Simplifying Fabric migration for a compliance-heavy data ecosystem

About client

The client is a specialised data organisation that collects, analyses, and distributes public-sector and social-domain datasets. Their work supports reporting, transparency, and evidence-based decision-making for institutions that rely heavily on accurate, timely, and responsibly governed data.

Challenges

When the client came to us, they were managing a large and complex Azure Data Factory (ADF) ecosystem—connecting multiple systems, customers, and product lines. Over time, the environment had grown difficult to maintain. What they needed was not just migration, but a more organised, predictable, and governed platform that could support their expanding data responsibilities, led to their decision of implementing Microsoft Fabric.

Fragmented configuration and metadata

Metadata and configuration logic were scattered across SQL tables, scripts, and multiple environments. Maintaining consistency required significant manual effort—and even minor changes came with operational risk.

High technical dependence

Routine configuration updates required SQL expertise. Business users had limited ability to manage their own settings, increasing reliance on technical teams.

Difficult Scalability

With each new dataset and integration, pipelines became harder to manage. Scaling the environment meant navigating around a growing list of constraints.

These challenges made it clear that continuing with the same architecture would limit their ability to grow and adapt. A transition to Microsoft Fabric became a strategic decision to unify their data landscape and strengthen governance.

Our approach

The objective was to migrate and modernise—without disrupting existing workflows. Our solution focused on three foundational pillars:

1. A metadata-driven framework

We re-engineered the configuration layer using a metadata-driven design. Instead of hard-coded components, pipelines were made dynamic, consistent, and easier to maintain across environments.

2. A business-friendly PowerApps interface

To reduce reliance on SQL, we built a PowerApps solution connected to Fabric’s Gold Warehouse. This allowed users to create and update systems, products, and customer configurations themselves. The interface also handled relationship mapping and dependency management, reducing errors and improving metadata quality.

3. Scalable pipeline architecture

Pipelines were redesigned to be modular, transparent, and easier to extend. Fabric’s unified governance and security layer enabled controlled access—ensuring sensitive datasets were only visible to authorised departments and clients.

Together, these changes delivered a framework that was not only easier to operate, but significantly more resilient.

The impact

The migration delivered benefits beyond the original scope, reshaping the client’s day-to-day operations:

Greater governance and consistency: A single metadata layer eliminated duplication and ensured stronger compliance across environments.

Lower technical dependency: Business teams were able to manage configurations independently, reducing operational bottlenecks.

Improved scalability: New systems and datasets could be onboarded with minimal disruption.

Strengthened data security: Fabric’s role-based control model ensured clean segregation and safer distribution of information.

Faster reporting and decision-making: A cleaner, automated foundation improved data readiness and reduced reporting delays.

Conclusion

This project was more than a platform migration—it was an opportunity to rethink how data should be structured, governed, and maintained in a compliance-heavy environment. With a metadata-driven foundation, intuitive configuration workflows, and a unified Fabric ecosystem, the client now operates with greater clarity, control, and confidence.

What emerged is not just a modernised data platform, but a future-ready foundation built for scalability, automation, and AI-driven insights.