.

Tips For Validation And Rollback Planning

The biggest pitfall is treating the shift as a simple “Lift and Shift.” Plan for validation at every stage.

• Checklist: Before cutting over, perform a comprehensive validation: confirm Data Accuracy (row counts, aggregates), Job Runtime (ensure Fabric performance meets SLAs), and Capacity Optimization (check compute usage against budget).
• Rollback: Maintain your Synapse environment in a read-only state for a defined period post-cutover. This “dark mode” allows for immediate rollback if critical issues are found.

Best Practices for a Seamless Synapse-to-Fabric Transition

To maximize the benefits of your Microsoft Fabric migration, adopt these strategic best practices that focus on efficiency and scalability.

1. Use Fabric Migration Assessment Tools And Templates

Microsoft provides tools within the Fabric portal and external resources to assess your current Synapse environment and gauge the complexity of the move. Leverage these checklists and templates to standardize your approach and identify specific technical debt early in your Synapse migration steps.

2. Implement Ci/Cd And Devops With Fabric Git Integration

Treat the migration as a software development project. Use the native Git integration in Fabric workspaces to connect to Azure DevOps or GitHub. This ensures all notebooks, dataflows, and semantic models are version-controlled, enabling robust testing, automated deployment, and seamless rollback capabilities.

3. Engage With Microsoft Fabric Consulting Partners

Given the architectural change and potential for complex code re-engineering, partnering with specialists can accelerate adoption and de-risk the project. Specialized Microsoft Fabric migration consultants can provide expertise on capacity planning, T-SQL re-platforming, and governance best practices. Folio3 Azure team is the perfect fit for migration.

4. Using Fabric’s Workspace Roles And Granular Permissions

Avoid replicating old security models. Use Fabric’s simplified workspace roles, combined with Row-Level Security (RLS) and Column-Level Security (CLS) in your Warehouse and semantic models, to manage data access efficiently and enforce compliance.

Conclusion

The decision to execute a migration from Synapse to Microsoft Fabric is a transformative strategic move. It is about more than just switching platforms; it’s about embracing a modern, unified, and AI-powered data architecture that is poised for the next decade of analytics.

By consolidating your data ecosystem onto OneLake and leveraging the power of a single SaaS solution, you can unlock massive operational savings and empower your organization with unparalleled speed and collaboration.

If your organization is ready to start its journey toward Microsoft Fabric migration and requires a trusted partner to navigate the technical complexities, from capacity planning to T-SQL re-platforming, consider engaging with Folio3 Azure Team for expert consulting and implementation services. Secure your future of AI-driven analytics today.