At Microsoft Ignite and recent Fabric updates, Microsoft made it clear that Fabric is becoming the unified layer for data and analytics across its ecosystem. This has prompted many enterprises to reassess how their existing Azure services fit together. As a result, Azure to Microsoft Fabric migration is gaining momentum among teams looking to simplify fragmented architectures and reduce integration overhead.
Most organizations running Azure Data Factory, Synapse, and Analysis Services face the same problem: these services do not connect naturally. Data teams often spend more time managing integrations than analyzing data. A Forrester Consulting study found that Microsoft Fabric delivers 379% ROI over three years, along with a 25% increase in data engineering productivity, highlighting the value of consolidation.
In this article, we’ll cover why enterprises are moving from Azure Data Factory to Fabric, what makes manual ADF migration so difficult, how to prepare before you start, and how automated tooling changes the timeline and cost.
Key Takeaways Microsoft Fabric consolidates ADF, Synapse, and Analysis Services into one platform, eliminating the need to manage separate data integration and warehousing tools Manual Azure Data Factory to Fabric migration typically takes 6 to 12 months and requires rebuilding every pipeline from scratch due to JSON structure differences Pre-migration assessment of your pipeline inventory, data sources, and governance policies directly determines how smooth the cutover goes Kanerika’s FLIP Migration Accelerator reduces migration timelines to 4 to 8 weeks with 40 to 60% lower costs compared to manual approachesOrganizations that automated their ADF to Fabric migration with FLIP maintained zero downtime and preserved all business logic during cutover
Why Migrate from Azure Data Factory & Synapse to Microsoft Fabric? 1. Unified Data Analytics Platform Microsoft Fabric brings data integration, warehousing, engineering, and business intelligence into a single environment. Teams can complete an entire analytics workflow, from ingestion through reporting, within one platform. This removes the overhead of managing separate data pipeline tools across ADF, Synapse, and Power BI.
2. Enhanced Performance and Scalability Fabric’s distributed architecture is less sensitive to load spikes and high concurrency than traditional ADF setups. Consolidating to larger Fabric capacity SKUs improves throughput across data processing workloads. The cloud-native SaaS model scales automatically, removing the infrastructure management that ADF requires during peak processing cycles.
3. Simplified Architecture and Reduced Complexity Fabric removes several ADF constructs that add overhead. Datasets are gone, with data properties defined inline within activities. Linked Services are replaced by Connections scoped to individual activities. Integration runtime configurations are handled automatically for most workloads, which means fewer objects to manage and lower operational complexity across your data engineering stack.
4. Built-in CI/CD and Developer Experience Fabric’s CI/CD capability works independently of ARM templates and external Git configuration. Developers can promote changes across environments, duplicate pipelines in seconds with the Save As feature, and work within a faster, more intuitive interface than ADF. The development cycle for new data pipelines is meaningfully shorter.
5. Cost Optimization Through OneLake OneLake, Fabric’s centralized data lake , removes the need for separate storage systems and reduces data movement costs. All Fabric services share OneLake automatically without additional configuration. Combined with eliminating duplicate services previously spread across ADF, Synapse, and SSAS, the cost reduction from cloud data platform consolidation is significant.
6. Real-Time Intelligence and AI-Ready Analytics Fabric’s Real-Time Intelligence capabilities allow organizations to ingest, process, query, and act on time-sensitive data without batch processing delays. Copilot for Power BI and Copilot for Notebooks bring AI-assisted analytics directly into the workflow, making Fabric the more capable platform for organizations building toward AI-driven data operations.
Challenges of Manual Azure to Microsoft Fabric Migration 1. Complex Pipeline Reconstruction Requirements Fabric and ADF use different JSON structures for pipeline definitions. ADF pipeline JSON is incompatible with Fabric Data Factory, so direct imports are off the table. Every pipeline must be rebuilt from scratch in Fabric, which means months of development effort for enterprise environments running hundreds of pipelines. Teams must understand both platforms’ design patterns simultaneously.
2. Integration Runtime Conversion Complexity Self-hosted integration runtimes (SHIRs) in ADF must be recreated as on-premises data gateways (OPDGs) in Fabric. VNet-enabled Azure IRs convert to Virtual Network Data Gateways. Each conversion requires reconfiguring network connectivity and security settings manually, as the two environments share no automated conversion path.
3. Data Connectivity Migration Issues ADF’s Linked Services and Datasets are constructs Fabric has moved away from entirely. Every data connection must be recreated within individual activities rather than managed centrally. This architectural shift means each activity needs its own connection configuration, which increases the risk of inconsistencies across large pipeline inventories.
4. Testing and Validation Overhead Migrated Synapse and ADF pipelines must produce identical outputs to their source counterparts. That requires thorough end-to-end testing across data accuracy , error handling, and performance metrics for every workflow. For large deployments, this validation phase alone can take several months before production cutover is safe.
5. Dataflow Transformation Challenges ADF data flows use a different execution engine and transformation language than Fabric’s Power Query-based dataflows. Manual ETL pipeline migration requires rewriting transformation logic from the ground up. Teams must learn Power Query while simultaneously reverse-engineering the ADF logic they’re replacing, which creates a significant learning curve under delivery pressure.
Pre-Migration Checklist: What to Do Before You Start? The quality of your preparation directly determines how smooth your ADF to Fabric migration goes. Rushing the assessment phase is the most common reason migrations run over time and budget. Before any conversion work begins, work through these five steps.
Inventory your full Azure environment: catalog every ADF pipeline, Synapse workspace, SSIS package, linked service, and integration runtime. Know the scope before you start, because missing a dependency mid-migration is expensive. Classify pipelines by complexity: separate simple copy pipelines from those with custom activities, stored procedures, or complex transformation logic. Complexity tier drives effort estimates and sequencing decisions. Map all data sources and connections: document every source system, connection string, and credential configuration. Recreating these in Fabric activities requires a complete map covering even the less obvious ones. Review your governance and access policies: Fabric uses a different workspace and permission model than ADF. Define your target workspace structure, role assignments, and capacity allocation before migration begins. Identify what stays on ADF temporarily: SSIS packages and certain ADF connectors have limited Fabric parity right now. Decide upfront which workloads will run in parallel while the rest migrate, and for how long.
Use Kanerika’s Migration ROI Calculator to estimate your cost and time savings before you commit to a migration approach.
How Kanerika’s FLIP Migration Accelerator Automates Azure to Microsoft Fabric Migration Kanerika’s FLIP Migration Accelerator automates the conversion of Azure Data Factory and Synapse pipelines into Fabric-native workflows. FLIP is listed on the Azure Marketplace and eligible for Azure Committed Spend (MACC). It reduces manual migration effort by up to 75%, compressing timelines from months to weeks while preserving business logic throughout.
1. Pipeline Architecture Assessment and Discovery FLIP begins by cataloguing your complete Azure data infrastructure , covering every ADF pipeline, Synapse workspace, linked service, trigger, and integration runtime, and packages all assets with full dependencies for conversion. This automated inventory replaces the manual audit phase that typically takes weeks in traditional data migration projects.
What gets analyzed:
All existing pipeline dependencies, triggers, and scheduling configurations across your Azure environment Data source connections, linked services, and integration runtime configurations that need migration Custom activities, stored procedures, and transformation logic that require conversion planning
2. Activity Conversion and Fabric Optimization FLIP automatically converts ADF activities into their Fabric Data Factory equivalents. ADF copy activities map to Fabric pipelines with optimized performance settings. Synapse notebooks and Spark jobs convert to Fabric notebooks with minimal code changes. Transformation logic adapts to Fabric’s execution environment without losing functionality.
How the conversion works:
ADF copy activities get mapped to Fabric data pipelines with optimized performance settings Synapse notebooks and Spark jobs convert to Fabric notebooks with minimal code changes required Custom scripts and transformation logic adapt to Fabric’s execution environment without losing functionality
3. Integration Mapping and Workspace Configuration Your Azure resources get organized into Fabric workspaces aligned with your team structure and data governance policies. FLIP handles connection string updates, service principal configurations, and data gateway setup for on-premises sources automatically. This removes the manual configuration work that accounts for a significant portion of migration project hours.
Configuration includes:
Workspace creation with proper role assignments and capacity allocation for different teams Connection string updates and service principal configurations for secure data access Data gateway setup for on-premises sources and existing Azure SQL databases
4. Validation Testing and Performance Optimization Before production cutover, FLIP runs end-to-end pipeline execution tests with actual data, benchmarks performance against Azure baselines, and validates data lineage to confirm all relationships between datasets remain intact. Your existing ADF and Synapse pipelines continue running in parallel throughout, so production workloads stay active while Fabric pipelines are being validated.
Testing covers:
Performance benchmarking against Azure baselines to identify optimization opportunities in Fabric End-to-end pipeline execution tests with actual data to verify output accuracy Data lineage validation to confirm relationships between datasets remain intact after migration
Manual vs. Automated Azure to Microsoft Fabric Migration Aspect Manual Migration Automated Migration (FLIP) Timeline 6 to 12 months or more 4 to 8 weeks from assessment to production Resource Requirements Full-time dedicated data engineering team Small team with automated conversion Pipeline Conversion Rebuild every pipeline from scratch Automated activity mapping preserves business logic Integration Runtime Setup Manual reconfiguration of network and security Automated gateway conversion with preserved connectivity Data Connection Management Recreate each connection individually per activity Automated connection string updates across all pipelines Testing Scope Months of manual validation Automated end-to-end testing with parallel environments Error Risk High chance of configuration inconsistencies Built-in validation checks ensure accuracy Cost Full project budget with potential overruns 40% to 60% cost savings Dataflow Conversion Rewrite transformation logic in Power Query manually Automated conversion maintains original logic Business Continuity Potential downtime during cutover Zero downtime with parallel testing environment
ADF to Fabric Feature Parity: Know Your Migration Scope Before You Start Microsoft categorizes every ADF asset into four readiness states before migration: Ready, Needs Review, Coming Soon, and Unsupported. Knowing which bucket your pipelines fall into determines your timeline, your risk, and how you sequence the work.
1. Pipelines That Convert Directly to Fabric Standard copy activities, basic pipeline orchestration, most connectors, Synapse notebooks, Spark jobs, and simple scheduling triggers all have direct Fabric equivalents. Linked Services map to activity-level Connections in Fabric. These are safe to prioritize first and are well-handled by both Microsoft’s native tooling and FLIP’s automated conversion.
2. Metadata-Driven Pipelines That Need Pre-Migration Restructuring Metadata-driven pipelines with dynamically parameterized connections need restructuring ahead of migration. Each parameter permutation requires its own Fabric connection rather than a single dynamic linked service. These pipelines are migratable, but they need engineering review before the automated conversion runs.
3. SSIS, Data Flows, and Triggers That Require a Dedicated Plan SSIS integration runtimes : have no direct Fabric migration path. The interim approach is invoking ADF pipelines from Fabric for SSIS execution while progressively converting packages to Fabric-native solutions.Mapping data flows : are listed as coming soon in Fabric. Teams with significant investment here should convert to Dataflow Gen2 or Spark notebooks in the meantime.Tumbling window triggers : use dependency chaining and backfill semantics that Fabric’s interval-based scheduling handles differently. These require a redesign rather than a direct conversion.Managed VNet integration runtimes : need reconfiguration as Virtual Network Data Gateways in Fabric. Security posture carries over, but the setup is manual and should be flagged during the pre-migration assessment.
4. Running Microsoft’s Built-In ADF Migration Assessment In March 2026, Microsoft released a built-in migration experience inside the ADF authoring portal. It scans every pipeline, assigns a readiness status, and exports a full CSV report before anything moves. Pipelines migrate with triggers disabled by default, so your team controls when production workloads cut over.
Running this assessment first gives you a clear scope. Layering FLIP on top for bulk conversion gives you the speed to act on it.
Why Choose Kanerika for Azure to Microsoft Fabric Migration Services Proven Enterprise Data Platform Modernization Expertise As a Microsoft Fabric Featured Partner and Microsoft Solutions Partner for Data and AI, Kanerika specializes in transitioning legacy Azure workloads to Microsoft Fabric. We handle ADF pipeline migration, Synapse data warehouse migration , and full ETL modernization for enterprises across manufacturing, retail, financial services, and logistics.
FLIP automates the heavy lifting so your team focuses on validation rather than manual rebuilding. It has passed Microsoft’s technical and compliance requirements and is available on the Azure Marketplace with MACC eligibility.
Automated Migration That Preserves What Matters FLIP converts complex ADF pipelines and Synapse dataflows into fully functional Fabric workflows in days, not months. Business logic, data connections, transformation rules, and scheduling configurations are preserved throughout. The tool handles repetitive conversion tasks, updating connection strings, reconfiguring triggers, and adapting dataset schemas, so your engineers can stay focused on higher-value work.
Measurable Results Across Industries Across ADF, Synapse, and legacy ETL migrations , FLIP reduces manual migration effort by 75% on average. In manufacturing and retail, organizations report 40 to 60% reductions in cloud infrastructure costs post-migration. In financial services and logistics, the shift to Fabric’s unified platform cuts data engineering maintenance overhead, freeing teams to focus on analytics and AI initiatives rather than pipeline upkeep.
Case Study: ADF to Fabric Migration for a Global Packaging Leader A global leader in packaging solutions for food, industrial, and e-commerce applications partnered with Kanerika to migrate from Azure Data Factory and Synapse to Microsoft Fabric. Fragmented workflows, a failing Parquet conversion layer, and inconsistent governance were slowing down analytics and inflating cloud costs .
Challenges
Solutions Migrated all Azure assets to Microsoft Fabric using FLIP, maintaining full code integrity throughout the transition Enabled direct SAP C4C to Fabric integration , removing the redundant Parquet processing layer and improving data flow reliability Established a unified governance framework covering naming conventions, version control, and documentation
Results 30% reduction in cloud and data costs 50% improvement in data pipeline performance 80% faster business insights and reporting Migration completed with zero disruption to ongoing operations
Conclusion Azure to Microsoft Fabric migration is one of the more consequential infrastructure decisions a data team can make in 2026. The platform consolidation benefits are real: unified analytics, lower operational overhead, better CI/CD, and a direct path to AI-ready data pipelines. Manual migration from Azure Data Factory or Synapse is slow, error-prone, and resource-intensive, which is why automated tooling changes the equation entirely.
The organizations getting the most out of this transition are the ones that prepared thoroughly before starting. They inventoried their pipelines, mapped dependencies, and defined their Fabric workspace structure upfront. They also used automated tooling to handle conversion work rather than rebuilding hundreds of pipelines by hand.
Kanerika’s FLIP Migration Accelerator handles the technical complexity so your team can focus on what actually matters: using the platform. If you’re evaluating an ADF to Fabric migration, start with the assessment. The scope you find there will shape everything else.
FAQs Is the Azure Data Factory going away? Azure Data Factory is not going away. Microsoft continues to support and develop ADF as a standalone service, though the company is heavily investing in Microsoft Fabric as its unified analytics platform. Many enterprises still rely on ADF for data integration and orchestration workloads. However, Microsoft encourages organizations to consider Fabric for new projects and future migrations to leverage consolidated capabilities. Planning your Azure to Fabric migration strategy now positions your team ahead of the curve. Kanerika helps enterprises evaluate their ADF environment and create migration roadmaps tailored to business priorities.
Will Fabric replace Azure? Fabric will not replace Azure entirely but consolidates multiple Azure analytics services into one unified platform. Microsoft Fabric integrates capabilities from Azure Data Factory, Synapse Analytics, Power BI, and Data Lake under a single SaaS experience. Azure remains the broader cloud infrastructure, while Fabric focuses specifically on analytics and data workloads. Organizations migrating from Azure to Microsoft Fabric gain streamlined governance, simplified licensing, and unified data management without abandoning their Azure investments. Kanerika’s migration specialists help enterprises navigate this transition while preserving existing Azure infrastructure investments.
Why migrate Azure Data Factory to Fabric? Migrating Azure Data Factory to Fabric delivers unified analytics, simplified licensing, and native integration with Power BI, Data Lake, and real-time analytics in one platform. Organizations eliminate the complexity of managing multiple Azure services separately while gaining consolidated governance and cost visibility. Fabric’s OneLake architecture removes data silos, enabling faster insights across the enterprise. The migration also future-proofs your data platform as Microsoft continues investing heavily in Fabric capabilities. Kanerika’s ADF to Fabric migration accelerator reduces transition time by up to sixty percent—schedule a consultation to explore your modernization options.
Is Microsoft Fabric better than Azure Data Factory? Microsoft Fabric offers broader capabilities than Azure Data Factory alone by combining data integration, warehousing, real-time analytics, and business intelligence in one platform. ADF excels at ETL orchestration, but Fabric extends this with native Lakehouse architecture, Power BI integration, and unified governance through OneLake. For organizations seeking end-to-end analytics consolidation, Fabric provides significant advantages. However, ADF remains effective for specific integration-focused workloads. The right choice depends on your data strategy and modernization goals. Kanerika’s assessment helps enterprises compare both platforms against their unique requirements—request your free evaluation today.
How to migrate an ADF pipeline to Fabric? Migrating an ADF pipeline to Fabric involves exporting pipeline definitions as ARM templates, adapting them for Fabric Data Factory, and reconfiguring connections within the Fabric workspace. You must map ADF datasets to Fabric lakehouse tables, update linked services, and validate trigger configurations. Testing each pipeline in Fabric ensures data flows execute correctly before decommissioning ADF resources. Complex pipelines with custom activities require additional refactoring for Fabric compatibility. This process demands careful planning to avoid disruption. Kanerika’s migration accelerator automates much of this conversion—contact our team to streamline your ADF to Fabric migration.
Will Azure Data Factory be deprecated? Microsoft has not announced any deprecation timeline for Azure Data Factory. ADF continues receiving updates and remains a supported service for data integration workloads. However, Microsoft’s strategic focus on Fabric suggests that future innovation will concentrate there. Organizations using ADF should monitor Microsoft’s roadmap while planning gradual migration to Fabric for long-term platform alignment. Proactive migration avoids rushed transitions when support policies eventually change. Starting your Azure to Fabric migration now gives your team time to adapt methodically. Kanerika helps enterprises build phased migration strategies that minimize disruption—reach out to discuss your timeline.
What is the difference between ADF and Fabric Data Pipelines? ADF operates as a standalone Azure service for ETL orchestration, while Fabric Data Pipelines are embedded within Microsoft Fabric’s unified analytics platform. Fabric pipelines natively integrate with OneLake, lakehouses, and Power BI without requiring separate configurations. ADF uses linked services and integration runtimes, whereas Fabric simplifies connectivity through workspace-level settings. Fabric also provides built-in governance and lineage tracking across all data assets. The core pipeline authoring experience remains similar, easing the learning curve for ADF users transitioning to Fabric. Kanerika’s experts guide enterprises through these differences during migration—book a technical walkthrough today.
Is Microsoft Fabric the future? Microsoft Fabric represents the company’s vision for unified data analytics, combining previously separate services into one cohesive platform. Microsoft is investing heavily in Fabric, positioning it as the foundation for enterprise analytics moving forward. The platform’s rapid feature releases, tight Copilot integration, and consolidated licensing model signal long-term strategic priority. Organizations adopting Fabric now gain early access to innovations while simplifying their analytics architecture. Waiting may mean larger migration efforts later as legacy services receive less attention. Kanerika helps enterprises future-proof their data strategy with Fabric adoption—connect with us to start planning.
What is the difference between Azure Synapse and Fabric? Azure Synapse combines data warehousing and big data analytics within Azure, while Microsoft Fabric extends this by integrating Power BI, Data Factory, and real-time analytics into a single SaaS platform. Fabric’s OneLake eliminates data duplication across services, unlike Synapse’s separate storage layers. Fabric also offers simplified capacity-based licensing versus Synapse’s resource-specific pricing. Synapse remains available for existing deployments, but Fabric provides a more unified experience for new analytics initiatives. Understanding these differences helps plan your Azure to Microsoft Fabric migration effectively. Kanerika’s consultants map Synapse workloads to Fabric equivalents—schedule your assessment now.
Why use Fabric over Azure? Fabric consolidates analytics capabilities that previously required multiple Azure services into one unified platform with shared governance, storage, and licensing. Instead of managing separate subscriptions for Data Factory, Synapse, Power BI, and Data Lake, Fabric provides everything under a single capacity model. This reduces administrative overhead, simplifies cost management, and accelerates time-to-insight by eliminating data movement between services. Organizations still use Azure for broader infrastructure needs while leveraging Fabric specifically for analytics workloads. The combination delivers both flexibility and consolidation. Kanerika designs hybrid Azure-Fabric architectures optimized for your enterprise—contact us for a strategy session.
Can I migrate ADF pipelines directly to Fabric? Yes, ADF pipelines can migrate to Fabric, though direct one-click migration is not available. You export pipeline JSON definitions from ADF, then import and adapt them within Fabric Data Factory. Most pipeline activities translate directly, but linked services, datasets, and integration runtime configurations require reconfiguration for Fabric’s workspace model. Triggers and scheduling also need manual recreation. Automated migration tools significantly reduce this effort by handling bulk conversions and dependency mapping. Testing remains essential before production cutover. Kanerika’s migration accelerator automates ADF to Fabric pipeline conversion—reach out for a demonstration of our approach.
How long does ADF to Fabric migration typically take? ADF to Fabric migration timelines range from weeks to several months depending on pipeline complexity, data volumes, and integration dependencies. Simple environments with fewer than fifty pipelines often complete within four to six weeks. Enterprise deployments with hundreds of pipelines, custom activities, and complex orchestration require three to six months for thorough migration and testing. Factors like SSIS package conversion, integration runtime reconfiguration, and downstream system validation extend timelines further. Proper planning and automated tooling accelerate the process considerably. Kanerika’s migration methodology delivers predictable timelines—request a scoping session to estimate your specific project duration.
Are Azure Data Factory and Fabric Data Factory the same? Azure Data Factory and Fabric Data Factory share the same core pipeline authoring experience but operate in different contexts. ADF runs as a standalone Azure service with its own resource management, while Fabric Data Factory exists within Microsoft Fabric’s unified workspace environment. Fabric Data Factory natively connects to OneLake, lakehouses, and other Fabric workloads without additional configuration. Licensing also differs—ADF bills per activity run, whereas Fabric uses capacity-based pricing. The familiar interface eases transition, but architectural differences require careful migration planning. Kanerika helps teams understand these distinctions during Azure to Fabric migration—talk to our specialists today.
Do you need Azure to use Fabric? Microsoft Fabric does not require an existing Azure subscription for basic usage. Fabric operates as a standalone SaaS platform accessible through Microsoft 365 licensing or dedicated Fabric capacity purchases. However, hybrid scenarios connecting Fabric to Azure Data Lake, Azure SQL, or other Azure services naturally require Azure subscriptions for those resources. Organizations already invested in Azure benefit from seamless integration between platforms. New users can start with Fabric independently and expand into Azure as needed. Understanding these options shapes your migration approach effectively. Kanerika architects solutions spanning both Fabric and Azure—consult with us to design your optimal configuration.
Is Azure Synapse dying? Azure Synapse is not being discontinued, but Microsoft’s strategic focus has shifted toward Microsoft Fabric for unified analytics. Synapse continues receiving support and critical updates, though major innovation now concentrates on Fabric. Organizations running production Synapse workloads should evaluate migration timelines based on their business roadmap rather than immediate urgency. However, delaying migration indefinitely may create technical debt as Fabric matures and Synapse’s relative feature parity diminishes. Proactive planning ensures smooth transitions without rushed execution. Kanerika helps enterprises assess their Synapse environments and plan phased migrations to Fabric—schedule your readiness assessment today.
Do I need special licensing for Microsoft Fabric? Microsoft Fabric requires either a Fabric capacity subscription or Power BI Premium capacity to access full platform capabilities. Trial capacities allow initial exploration, but production workloads need paid F-SKU or P-SKU capacities. Licensing covers all Fabric workloads—Data Factory, Data Warehouse, Lakehouse, and Power BI—under unified capacity billing rather than per-service charges. Organizations already holding Power BI Premium can enable Fabric features within existing capacity. Understanding licensing implications before migration prevents budget surprises. Kanerika advises enterprises on optimal Fabric licensing configurations aligned with their workload requirements—connect with us for licensing guidance.
Can I run ADF and Fabric pipelines simultaneously? Yes, running ADF and Fabric pipelines simultaneously is fully supported and often recommended during migration. This parallel operation allows gradual workload transition without disrupting production processes. Organizations typically migrate pipeline groups incrementally, validating Fabric execution before decommissioning ADF equivalents. Both platforms can access shared data sources, enabling side-by-side testing and comparison. The hybrid period length depends on migration complexity and validation requirements. Maintaining both temporarily adds operational overhead but reduces transition risk significantly. Kanerika designs phased migration approaches that leverage parallel execution for seamless cutover—talk to our team about your transition strategy.
What happens to my existing integration runtimes? Existing self-hosted integration runtimes in ADF do not automatically transfer to Microsoft Fabric. Fabric Data Factory supports its own integration runtime configuration, requiring reinstallation and reconfiguration of self-hosted runtimes within Fabric workspaces. Azure-hosted integration runtimes in ADF have no direct equivalent in Fabric, which uses managed connectivity instead. On-premises data gateway configurations also need recreation for Fabric access. Planning runtime migration alongside pipeline conversion ensures connectivity remains intact throughout the transition. Kanerika’s migration methodology includes comprehensive integration runtime assessment and reconfiguration—contact us to ensure your data connectivity remains uninterrupted.
Does Fabric support all ADF connectors? Fabric Data Factory supports most common ADF connectors but not the complete connector catalog available in standalone ADF. Core connectors for Azure services, SQL databases, file systems, and popular SaaS applications work in Fabric. However, some specialized or less common connectors may lack Fabric equivalents currently. Microsoft continues expanding Fabric connector coverage with regular updates. Before migration, auditing your ADF connector usage against Fabric availability prevents unexpected gaps. Alternative approaches using custom connectors or intermediate staging can address missing connectors. Kanerika performs connector compatibility assessments as part of migration planning—request your detailed analysis today.
How do I handle SSIS packages during migration? SSIS packages require special handling during Azure to Fabric migration since Fabric does not natively run SSIS. Options include converting SSIS logic to Fabric Data Factory pipelines, maintaining Azure-SSIS Integration Runtime separately, or refactoring packages into modern dataflows. Simple packages often convert directly to pipeline activities, while complex packages with scripts need careful redesign. Some organizations maintain hybrid architectures where SSIS continues running on Azure while Fabric handles new workloads. Evaluating each package’s complexity determines the optimal approach. Kanerika’s SSIS modernization experts convert legacy packages efficiently during Fabric migrations—reach out for a package assessment.
Is Azure now Fabric? Azure and Fabric are distinct but complementary platforms. Azure remains Microsoft’s comprehensive cloud infrastructure offering compute, storage, networking, and hundreds of services. Microsoft Fabric is a specialized analytics platform built on Azure infrastructure that unifies data integration, warehousing, and business intelligence. Fabric consolidates specific Azure analytics services like Data Factory and Synapse into one experience but does not replace Azure’s broader capabilities. Organizations use Azure for general cloud workloads while leveraging Fabric specifically for analytics needs. Understanding this distinction guides proper migration scope. Kanerika helps enterprises architect solutions using both Azure and Fabric optimally—consult with our team.
How to connect Azure Data Factory to Fabric? Connecting Azure Data Factory to Fabric involves configuring ADF pipelines to write data into Fabric OneLake endpoints or lakehouse storage. You create linked services in ADF pointing to Fabric workspace URLs using service principal or managed identity authentication. This hybrid approach allows gradual migration where ADF continues orchestration while Fabric handles downstream analytics. Shortcuts in Fabric can also reference external Azure storage that ADF populates, enabling immediate access without data duplication. Proper authentication setup ensures secure cross-platform connectivity throughout the transition period. Kanerika implements hybrid ADF-Fabric architectures for phased migrations—schedule a technical consultation to explore your options.
