Most organizations today run multiple Azure services (Synapse, Data Factory, Analysis Services) that don’t naturally work together. Data teams end up spending significant time managing integrations instead of analyzing data. Research shows that IT teams spend an average of 19 weeks annually managing data and app infrastructure across public cloud environments, while almost 80% of organizations store more than half of their data in multi-cloud and hybrid infrastructures.
Microsoft Fabric addresses this by bringing your data tools into one unified platform. Azure to Microsoft Fabric migration means moving your existing workloads (data warehouses , ETL pipelines, reports) into Fabric’s integrated environment. The business case is becoming clearer: a Forrester Consulting study found that Microsoft Fabric delivers 379% ROI over three years, with organizations seeing a 25% increase in data engineering productivity.
The challenge lies in executing this transition without disrupting your operations or losing data integrity. Kanerika helps organizations navigate Azure to Microsoft Fabric migration from assessment to deployment. Our proven accelerators and migration frameworks reduce transition time while maintaining data governance standards. We handle everything from dependency mapping and workload prioritization to performance enhancement and user training, so your team can focus on using the platform rather than figuring out how to migrate to it.
TL;DR Azure to Microsoft Fabric migration consolidates fragmented data tools into one unified platform, improving data engineering productivity by 25% and delivering 379% ROI over three years. Manual migration takes 3 to 6 months and requires extensive pipeline rebuilding. Kanerika’s migration accelerator automates the process in 4 to 8 weeks through pipeline assessment, automated activity conversion, workspace configuration, and validation testing, reducing costs by 40% to 60% while maintaining zero downtime.
Why Should Enterprises Migrate from Azure Data Factory/Synapse to Microsoft Fabric? Microsoft Fabric combines all data analytics tools into one integrated experience. Instead of managing separate services for data integration , warehousing, engineering, and business intelligence, Fabric provides everything in a single platform.
This unified approach eliminates the complexity of connecting multiple tools. Teams can move from data ingestion to insights without switching between different platforms or managing complex integrations.
Due to its distributed architecture, Fabric capacities are less sensitive to overall load, temporal spikes, and high concurrency. By consolidating capacities to larger Fabric capacity SKUs, customers can achieve increased performance and throughput.
Fabric’s cloud-native SaaS architecture automatically scales based on workload demands. Unlike ADF’s infrastructure management requirements, Fabric handles resource allocation automatically for consistent performance during peak processing times.
3. Simplified Architecture and Reduced Complexity Fabric eliminates many architectural complexities that exist in ADF environments. Fabric eliminates datasets, defining data properties inline within activities. This reduces the number of objects teams need to manage and maintain.
Fabric replaces Linked Services with Connections defined within activities, simplifying connection management and reducing configuration overhead. Teams no longer need to manage integration runtime configurations, reducing operational overhead and infrastructure costs.
4. Built-in CI/CD and Collaboration Features Built-in CI/CD features without needing external Git integration simplify development processes significantly. Teams can promote code changes across environments without complex Azure DevOps configurations.
In Fabric, the CI/CD experience is much easier and more flexible than in Azure Data Factory or Synapse. There is no connection between CI/CD and ARM templates in Fabric making it simple to select individual parts of your Fabric workspace.
5. Advanced Monitoring and Observability The monitoring hub gives you a complete view of all your workloads, and you can drill down into any activity for detailed insights. Cross-workspace analysis is built right in, so you can see the big picture across your entire organization.
This improved monitoring provides better visibility into pipeline performance, helping teams identify challenges faster and optimize data processing workflows more effectively.
6. Enhanced Developer Experience In Fabric Data Factory, building your pipeline, dataflows, and other Data Factory items is incredibly easy and fast thanks for native integration. The improved user interface reduces development time and makes data pipeline creation more accessible.
The Save as feature lets you duplicate any existing pipeline in seconds. It’s perfect for creating development versions, testing variations, or setting up similar processes.
7. Cost Optimization Through OneLake Integration Fabric’s centralized data lake , OneLake, reduces the need for data movement or duplication, cutting down costs and complexity in data management. OneLake eliminates the need to maintain multiple storage systems and reduces data transfer costs.
Workspace integration with OneLake for streamlined analytics management means all Fabric services automatically use OneLake without additional configuration.
8. Better Integration with Microsoft Ecosystem The Office 365 Outlook activity lets you send customized email notifications about pipeline runs, activity status, and results—all with simple configuration. Native Microsoft Teams integration provides better collaboration during pipeline development and monitoring.
9. Real-Time Data Processing Capabilities Microsoft Fabric’s Real-Time Intelligence (RTI) capabilities allow organisations to ingest, process, query, visualise, and act upon time-sensitive data in real-time. This enables businesses to respond to changing conditions immediately rather than waiting for batch processing cycles.
10. Advanced AI and Analytics Integration Microsoft Fabric integrates advanced analytics and AI capabilities to transform raw data into actionable insights. With tools like Copilot for Power BI and Copilot for Notebooks, Fabric uses AI to enhance data visualization, predictive analytics, and machine learning.
Azure Data Factory/Synapse to Microsoft Fabric: Challenges of Manual Migration 1. Complex Pipeline Reconstruction Requirements Manual migration requires rebuilding every ADF/Synapse pipeline from scratch in Fabric. The JSON definitions for Fabric pipelines differ slightly from ADF, so you can’t directly copy/paste or import/export pipeline JSON.
Teams must manually recreate hundreds of pipelines, understanding both platforms’ different design patterns and component structures. This process takes months of development effort for large-scale implementations.
2. Integration Runtime Conversion Complexity Recreate SHIRs as OPDGs and VNet-enabled Azure IRs as Virtual Network Data Gateways. Manual conversion of self-hosted integration runtimes requires reconfiguring network connectivity and security settings.
Teams must ensure data gateways provide equivalent functionality while maintaining security compliance and performance requirements.
3. Data Connectivity Migration Issues Linked Services and Datasets from ADF don’t exist in Fabric. Manual migration requires recreating all data connections within individual activities rather than centralized linked services.
This architectural change means every activity needs individual connection configuration, increasing the chance of errors and inconsistencies.
4.Testing and Validation Overhead Manual migration requires extensive testing to ensure migrated pipelines produce identical results. Teams must validate data accuracy, performance metrics, and error handling across all migrated workflows.
Testing scope expands significantly when validating hundreds of pipelines manually, often need months of validation cycles before production deployment.
Fabric dataflows use Power Query , while ADF data flows rely on a different execution engine and language. Manual conversion requires rewriting transformation logic using completely different tools and syntax.
Teams must learn Power Query while simultaneously reverse-engineering existing ADF dataflow logic, creating significant learning curve challenges.
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How Kanerika’s Migration Accelerator Automates Azure to Microsoft Fabric Migration Kanerika’s migration accelerator automates the complex process of moving your Azure Data Factory and Synapse pipelines to Microsoft Fabric. The framework reduces manual intervention, minimizes migration risks, and ensures your data pipelines continue running without disruption.
1. Pipeline Architecture Assessment & Discovery Our migration accelerator starts by analyzing your complete Azure data infrastructure . The tool examines every ADF pipeline, Synapse workspace, and data orchestration component to build a detailed migration blueprint.
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 & Fabric Optimization The accelerator automatically converts your Azure Data Factory activities into their Fabric Data Factory equivalents. This automated conversion maintains your business logic while taking advantage of native Fabric capabilities like OneLake integration and unified compute.
How the conversion works:
3. Integration Mapping & Workspace Configuration Your Azure resources get organized into Fabric workspaces that match your team structure and data governance policies. The accelerator handles the technical setup of connections, permissions, and capacity assignments automatically.
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 & Performance Optimization The final phase ensures everything works correctly before you switch over to production. Our accelerator runs comprehensive tests on migrated pipelines and fine-tunes performance based on Fabric’s architecture.
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
5 Key Benefits of Automated Azure to Microsoft Fabric Migration 1. Faster Migration Timeline Manual Azure to Fabric migration typically takes 3 to 6 months depending on pipeline complexity. Kanerika’s accelerator compresses this timeline to 4 to 8 weeks by automating pipeline discovery, activity conversion, and workspace configuration.
The accelerator eliminates weeks spent on manual code translation and testing cycles. Your team can move from assessment to production deployment faster while maintaining thorough validation at every stage.
2. Lower Resource Requirements Traditional migration projects require dedicated data engineers working full time for months. Our accelerator reduces the staffing needs to a small team working part time, freeing up your engineers for other priorities.
You avoid hiring external consultants or pulling multiple team members off revenue-generating projects. The automated conversion handles technical complexity that would otherwise require specialized Azure and Fabric expertise.
3. Zero Data Loss and Business Continuity The accelerator includes built-in validation checks that verify data integrity throughout the migration process. Every pipeline, dataset, and transformation gets tested against source data to confirm accuracy before cutover.
Your existing Azure pipelines continue running during migration with no service interruption. The parallel testing environment lets you validate Fabric pipelines thoroughly before switching production workloads, eliminating the risk of data corruption or missing records.
4. Minimal Manual Intervention Automated activity mapping removes the need for line-by-line code rewrites across hundreds of pipelines. The accelerator handles repetitive conversion tasks like updating connection strings, reconfiguring triggers, and adapting dataset schemas to Fabric standards.
Your team focuses on strategic decisions rather than tedious technical work. Manual effort gets limited to reviewing converted pipelines and adjusting business logic where custom requirements exist.
5. Reduced Migration Costs Shorter timelines and smaller team requirements translate directly to lower project costs. Organizations typically save 40% to 60% on migration expenses compared to manual approaches or traditional consulting engagements.
The accelerator eliminates expensive trial-and-error cycles where teams discover compatibility issues late in the process. You avoid budget overruns from extended timelines, additional contractor hours, and production incidents caused by migration errors.
Manual vs. Automated Azure to Microsoft Fabric Migration Aspect Manual Migration Automated Migration (Kanerika’s Accelerator) Timeline 6-12 months or more for enterprise deployments Few 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 in Fabric Automated activity mapping preserves business logic Integration Runtime Setup Manual reconfiguration of network and security settings 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 across hundreds of pipelines 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 on migration expenses Manual Effort Line-by-line code rewrites and JSON reconstruction Limited to reviewing converted pipelines Expertise Required Deep knowledge of both ADF and Fabric platforms Accelerator handles technical complexity automatically Business Continuity Potential service interruption during cutover Zero downtime with parallel testing environment Dataflow Conversion Rewrite transformation logic in Power Query manually Automated conversion maintains original logic
Why Choose Kanerika for Azure to Microsoft Fabric Migration Services Kanerika specializes in seamless transitions from legacy data platforms like Azure Data Factory/Synapse to modern cloud-native platforms like Microsoft Fabric. We help organizations modernize their data infrastructure while maintaining complete operational continuity and data integrity .
Our automated migration approach ensures Microsoft Fabric delivers immediate value through improved development productivity, reduced operational overhead, and enhanced data processing capabilities. Manual ADF migration creates significant project risks including extended timelines, cost overruns, and pipeline accuracy problems.
Automated Migration Excellence Our migration accelerator automates the entire conversion process , transforming complex ADF pipelines and dataflows into fully functional Fabric workflows within hours instead of months. The solution preserves original transformation logic, data connections, and business rules with complete precision.
We support comprehensive platform transitions including ETL modernization, legacy system migrations, cloud data platform upgrades, and various data pipeline transformation projects across industries and organization sizes.
Quantifiable Business Results Client organizations consistently achieve substantial efficiency improvements, improved data processing performance, and reduced operational costs through our migration services . Our accelerators save thousands of manual hours per project while maintaining high quality standards and security requirements.
The transformation extends beyond simple platform replacement to include complete modernization toward cloud-native data integration capabilities where pipelines execute faster, development becomes more accessible, and teams embrace integrated analytics across all data processing functions.
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Frequently Asked Questions Is Microsoft Fabric better than Azure Data Factory? Microsoft Fabric offers several advantages over Azure Data Factory including unified analytics platform integration, built-in CI/CD capabilities, and simplified architecture without infrastructure management. Customers who choose Fabric capacities can expect to benefit from alignment with the Microsoft Fabric product roadmap. However, ADF remains suitable for organizations with specific integration runtime requirements or existing Azure ecosystem investments.
Will Azure Data Factory be deprecated? Can I migrate ADF pipelines directly to Fabric? The JSON definitions for Fabric pipelines differ slightly from ADF, so you can’t directly copy/paste or import/export pipeline JSON. Pipelines require reconstruction in Fabric, but automated migration tools can significantly reduce manual effort and ensure accuracy during the transition process.
What are the main differences between ADF and Fabric Data Factory? Key differences include Fabric uses cloud-based compute by default, while ADF requires configuring integration runtimes, Fabric replaces Linked Services with Connections defined within activities, and Fabric eliminates datasets, defining data properties inline within activities. Fabric also provides better integration with Microsoft’s analytics ecosystem .
How long does ADF to Fabric migration typically take? Manual migrations can take 6-12 months for enterprise environments, but automated migration accelerators reduce this timeline to weeks or days. The duration depends on the number of pipelines, complexity of transformations, and testing requirements for production deployment.
Do I need special licensing for Microsoft Fabric? Fabric pipelines require at minimum a Microsoft Fabric (Free) license to author in a premium capacity workspace. Organizations need appropriate Fabric capacity licensing based on their data processing requirements and user access needs.
Can I run ADF and Fabric pipelines simultaneously? Yes, Azure Data Factory Item (Mount) lets you effortlessly bring your existing Azure Data Factory into a Fabric workspace. It’s like opening a live view of your ADF pipelines right inside Fabric—without having to migrate or rebuild anything. This enables gradual migration and testing while maintaining existing operations .
What happens to my existing integration runtimes? Does Fabric support all ADF connectors? Fabric supports many ADF connectors but not all features have complete parity. To view the connectors that are currently supported for data pipelines , refer to Pipeline support. Microsoft continues expanding Fabric connector support based on customer requirements and usage patterns.
How do I handle SSIS packages during migration? Fabric doesn’t currently support SSIS IRs but allows invoking ADF pipelines for SSIS execution. Organizations can maintain SSIS functionality by invoking ADF pipelines from Fabric or converting SSIS packages to Fabric-native solutions where appropriate.
