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Architecture Reference · Microsoft Stack · 2026

Right Tool.
Right Workload.

How Fabric, Databricks, Purview, and Copilot work in concert, every layer of the Microsoft stack precisely mapped to a single, trusted financial truth for mid-market and PE-backed organizations.

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Data Supply Chain
Click any stage · Governed end-to-end by Microsoft Purview
01
Sources
02
Ingest
03
Engineer
04
Quality
05
BI
06
AI
◉
Output
Active: Enterprise Sources ERP · CRM · Flat Files
Purview governs all layers
Microsoft Fabric
Fast Lane · When speed-to-insight is the constraint
The Commuter Car
SaaS-native, low-code, fast time-to-insight. Data Factory, OneLake, and Power BI handle board reporting, close acceleration, and self-serve executive views without waiting on data engineering.
Azure Databricks
Heavy Lane · When complexity and scale are the constraint
The Heavy-Duty Truck
Built for scale. High-volume raw data ingestion, ML model training, and complex cross-entity transformations, integrated with Fabric and Purview for governed, AI-ready outputs.
Architecture Reference 2026 Edition
Mid-Market Finance PE-Backed Operations Microsoft Stack
🛡 Microsoft Purview
Data Lineage Access Policy Compliance Data Cataloging Sensitivity Labels
Governance spans every layer: every record, pipeline, and output is tracked, classified, and policy-enforced end to end.
01 Sources
Enterprise Data Sources
Raw transactional, master, and external data across your full technology stack
ERP · SAP / Oracle Finance Systems CRM · Salesforce Operations Data External Feeds Flat Files · APIs Streaming Events
02 Ingest
Ingestion Layer: Dual Pipeline
Right tool matched to right workload from day one
SaaS-Native · Finance-First
Microsoft Fabric: Data Factory
Data Factory pipelines · ERP connectors · Scheduling
OneLake Bronze layer · Single copy, zero duplication
Eventstream for live AR/AP monitoring
ML-Grade · High-Volume
Azure Databricks: High-Volume
High-volume raw data ingestion at scale
Multi-ERP parallel ingest · Batch and streaming
Auto Loader · Delta format · Incremental processing
03 Engineer
Data Engineering & Transformation
Where raw data becomes governed, trusted, and analytics-ready
Fabric Silver / Gold
OneLake: Silver & Gold Layers
Governed Lakehouse · Finance semantic layer
Golden Supplier Records · Deduplicated master data
UNSPSC taxonomy · Spend classification rules
Databricks ML
Databricks: ML & AI Transforms
Fuzzy-match dedup · Confidence scoring · Normalization
ML model training · Anomaly detection · Forecasting
Delta-format outputs → integrated into Fabric OneLake
04 Quality
Data Quality & Observability
Every record validated, every pipeline monitored, before a single number reaches a report or AI model
Validation
Schema checks · Null detection · Range rules · Business constraints
Freshness
SLA monitoring · Pipeline health · Stale data alerts
Reliability
Anomaly detection · Statistical drift · Automated alerting
Observability
End-to-end lineage · Record-level audit trail · Full traceability
05 BI
Business Intelligence & Reporting
Trusted semantic models powering board-ready views, close packages, and self-serve analytics
Power BI: Semantic Layer
Pre-built financial models · Direct Lake mode · Sub-second refresh on clean data
Executive Dashboards
Board reporting · PE portfolio roll-ups · Working capital views · DSO / DPO / CCC
Close Acceleration
Automated close checklists · Reconciliation outputs · Audit-ready packages
Self-Serve Analytics
Finance-led exploration on governed data · No IT dependency · Row-level security
06 AI
AI
AI & Copilot Intelligence Layer
Acts on governed, quality-validated data from BI; finance teams query in plain English, AI drafts the answers
Copilot Agents
2026 Fabric workload kits · Embedded in finance workflows · Variance explanation · Exception triage
Natural Language Queries
"What is our DPO vs last year?" Answered from governed semantic models, not raw data.
Decision Support
Working capital signals · Anomaly surfacing · Automated close checklist completion
◉ Output
Outcome
Single Financial Truth
Trusted, self-serve views across revenue, margins, cash, and KPIs, governed, AI-ready, and always current. Finance and operations finally speaking from the same number.
↓60%
Reporting Prep Time
<24h
Close Acceleration
100%
Data Lineage Coverage
0
Manual Dict. Updates
Platform Ops
Azure DevOps
CI/CD Pipelines
Infrastructure as Code
Release Management
Quality Gates
Automated Testing
Rollback Safety
Audit Trails
Deploys & governs all layers
DataQubi © 2026 DataQubi · Architecture reference for mid-market and PE-backed finance organizations · dataqubi.com