AI-Ready Data Platform Services

Microsoft Fabric is Microsoft’s end-to-end SaaS analytics platform. It brings data integration (Data Factory), data engineering, lakehouse and warehouse, real-time intelligence, data science and Power BI together on one shared data lake, OneLake. Power BI is one part of Fabric: it visualises data, while Fabric also stores, prepares, governs and serves that data. If you already use Power BI, Fabric extends that investment rather than replacing it.

An ontology creates a shared model of how your business works. It defines key concepts such as customers, products and orders, explains how they relate, and connects them to the underlying data. This gives Copilots and AI agents the business context they need to understand questions correctly and provide more consistent, relevant answers. Without this shared context, AI may have access to the data but not fully understand what it means.

A data warehouse holds structured, modelled data for reporting. A lakehouse combines the flexibility of a data lake (structured and unstructured data, suited to AI and machine learning) with warehouse-style management. Data fabric is an architecture approach that connects distributed data through a shared integration and metadata layer, while data mesh is an operating model in which business domains own their data as products. Most organisations end up with a combination, for example, a Fabric lakehouse with domain-based ownership. We recommend the pattern based on your scale, skills and goals.

It depends on scope and the current state of your data. A focused first phase built around one high-value use case typically delivers working results within a few weeks; a wider, organisation-wide platform is then rolled out in phases. The Assess stage gives you a realistic timeline and plan before any build begins.

There are two parts: Microsoft Fabric capacity and implementation services. Fabric is licensed through capacities (F SKUs) that can be paid for as you go or reserved, and sized to your workloads, so the right sizing matters. Implementation cost depends on scope, number of data sources and migration effort. Our assessment gives you a costed plan and a capacity recommendation, so you don’t pay for more than you need.

Yes. We migrate in phases: old and new platforms run in parallel, numbers are reconciled, and reports are switched over only once they match. Fabric features such as mirroring and OneLake shortcuts can also bring existing data into Fabric without moving it all on day one.

AI-ready data is integrated, accurate and structured around clear business definitions. It is properly governed, protected with appropriate access controls and sensitivity labels, and easy for users and AI solutions to discover. Copilots and AI agents deliver more reliable, context-aware answers when they work with trusted, well-modelled data. That is why a modern data platform is the foundation for scaling AI across the organisation.

Yes. Many organisations already run Azure Databricks, and it can work alongside Fabric thanks to the open Delta Lake format and OneLake integration. We help you decide what stays in Databricks, what moves to Fabric and how the two work together, without duplicating data or cost.

Yes. Microsoft Fabric capacities can be deployed in the Azure region that meets your residency requirements, and we design with data residency, sovereignty and compliance in mind, using Microsoft Purview for governance and mapping the design to your regulatory obligations.