Data & Analytics

SQL Server to Microsoft Fabric Data Warehouse Migration

Modernizing an enterprise SQL Server data warehouse with Microsoft Fabric while maintaining data quality, performance, security, and reporting continuity.

softreetechnology.com/case-studies
data-analytics
Security Assurance
Role, permission, and access validation
Code Continuity
Validated stored procedures and functions
Data Trust
Source-to-target reconciliation

Client Profile

An enterprise organization using Microsoft SQL Server as a core platform for operational reporting, data integration, and enterprise analytics. Softree proposed a structured migration approach to Microsoft Fabric Warehouse covering schema compatibility, data movement, procedural logic, performance, security, validation, and controlled cutover.

Use Cases
Data Warehouse Modernization, Data Migration, Analytics Modernization
Industry
Data & Analytics
Project Type
SQL Server to Microsoft Fabric Warehouse Migration
Scale of Operation
Enterprise data platform supporting operational reporting, data integration, enterprise analytics, and downstream reporting workloads.
End Users
Enterprise Reporting & Analytics Users
Service Provided
Microsoft Fabric MigrationData Warehouse ModernizationData EngineeringSQL Server MigrationPerformance OptimizationData Validation
The Client Challenge

Business Process Challenges

The enterprise organization relied on Microsoft SQL Server for operational reporting, data integration, and enterprise analytics. Over time, the environment had accumulated relational tables, views, stored procedures, user-defined functions, scheduled jobs, security rules, and downstream reporting dependencies, making a direct migration to Microsoft Fabric more complex than simply moving data. SQL Server to Microsoft Fabric …

The migration needed to preserve trusted data and predictable query behavior while addressing differences between SQL Server and Fabric Warehouse.

  • SQL Server estate complexity: The environment required a detailed inventory of schemas, tables, views, data types, constraints, indexes, stored procedures, functions, jobs, and dependencies before migration.
  • Schema and T-SQL compatibility: Existing SQL scripts and database objects required compatibility assessment because unsupported or behaviorally different constructs could require remediation before deployment.
  • Data migration planning: Data movement needed to account for table size, load patterns, incremental changes, source-system impact, and reconciliation requirements.
  • Procedural logic: Stored procedures and user-defined functions could require redesign or refactoring because SQL Server and Fabric Warehouse do not support every feature or execution pattern identically.
  • Performance considerations: Existing SQL Server indexing and tuning approaches could not simply be transferred to Fabric, requiring representative workload benchmarking and target-side optimization.
  • Security and cutover risk: Existing roles, permissions, identities, sensitive-data controls, acceptance criteria, rollback procedures, and post-migration monitoring needed to be addressed before production consumers were redirected.
Our Approach

Our Strategic Approach

Softree's approach focused on a structured, evidence-led migration from SQL Server to Microsoft Fabric Warehouse rather than treating the project as a simple database copy. The proposed architecture introduced controlled assessment, schema migration, data movement, code remediation, performance validation, security mapping, and phased cutover.

  • SQL Server assessment: The existing databases, schemas, workloads, dependencies, data volumes, schedules, security objects, and downstream consumers were assessed and prioritized according to business criticality, complexity, and operational risk.
  • Schema compatibility: Microsoft Fabric Migration Assistant for Data Warehouse was proposed to support schema migration and guided remediation, with additional review for incompatible scripts, dependencies, data types, constraints, and physical design.
  • Controlled data migration: Fabric Data Factory copy capabilities were used as the proposed data movement mechanism, with full, incremental, staged, or partition-aware approaches selected according to data volume, latency, and source-system constraints.
  • Data reconciliation: Source and target environments were compared using row counts, aggregates, key business measures, and selected record-level samples to provide evidence that migrated data remained consistent.
  • Code remediation: Stored procedures and functions were reviewed for SQL Server-specific syntax and behavior, with incompatible logic refactored into supported Fabric patterns while preserving validated business outcomes.
  • Performance optimization: Critical queries and workloads were benchmarked against agreed expectations, with target-side SQL, data design, concurrency, refresh windows, and Fabric capacity consumption evaluated before cutover.
  • Security and governance: Existing SQL Server users, roles, permissions, and sensitive-data controls were mapped to appropriate Microsoft Entra identities and Fabric security mechanisms using least-privilege principles.
  • Validation and cutover: Functional testing, source-to-target reconciliation, performance testing, security validation, business acceptance, rollback planning, and stabilization monitoring were incorporated into the migration process.
Our Solution Architecture

How we delivered it.

MSS
Microsoft SQL Server
Integrated Microsoft SQL Server layer in the solution architecture.
MFW
Microsoft Fabric Warehouse
Integrated Microsoft Fabric Warehouse layer in the solution architecture.
MFM
Microsoft Fabric Migration Assistant for Data Warehouse
Integrated Microsoft Fabric Migration Assistant for Data Warehouse layer in the solution architecture.
FDF
Fabric Data Factory
Integrated Fabric Data Factory layer in the solution architecture.
T
T-SQL
Integrated T-SQL layer in the solution architecture.
SDP
SQL Database Projects
Integrated SQL Database Projects layer in the solution architecture.
MEI
Microsoft Entra ID
Integrated Microsoft Entra ID layer in the solution architecture.
Visual Proof

Explore the Solution Through visuals

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The Outcome

What changed for the client.

The proposed migration establishes a structured foundation for moving enterprise SQL Server workloads into Microsoft Fabric Warehouse while maintaining focus on data trust, code continuity, performance visibility, security assurance, and operational risk management.

  • Migration readiness: A documented inventory, compatibility assessment, dependency map, and prioritized remediation plan provide clearer visibility into migration scope.
  • Data trust: Source-to-target reconciliation across row counts, aggregates, key measures, and selected records provides evidence for validating migrated data.
  • Code continuity: Stored procedures and functions can be validated against approved source behavior, with exceptions and intentional changes documented.
  • Performance visibility: Repeatable workload benchmarks provide measurable evidence of target query behavior, execution time, and Fabric capacity consumption rather than relying on assumptions.
  • Security assurance: Role and permission mapping, least-privilege access, and positive and negative access tests provide a structured basis for validating the target security model.
  • Lower migration risk: Phased deployment, acceptance criteria, cutover checkpoints, rollback planning, and post-migration monitoring create a controlled path for transitioning downstream consumers.
Results & Business Impact

The numbers behind the rollout.

01
Role, permission, and access validation
Security Assurance
02
Validated stored procedures and functions
Code Continuity
03
Source-to-target reconciliation
Data Trust
Reference Tech Stack

The full integration layer.

Microsoft SQL Server
Microsoft Fabric Warehouse
Microsoft Fabric Migration Assistant for Data Warehouse
Fabric Data Factory
T-SQL
SQL Database Projects
Microsoft Entra ID
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Frequently asked questions.

Softree delivers custom solutions across AI and automation, Power Platform, SharePoint customization, full-stack web and SaaS engineering, and data analytics.
We combine modern software engineering standards, secure cloud configurations, pre-built accelerators, and agile delivery methodologies to produce governed, scalable applications.
Our agile delivery model typically produces scoped initial MVPs in 4 to 8 weeks, with comprehensive enterprise deployments completed in 10 to 12 weeks.
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