👤 Who is this for?

Solutions Engineer Data Architect IT Admin — Use these industry templates as starting points for Fabric architecture design. Each scenario includes recommended components, data flow, governance model, and SKU sizing.

Templates

Customer Scenario Templates

Pre-built architecture blueprints for common industry use cases. Click any scenario to expand the full blueprint.

🛒

Retail Analytics Platform

Retail F64+
▾

Unified analytics platform for multi-channel retail operations — combining POS transactions, e-commerce data, inventory, and customer behavior into a single Fabric lakehouse for real-time and historical insights.

🏗️ Architecture Components

Data Factory Lakehouse Data Warehouse Power BI Real-Time Intelligence Data Activator

🔄 Data Flow

Sources
POS systems, e-commerce, inventory DB, loyalty program, supplier feeds
→
Bronze
Raw transactions, clickstream events, inventory snapshots (Delta)
→
Silver
Cleaned orders, deduplicated customers, joined inventory + sales
→
Gold
Sales metrics, customer 360, demand forecasting, store performance
→
Consume
Power BI dashboards, real-time inventory alerts, demand predictions

📐 Recommended Sizing

Starting SKU F64
Why F64 Free Power BI viewers for store managers + Spark for daily ETL
Scale trigger Move to F128 when >200 concurrent BI viewers or >50 daily Spark jobs

🔒 Governance Model

  • RLS per region/store: Store managers see only their location's data
  • Sensitivity labels: Customer PII (loyalty data) classified as Confidential
  • Workspaces: [Retail]-[Analytics]-[Dev/Test/Prod] naming convention
  • Domain: "Retail Operations" with merchandising and supply chain sub-domains

📚 Related Sections

Medallion Architecture → Real-Time Analytics → Capacity Sizing →
🏥

Healthcare Data Platform

Healthcare F64+
▾

HIPAA-compliant data mesh architecture for healthcare organizations — separate domains for patient data, clinical operations, and financial analytics with strict access controls and audit trails.

🏗️ Architecture Components

Data Factory Lakehouse Data Warehouse Power BI Microsoft Purview Managed Private Endpoints

🔄 Data Flow

Sources
EHR (Epic/Cerner), claims systems, lab results, pharmacy, wearables
→
Domains
Patient • Clinical • Financial (each with own lakehouse)
→
Medallion
Bronze→Silver→Gold per domain, PHI de-identified at Silver
→
Cross-Domain
OneLake shortcuts for population health and quality metrics
→
Consume
Clinical dashboards, regulatory reports, research datasets

📐 Recommended Sizing

Starting SKU F64
Why F64 Managed VNets for HIPAA + enough CU for multi-domain ETL
Scale trigger Move to F128 for real-time patient monitoring or ML workloads

🔒 Governance Model

  • HIPAA compliance: Managed private endpoints, no public internet access to data
  • PHI protection: Sensitivity labels auto-applied, CLS on SSN/MRN, dynamic masking
  • Audit trail: Purview lineage from source EHR to report, full access logs
  • Domain isolation: Patient, Clinical, Financial domains with separate security groups

📚 Related Sections

Data Mesh Architecture → Security & Access → Network Security →
🏦

Financial Services Reporting

Finance F128+
▾

Enterprise reporting platform for banks and financial institutions — regulatory compliance reports, risk analytics, and real-time trading dashboards with strong audit controls and SOX compliance.

🏗️ Architecture Components

Data Warehouse Lakehouse Power BI Deployment Pipelines Fabric Mirroring Microsoft Purview

🔄 Data Flow

Sources
Core banking, trading platforms, risk engines, market data feeds
→
Ingest
Mirroring for real-time CDC from SQL databases + batch market data
→
Transform
Bronze→Silver reconciliation, Silver→Gold regulatory aggregations
→
Serve
Warehouse for regulatory SQL queries + Power BI dashboards

📐 Recommended Sizing

Starting SKU F128
Why F128 Heavy SQL workloads for regulatory queries + concurrent analyst access
Scale trigger F256 for intraday risk calculations or >500 report consumers

🔒 Governance Model

  • SOX compliance: Full deployment pipelines (Dev→Test→Prod) with approval gates
  • CLS on sensitive fields: Account numbers and transaction amounts masked for non-privileged users
  • Private endpoints: No public access, VNet-integrated for regulatory requirements
  • Immutable audit: Purview lineage + activity logs retained per regulatory timelines

📚 Related Sections

Deployment Pipelines → Governance & Security → Private Link →
🏭

IoT & Manufacturing Analytics

Manufacturing F32–F64
▾

Real-time operational intelligence for manufacturing and IoT — streaming sensor data into Eventhouse for live monitoring, anomaly detection, and predictive maintenance, with historical analysis in Lakehouse.

🏗️ Architecture Components

Real-Time Intelligence Eventhouse Data Activator Lakehouse Spark Notebooks Power BI

🔄 Data Flow

Sources
IoT sensors, PLCs, SCADA, MES, ERP (SAP/Oracle)
→
Stream
Event Hub / IoT Hub → Eventhouse for real-time KQL queries
→
Process
Real-time anomaly detection + Data Activator alerts on thresholds
→
Store
Lakehouse for historical time-series, Spark for predictive ML
→
Consume
Live OEE dashboards, maintenance predictions, production reports

📐 Recommended Sizing

Starting SKU F32
Why F32 Eventhouse ingestion is efficient; F32 handles moderate sensor volumes
Scale trigger F64 when ingesting >100K events/sec or adding ML training workloads

🔒 Governance Model

  • Device-level access: Separate workspaces per plant/facility with dedicated security groups
  • Retention policies: Raw sensor data 90 days in Eventhouse, summarized in Lakehouse long-term
  • OT/IT separation: Network isolation between OT sensor networks and Fabric analytics
  • Alert governance: Data Activator rules reviewed quarterly, escalation paths documented

📚 Related Sections

Real-Time Analytics → Real-Time Intelligence → Capacity Management →
📈

Marketing Analytics — Customer 360

Marketing F32–F64
▾

Unified customer view combining CRM, web analytics, social media, and campaign data — enabling identity resolution, attribution modeling, and personalized marketing with GDPR-compliant data handling.

🏗️ Architecture Components

Data Factory Lakehouse Spark Notebooks Data Warehouse Power BI OneLake Shortcuts

🔄 Data Flow

Sources
CRM (Dynamics/Salesforce), GA4, social APIs, email, ad networks
→
Bronze
Raw events from each channel, API responses, campaign metadata
→
Silver
Identity resolution (Spark), unified customer profile, consent tracking
→
Gold
Customer segments, campaign attribution, lifetime value, churn scores
→
Consume
Campaign dashboards, audience exports, personalization APIs

📐 Recommended Sizing

Starting SKU F32
Why F32 Identity resolution runs as batch Spark job; BI load is moderate
Scale trigger F64 when adding real-time personalization or >100M events/month

🔒 Governance Model

  • GDPR compliance: Consent-based processing, right-to-deletion via Spark notebooks
  • PII masking: CLS hides email/phone from analysts, only aggregated segments visible
  • Data retention: Raw web events deleted after 13 months per GDPR, aggregates retained longer
  • Cross-team sharing: Marketing Gold layer shared via shortcuts with Sales domain (read-only)

📚 Related Sections

Data Engineering → Security & Compliance → Power BI Best Practices →
🏢

Enterprise Data Platform

Enterprise F128+
▾

Full-scale enterprise data platform using data mesh principles — multiple business domains (Finance, HR, Operations, Sales) each owning their data products, with centralized governance and shared Fabric capacity.

🏗️ Architecture Components

All Fabric Workloads Fabric Domains Deployment Pipelines Microsoft Purview Managed VNet Capacity Metrics App

🔄 Data Flow

Sources
SAP, Salesforce, Oracle, SQL Server, APIs, files
→
Domain Lakehouses
Each domain runs Bronze→Silver→Gold in its own workspace
→
Data Products
Endorsed Gold datasets published as data products with SLAs
→
Cross-Domain
Shortcuts + Warehouse views for cross-domain analytics
→
Self-Service
Departments build reports from governed data products

📐 Recommended Sizing

Starting SKU F128
Why F128 Multiple concurrent domain workloads + enterprise BI + Spark jobs
Scale trigger F256+ when >5 active domains or >1000 BI consumers

🔒 Governance Model

  • Federated governance: Central CoE defines standards; domain teams implement within guardrails
  • Deployment pipelines: All domains use Dev→Test→Prod with branch policies and PR reviews
  • Purview integration: Automated cataloging, lineage, sensitivity labels across all domains
  • Capacity management: Separate capacities for Prod vs Dev; Metrics app monitored by CoE
  • Workspace naming: [Domain]-[Product]-[Env] enforced by policy (e.g., Finance-AR-Prod)

📚 Related Sections

Data Mesh Architecture → Governance-First Guide → Capacity Sizing →
💡 How to Use These Templates

These scenarios are starting points, not rigid blueprints. Every customer's needs are unique. Use these as conversation starters:

  • 1. Identify which scenario is closest to your customer's situation
  • 2. Walk through the architecture components and data flow together
  • 3. Adjust the governance model based on their compliance requirements
  • 4. Use the TCO Calculator to estimate costs
  • 5. Start with a pilot using the recommended SKU, then iterate

📎 See Also

Architecture — Core Concepts → Best Practices → Data Mesh Architecture → Capacity Planning →