As organisations modernise their data platforms, traditional data integration tools are being re-evaluated in the context of cloud-native analytics and lakehouse architectures. Azure Data Factory has long served as a reliable and mature platform for orchestrating data movement and transformation across diverse environments. However, Microsoft Fabric represents a strategic evolution toward a unified analytics platform designed to support modern, analytics-driven use cases.
Azure Data Factory was built primarily as a general-purpose data integration service. It continues to be well-suited for hybrid data movement and complex orchestration scenarios. However, it operates largely as a standalone platform that requires integration with multiple downstream services for analytics, reporting, and advanced processing. Microsoft Fabric consolidates these capabilities into a single software-as-a-service experience, reducing architectural complexity and operational overhead.
A key advantage of Microsoft Fabric is its unified storage layer, OneLake. Built on the Delta Lake format, OneLake provides a single source of truth for all data within Fabric. Once data is ingested, it becomes immediately accessible across data engineering, data warehousing, and business intelligence workloads without the need for duplication or additional configuration. This enables organisations to adopt lakehouse architectures that improve data accessibility and collaboration.
Fabric also introduces a modernised development experience. Dataflows Gen2, powered by the next-generation Power Query engine, offer a more intuitive and efficient approach to data transformation compared to traditional Azure Data Factory mapping data flows. Native Git integration and built-in deployment pipelines simplify version control and environment promotion, enabling more consistent and maintainable delivery practices.
From an analytics perspective, Microsoft Fabric is designed to support a broader range of workloads, including real-time analytics and AI-enabled use cases. Its tight integration with Power BI allows insights to be delivered more quickly, while shared capacity across services improves scalability and cost predictability when workloads are planned effectively.
Despite these benefits, migration from Azure Data Factory to Microsoft Fabric requires careful planning. Fabric is still evolving, and some advanced integration features available in Azure Data Factory may not yet be fully supported. Additionally, there is no direct migration path, which means existing pipelines often need to be redesigned to align with Fabric’s lakehouse-centric architecture. Organisations must also consider the implications of capacity-based pricing and ensure teams are prepared for new development patterns.
Microsoft’s strategic direction is clear. While Azure Data Factory remains supported and appropriate for many existing workloads, Microsoft Fabric is positioned as the future of analytics on Azure. For most organisations, the optimal approach is to adopt Fabric for new analytics initiatives while gradually modernising Azure Data Factory workloads where there is clear business value.
By taking this phased approach, organisations can reduce platform complexity, modernise data integration capabilities, and align their analytics platforms with Microsoft’s long-term roadmap, without disrupting existing, business-critical processes.
If you’re assessing how Azure Data Factory and Microsoft Fabric fit into your future data platform, having the right guidance can make a real difference. At DSP, we work with organisations at every stage of this journey, from architectural assessment through to delivery. Get in touch to discuss how we can support your data modernisation plans.
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