Make the move to Microsoft Fabric with the help of our experienced consultants

Fabric makes it easy to link and share data across your organisation — which is genuinely useful, and also how you end up with a "OneDump" scenario where no one agrees on which version of the truth to trust. The answer isn't to restrict access; it's to implement the right architecture before the sprawl starts. We design domain-driven Medallion Architectures — a structured approach that separates raw, refined, and production-ready data into distinct layers — and govern the whole estate using Microsoft Purview (Microsoft's data cataloguing and lineage platform). That means every dataset has an owner, a definition, and a quality score. Your unified lake stays a clean, searchable asset rather than a liability that erodes trust in your reporting.

Fabric runs on a shared capacity model — meaning all your workloads draw from the same compute pool. One inefficient Spark job (Fabric's engine for large-scale data processing) can throttle the dashboards your executives rely on and push you into overage costs. We set up granular capacity monitoring and automated alerts as part of every Fabric architecture we deliver, so you can see exactly what's consuming your capacity, when, and why. We also right-size your F-SKU — the capacity tier you're paying for — so your spending stays predictable rather than driven by surprises. If a workload is misbehaving, you'll know about it before your finance team does.

The honest answer is: it depends on your legacy environment, and anyone who tells you otherwise before looking at it is guessing. Some elements — standard SQL pipelines, straightforward transformations — can be migrated with a high degree of automation. Complex stored procedures and legacy ETL logic often need strategic refactoring to actually benefit from the new environment, rather than just re-creating the same problems on a different platform. Our Discovery Assessment maps exactly what you have: what can move quickly, what needs a manual touch, and what should be redesigned rather than migrated. That assessment is what prevents the mid-project surprises that blow out timelines and budgets. When migration does proceed, we execute it with zero data loss by design — pre-migration validation, schema harmonisation, cutover planning, and post-migration testing built into every engagement.

Yes — and this is one of the most important conversations to have before an AI deployment. Turning on Microsoft Copilot over messy, siloed, or ungoverned data doesn't produce better insights; it produces faster versions of incorrect answers. The AI has no way of distinguishing validated data from stale or conflicting data unless that classification exists in your data estate. We assess AI-readiness as part of every Fabric engagement — including whether your semantic layer (the structured layer that defines how data is organised and related) and data pipelines are in a state where the outputs can actually be trusted. The goal is that when your business leaders act on an AI-generated insight, they can do so with confidence. That requires getting the foundation right first.

Fabric is designed to be inclusive of SQL-first teams — and that's genuinely one of its strengths. But the shift to a unified platform can still be a steep learning curve for people used to working in traditional silos, and some Fabric capabilities do require Spark and Python skills that aren't always available internally. Rather than leaving your team to figure it out or rushing into a hiring process, our managed service model gives you the option of embedded Fabric engineers and data architects who work alongside your team — adding the technical depth you need for specific workloads, without the recruitment timeline or permanent headcount overhead. Over time, we also help your internal team build capability so the dependency on external support reduces, not grows.