Data Platform
Building enterprise AI on Microsoft Fabric
Data engineering, warehousing, and Power BI reporting on one Microsoft-native foundation. We use Fabric where an organization's data and reporting are already Microsoft-centric.
Where does Microsoft Fabric fit in an AI and data architecture?
Microsoft Fabric is a strong fit when an organization's data engineering, warehousing, and reporting are already — or should be — consolidated on one Microsoft-native platform, particularly where Power BI is the primary reporting tool. Divine AI Systems uses Fabric in the Knowledge & Data layer of a client's architecture to prepare and govern data that feeds both dashboards and downstream AI features, typically alongside Azure.
Where It Fits
Where Fabric fits in the architecture
Fabric sits between raw enterprise data and the reporting or AI layers that consume it.
Knowledge & Data
- OneLake
- engineering
- warehousing
- reporting
Orchestration & Integration
- Microsoft 365
- Dynamics
- external sources
Cloud & Infrastructure
- Azure
Enterprise Systems
- Dynamics 365
- Microsoft 365
Security, Monitoring & Governance
- Microsoft Purview
- sensitivity labeling
What We Build
What we build on Microsoft Fabric
The range of what a Fabric-based engagement typically includes.
Unified Data Pipelines
Consolidating data from Microsoft 365, Dynamics, and external sources into OneLake.
Power BI-Connected Reporting
Data models designed to feed Power BI dashboards directly from governed Fabric data.
AI-Ready Data Preparation
Structuring and governing data so it can be safely used by retrieval and AI layers.
Data Governance
Purview-integrated access control and sensitivity labeling across the data estate.
When It Makes Sense
When Fabric is the right choice
These are the conditions where Fabric tends to be the stronger fit.
Power BI is already your primary reporting tool
Fabric's native Power BI integration avoids a separate data-modeling layer for reporting.
Your organization is standardized on Microsoft 365 and Azure
Consolidating data work on a Microsoft-native platform reduces the number of tools in play.
Data engineering and reporting need to share one governed source
OneLake removes the need to move data between separate engineering and BI systems.
Tradeoffs
What to weigh before committing
These are the considerations we walk through with clients before recommending Fabric.
Newer platform than some alternatives
Certain advanced data engineering patterns are more mature on longer-established platforms — we'll flag this where it matters for your use case.
Best value tied to Microsoft 365 and Power BI usage
Without that existing investment, the consolidation benefit is smaller relative to other data platforms.
Complex custom machine learning may need a dedicated ML platform
For heavy custom model development, a platform built specifically for the ML lifecycle can be a better fit alongside Fabric.
Related Services
Services we typically pair with Fabric
Related Solutions
Business problems this often supports
FAQ
Questions about building on Microsoft Fabric
We build on Microsoft Fabric for client engagements. We describe that as technical experience, not a formal Microsoft partnership or certification.
Not necessarily — Fabric adds the most value when you also want to consolidate data engineering and warehousing on the same platform as your reporting.
Fabric runs on Azure and is typically deployed alongside a client's existing Azure environment rather than as a standalone platform.
To a degree, yes — but for heavy custom ML development, we'll often recommend pairing it with a dedicated ML platform rather than relying on Fabric alone.
Already reporting through Power BI?
Tell us about your current Microsoft data setup, and we'll show you where Fabric and AI genuinely add value.