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🎰 Supercharge Microsoft Fabric 🎲

Casino & Gaming Industry POC + Federal Expansions

Transform your casino operations with enterprise-grade analytics powered by Microsoft Fabric

Real-time insights • Medallion Architecture • Regulatory Compliance • Direct Lake BI

📚 Documentation • 🚀 Quick Start • 🐳 Docker • 📖 Tutorials • 🏗️ Architecture • 📊 POC Agenda

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</div>

📍 Navigation

Home / README

Section	Description
🎯 Overview	What this POC delivers
👥 Target Audience	Who should use this
🚀 Quick Start	Get up and running
⚡ 5-Minute Quick Start	Fastest path to first results
📋 Cheat Sheet	Quick reference & commands
🐳 Docker Support	Container-based deployment
💻 Dev Container	One-click development setup
📊 Power BI Reports	Pre-built report templates
💰 Cost Estimation	Azure cost planning
📁 Sample Data	Pre-generated datasets
🏗️ Architecture	Technical deep-dive
🎰 Data Domains	Gaming-specific domains
📂 Repository Structure	What's included
📊 POC Agenda	Workshop schedule
📖 Tutorials	Learning path
📚 Documentation Site	Full docs with search
📜 Compliance	Regulatory coverage
🏛️ Phase 7 Expansions	Federal, streaming, analytics expansions
🆕 Phase 9-10 New Fabric Experience	40+ new feature docs, best practices, Bicep modules

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🎯 Overview

This repository provides a complete, production-ready proof-of-concept environment for Microsoft Fabric, purpose-built for the casino and gaming industry.

<table> <tr> <td width="50%">

✨ Key Features

Feature	Description
🏛️ Medallion Architecture	Bronze/Silver/Gold Lakehouse
⚡ Real-Time Intelligence	Casino floor monitoring
📊 Direct Lake	Sub-second Power BI analytics
🔐 Microsoft Purview	Data governance & compliance
🚀 Infrastructure as Code	Bicep/ARM deployment
📚 Step-by-Step Tutorials	Hands-on learning path

</td> <td width="50%">

💎 Value Proposition

┌─────────────────────────────────────┐
│  🎰 REAL-TIME SLOT TELEMETRY        │
│  🎲 TABLE GAME ANALYTICS            │
│  👤 PLAYER 360 INSIGHTS             │
│  💰 FINANCIAL COMPLIANCE            │
│  🔒 SECURITY & SURVEILLANCE         │
│  📋 REGULATORY REPORTING            │
└─────────────────────────────────────┘

</td> </tr> </table>

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👥 Target Audience

<table> <tr> <td align="center" width="16%"> <h2>🏗️</h2> <b>Data Architects</b><br/> _{Evaluating Fabric} </td> <td align="center" width="16%"> <h2>⚙️</h2> <b>Data Engineers</b><br/> _{Medallion patterns} </td> <td align="center" width="16%"> <h2>📊</h2> <b>BI Developers</b><br/> _{Direct Lake solutions} </td> <td align="center" width="16%"> <h2>📐</h2> <b>Solution Architects</b><br/> _{Enterprise platforms} </td> <td align="center" width="16%"> <h2>🎰</h2> <b>Gaming Industry</b><br/> _{Regulated operations} </td> <td align="center" width="16%"> <h2>🏨</h2> <b>Hospitality</b><br/> _{Guest analytics} </td> </tr> </table>

---

🚀 Quick Start

Choose your preferred deployment method:

Method	Best For	Time to Start
🐳 Docker Quick Start	Quick demos, testing data generators	~5 minutes
💻 Dev Container	Full development environment	~10 minutes
☁️ Azure Deployment	Production-like POC environment	~30 minutes

🔀 Two Ways to Run This POC

Path A (Production-Aligned): Deploy Azure infrastructure via Bicep (infra/main.bicep), upload data to ADLS Gen2, and connect it to Fabric via OneLake shortcuts. This unlocks governance (Purview), security (Private Endpoints), and monitoring tutorials. Cost: ~$1-3/day idle.

Path B (Quickstart): Skip Bicep entirely — upload generated data straight into your Fabric Lakehouse via the UI and start running notebooks immediately. Fastest path to learning the medallion architecture. Upgrade to Path A anytime.

See Tutorial 00 — Step 4 for details.

---

Docker Quick Start

The fastest way to generate sample data and explore the POC.

# Clone the repository
git clone https://github.com/fgarofalo56/Suppercharge_Microsoft_Fabric.git
cd Suppercharge_Microsoft_Fabric

# Generate demo data (1000 events, 7 days)
docker-compose run --rm demo-generator

# Generate full dataset (30 days, all domains)
docker-compose run --rm data-generator

# Output will be in ./output directory

💡 Pro Tip: Use the demo generator for quick testing (generates in ~2 minutes), or the full data generator for realistic POC scenarios with 30 days of data.

See Docker Support for more options.

---

Dev Container Quick Start

One-click development environment with all tools pre-configured.

VS Code:

1. Install the Dev Containers extension
2. Open this repository in VS Code
3. Click "Reopen in Container" when prompted (or use Ctrl+Shift+P > "Dev Containers: Reopen in Container")

GitHub Codespaces:

1. Click the green "Code" button on GitHub
2. Select "Codespaces" tab
3. Click "Create codespace on main"

💡 Pro Tip: GitHub Codespaces provides a cloud-based development environment with no local installation required. Perfect for team collaboration and workshops.

See Dev Container for configuration details.

---

Azure Deployment

📋 Prerequisites: Complete the full Prerequisites Guide before starting deployment. This includes Azure subscription setup, tool installation, and resource provider registration.

Prerequisites Checklist

• Azure subscription with Owner or Contributor access
• Microsoft Fabric capacity (F64 recommended for POC)
• Azure CLI 2.50+ with Bicep extension
• PowerShell 7+ or Bash
• Git installed
• Docker (optional, for data generation)

---

Step-by-Step Deployment

<table> <tr> <td width="80">

1️⃣

</td> <td>

Clone the Repository

git clone https://github.com/fgarofalo56/Suppercharge_Microsoft_Fabric.git
cd Suppercharge_Microsoft_Fabric

</td> </tr> <tr> <td>

2️⃣

</td> <td>

Configure Environment

cp .env.sample .env
# Edit .env with your Azure subscription and tenant details

⚠️ Warning: Ensure all required environment variables are populated. Missing values will cause deployment failures.

</td> </tr> <tr> <td>

3️⃣

</td> <td>

Login to Azure

az login
az account set --subscription "<your-subscription-id>"

</td> </tr> <tr> <td>

4️⃣

</td> <td>

Deploy Infrastructure

az deployment sub create \
  --location eastus2 \
  --template-file infra/main.bicep \
  --parameters infra/environments/dev/dev.bicepparam

💡 Pro Tip: Run az deployment sub what-if first to preview all resource changes before actual deployment.

</td> </tr> <tr> <td>

5️⃣

</td> <td>

Start Learning

👉 Begin with Tutorial 00: Environment Setup

</td> </tr> </table>

---

🏗️ Architecture

High-Level Data Flow

flowchart TB
    subgraph Sources["📥 Data Sources"]
        RT[/"⚡ Real-Time<br/>Casino Floor"/]
        BATCH[/"📦 Batch<br/>Systems"/]
        EXT[/"🔗 External<br/>APIs"/]
    end

    subgraph Ingestion["🔄 Ingestion Layer"]
        ES[Eventstreams]
        DF[Dataflows Gen2]
        MR[Database Mirroring]
    end

    subgraph Medallion["🏛️ Medallion Architecture"]
        subgraph Bronze["🥉 BRONZE"]
            BL[(Bronze Lakehouse<br/>Raw Data)]
        end
        subgraph Silver["🥈 SILVER"]
            SL[(Silver Lakehouse<br/>Cleansed)]
        end
        subgraph Gold["🥇 GOLD"]
            GL[(Gold Lakehouse<br/>Business Ready)]
        end
    end

    subgraph Analytics["📊 Analytics & Governance"]
        DL[Direct Lake<br/>Semantic Model]
        EH[Eventhouse<br/>KQL Analytics]
        PV[Microsoft Purview<br/>Governance]
    end

    subgraph Consumption["👁️ Consumption"]
        PBI[Power BI<br/>Reports]
        RTD[Real-Time<br/>Dashboards]
        API[REST APIs]
    end

    RT --> ES
    BATCH --> DF
    EXT --> MR

    ES --> BL
    DF --> BL
    MR --> BL

    BL --> SL
    SL --> GL

    GL --> DL
    GL --> EH
    GL -.-> PV
    SL -.-> PV
    BL -.-> PV

    DL --> PBI
    EH --> RTD
    GL --> API

    style Bronze fill:#cd7f32,color:#fff
    style Silver fill:#c0c0c0,color:#000
    style Gold fill:#ffd700,color:#000

Architecture Highlights

<details> <summary><b>🥉 Bronze Layer - Raw Ingestion</b></summary>

• Purpose: Land raw data with minimal transformation
• Pattern: Schema-on-read, append-only
• Format: Delta Lake tables
• Retention: Configurable (default 90 days)
• Key Feature: Full historical lineage preserved

</details> <details> <summary><b>🥈 Silver Layer - Cleansed & Validated</b></summary>

• Purpose: Business rules and data quality
• Pattern: Slowly Changing Dimensions (SCD Type 2)
• Transformations: Deduplication, validation, standardization
• Data Quality: Great Expectations integration
• Key Feature: Audit-ready data lineage

</details> <details> <summary><b>🥇 Gold Layer - Business Ready</b></summary>

• Purpose: Aggregations, KPIs, and business metrics
• Pattern: Star/Snowflake schema
• Optimization: Partitioned by date, optimized for queries
• Refresh: Incremental or scheduled
• Key Feature: Direct Lake semantic model integration

</details> <details> <summary><b>⚡ Real-Time Intelligence</b></summary>

• Eventstreams: Apache Kafka-compatible streaming
• Eventhouse: KQL-based analytics database
• Latency: Sub-second to seconds
• Use Cases: Slot monitoring, player alerts, anomaly detection

</details>

---

🏛️ Phase 7: Industry Expansions

[!NOTE] Phase 7 Complete — 71 features delivered across 5 waves with 197/197 tests passing and zero regressions.

Phase 7 expanded the Casino/Gaming POC to cover federal agencies, migration paths, streaming connectors, analytics pipelines, tribal healthcare, and DOT/FAA transportation.

flowchart TD
    subgraph Core["🎰 Casino/Gaming POC (Phases 1-6)"]
        C[Reference Implementation<br/>92/100 Audit Score]
    end

    subgraph W1["🏛️ Wave 1: Federal Agencies"]
        USDA[USDA<br/>Crop & Food Safety]
        SBA[SBA<br/>PPP & 7a Loans]
        NOAA[NOAA<br/>Weather & Storms]
        EPA[EPA<br/>Air & Water Quality]
        DOI[DOI<br/>Earthquakes & Land]
        DOJ[DOJ<br/>Crime & Antitrust]
    end

    subgraph W2["🔄 Wave 2: Migration & Streaming"]
        MIG[Migration Tutorials<br/>Snowflake · DB2 · Teradata]
        STR[8 Streaming Notebooks<br/>CDC · IoT · Kafka]
    end

    subgraph W3["📊 Wave 3: Analytics"]
        VID[Video Security<br/>YOLO · DeepSORT]
        MOV[People Movement<br/>30 Zones · Queue Detection]
        GEO[Geolocation<br/>H3 · Geofencing]
    end

    subgraph W4["🏥 Wave 4: Expansions"]
        TH[Tribal Healthcare<br/>HIPAA · IHS · FHIR]
        DOT[DOT/FAA<br/>FedRAMP · Aviation]
    end

    C --> W1
    C --> W2
    C --> W3
    W1 --> W4
    W2 --> W4
    W3 --> W4

    style Core fill:#ffd700,color:#000
    style W1 fill:#4a90d9,color:#fff
    style W2 fill:#50c878,color:#000
    style W3 fill:#ff6b6b,color:#fff
    style W4 fill:#9b59b6,color:#fff

Wave	Scope	Features	Tests	Status
Wave 1	Federal Agencies (USDA, SBA, NOAA, EPA, DOI)	26	54	🟢 Complete
Wave 2	Migration & Streaming	19	20	🟢 Complete
Wave 3	Video, Movement, Geolocation Analytics	12	30	🟢 Complete
Wave 4	Tribal Healthcare + DOT/FAA	15	—	🟢 Complete
Wave 5	Final Regression	1	197	🟢 Complete
Total		71	197	All Complete

---

🆕 Phase 9-10: New Fabric Experience

[!NOTE] Phases 9-10 Complete — Full coverage of the new Microsoft Fabric experience (July 2025 – April 2026 GA wave) with 40+ new documents, 8 Bicep modules, and 269/269 tests passing.

Phases 9 and 10 modernize the POC for the new Fabric experience, covering every major feature and enterprise best practice.

New Feature Documentation (22 features)

Category	Features
AI & Intelligence	Fabric IQ, AI Copilot, Data Agents, AutoML & Model Endpoints, Fabric MCP
Data Integration	Mirroring (Oracle/SAP/BigQuery/MySQL), Copy Job CDC, dbt Integration
Analytics	Direct Lake, Real-Time Intelligence, Semantic Link, Eventhouse Vector DB
Platform	Fabric SQL Database, API for GraphQL, Translytical Task Flows, Digital Twin Builder
Governance	OneLake Security, OneLake Catalog, Workspace Monitoring, Data Mesh, Iceberg Interop
Performance	Materialized Lake Views, Lakehouse Schemas, Shortcut Transformations

Enterprise Best Practices (16 guides)

Category	Guides
Operations	Capacity Planning & Cost Optimization, Monitoring & Observability, Testing Strategies
Security	Network Security (PE/VNet/IP Firewall), Identity & RBAC, Customer-Managed Keys, Outbound Access Protection
Architecture	Medallion Deep Dive, Multi-Tenant Workspace, Data Sharing & Federation, Migration Patterns
Data Engineering	Incremental Refresh & CDC, fabric-cicd CI/CD, Spark Runtime Migration, SQL Audit Logs
Resilience	Disaster Recovery & BCDR, Alerting & Data Activator

Infrastructure (Bicep)

Module	Purpose
fabric-warehouse.bicep	Fabric Warehouse configuration metadata
fabric-sql-database.bicep	Fabric SQL Database with DDM & CMK
fabric-pipeline.bicep	Data Factory Pipeline with scheduling
alerts-and-budgets.bicep	Capacity alerts & budget management
workspace-identity.bicep	Workspace Identity (GA 2026)

👉 See Feature Documentation and Best Practices for the complete guides.

---

🐳 Docker Support

Run the data generators and validation tools without installing any dependencies.

Available Services

Service	Command	Description
data-generator	docker-compose run --rm data-generator	Generate full dataset (30 days)
demo-generator	docker-compose run --rm demo-generator	Quick demo dataset (7 days, smaller volumes)
streaming-generator	docker-compose up streaming-generator	Real-time streaming to Event Hub
data-validator	docker-compose run --rm data-validator	Validate generated data

Common Commands

# Build the Docker image
docker-compose build

# Generate all data with custom parameters
docker-compose run --rm data-generator --all --days 14 --format parquet

# Generate specific data types
docker-compose run --rm data-generator --slots 50000 --players 1000

# Stream events to Azure Event Hub (requires configuration)
EVENTHUB_CONNECTION_STRING="your-connection-string" \
EVENTHUB_NAME="slot-telemetry" \
docker-compose up streaming-generator

# Run validation on generated data
docker-compose run --rm data-validator

Environment Variables

Variable	Default	Description
DATA_FORMAT	parquet	Output format (parquet, csv, json)
DATA_DAYS	30	Days of historical data to generate
EVENTHUB_CONNECTION_STRING	-	Azure Event Hub connection string
EVENTHUB_NAME	slot-telemetry	Event Hub name for streaming
STREAMING_RATE	10	Events per second for streaming

For detailed Docker documentation, see docker/README.md.

---

💻 Dev Container

The Dev Container provides a complete, pre-configured development environment with all necessary tools.

Included Tools

Tool	Version	Purpose
Python	3.11	Data generation, notebooks
Azure CLI	Latest	Azure resource management
Bicep	Latest	Infrastructure as Code
Git	Latest	Version control
PowerShell	7.x	Scripting
Docker CLI	Latest	Container management

VS Code Extensions (Pre-installed)

• Azure Account
• Bicep
• Python
• Jupyter
• Docker
• GitHub Copilot (if licensed)
• Power BI (preview)

Features

• Automatic Python environment: Virtual environment created on container start
• Azure CLI authentication: Sign in once, stay authenticated
• Port forwarding: Automatic forwarding for Jupyter and other services
• GitHub Codespaces ready: Same experience in the cloud

Configuration Files

.devcontainer/
├── devcontainer.json    # Main configuration
├── Dockerfile           # Container image definition
└── post-create.sh       # Post-creation setup script

For customization options, see the Dev Containers documentation.

---

📊 Power BI Reports

Pre-built Power BI report templates and semantic model definitions for quick deployment.

Available Reports

Report	Description	Key Visuals
Casino Executive Dashboard	High-level KPIs and trends	Revenue trends, floor performance, player metrics
Slot Performance Analysis	Machine-level analytics	Hold percentage, utilization, jackpot frequency
Player 360 View	Customer analytics	Player segments, lifetime value, visit patterns
Compliance Monitoring	Regulatory reporting	CTR/SAR status, W-2G tracking, audit trails
Real-Time Floor Monitor	Live casino floor status	Machine status, alerts, occupancy

Report Locations

reports/
├── report-definitions/           # Power BI report definition files
│   ├── executive-dashboard/
│   ├── slot-performance/
│   └── player-360/
└── semantic-model/               # Direct Lake semantic model
    ├── tables/                   # Table definitions
    └── measures/                 # DAX measures

How to Import

1. Connect to Fabric Workspace: Open Power BI Desktop, connect to your Fabric workspace
2. Import Semantic Model: Use the definitions in reports/semantic-model/
3. Import Reports: Open .pbip files from reports/report-definitions/
4. Configure Data Source: Point to your Gold layer Lakehouse

For detailed instructions, see reports/README.md.

---

💰 Cost Estimation

Understand Azure costs before deployment with our comprehensive cost guide.

Quick Reference

Environment	Fabric SKU	Monthly Estimate	Notes
Development	F4	$450 - $650	8 hrs/day weekdays
Staging	F16	$1,800 - $2,500	12 hrs/day weekdays
Production POC	F64	$9,500 - $12,500	24/7 operation
Production Pilot	F64 Reserved	$6,500 - $9,000	1-year reserved

Cost Breakdown (Production POC)

Component	Monthly Cost	% of Total
Fabric Capacity (F64)	~$8,500	75-80%
ADLS Gen2 Storage	~$500	4-5%
Microsoft Purview	~$800	7-8%
Log Analytics	~$300	2-3%
Key Vault	~$10	<1%
Networking	~$200	1-2%

Cost Optimization Tips

• Pause capacity during off-hours (saves up to 76%)
• Use reserved capacity for production (saves 25-30%)
• Implement storage lifecycle policies (move cold data to Cool tier)
• Set up Azure Cost Management alerts

💡 Pro Tip: Enable auto-pause on dev/staging environments to automatically suspend compute during idle periods. This can reduce costs by up to 76% for non-production workloads.

For detailed cost scenarios and optimization strategies, see docs/COST_ESTIMATION.md.

---

📁 Sample Data

Pre-generated sample datasets for quick exploration without running data generators.

Available Datasets

Dataset	Records	Format	Size	Location
Slot Telemetry (7 days)	10,000	CSV/Parquet	~10 MB	sample-data/bronze/
Player Profiles	500	CSV/Parquet	~1 MB	sample-data/bronze/
Table Games	2,000	CSV/Parquet	~2 MB	sample-data/bronze/
Financial Transactions	1,000	CSV/Parquet	~1 MB	sample-data/bronze/

Quick Exploration

# View sample data structure
ls sample-data/bronze/

# Load into Pandas (Python)
import pandas as pd
df = pd.read_parquet('sample-data/bronze/slot_telemetry_sample.parquet')
df.head()

# View schemas
ls sample-data/schemas/

Schema Definitions

Sample data includes matching schema definitions in sample-data/schemas/ that document:

• Column names and data types
• Business descriptions
• Valid value ranges
• PII handling requirements

💡 Pro Tip: Sample data is perfect for initial exploration and testing notebooks without waiting for data generation. Use it to validate your environment setup before generating full datasets.

For generating larger custom datasets, see data_generation/README.md.

---

🎰 Casino/Gaming Data Domains

<table> <tr> <th width="15%">Domain</th> <th width="5%">Icon</th> <th width="40%">Description</th> <th width="25%">Key Entities</th> <th width="15%">Compliance</th> </tr> <tr> <td><b>Slot Machines</b></td> <td align="center">🎰</td> <td>Telemetry, meter readings, jackpot events, machine performance analytics</td> <td>Machines, Meters, Jackpots, Sessions</td> <td>NIGC MICS</td> </tr> <tr> <td><b>Table Games</b></td> <td align="center">🎲</td> <td>Hand results, chip tracking, table performance, dealer analytics</td> <td>Tables, Games, Hands, Chips</td> <td>NIGC MICS</td> </tr> <tr> <td><b>Player/Loyalty</b></td> <td align="center">👤</td> <td>Player profiles, rewards programs, activity tracking, Player 360</td> <td>Players, Tiers, Points, Offers</td> <td>PCI-DSS, PII</td> </tr> <tr> <td><b>Financial/Cage</b></td> <td align="center">💰</td> <td>Transactions, fills, credits, cash management, cage operations</td> <td>Transactions, Fills, Drops</td> <td>FinCEN BSA</td> </tr> <tr> <td><b>Security</b></td> <td align="center">🔒</td> <td>Surveillance integration, access control, incident tracking</td> <td>Events, Incidents, Access Logs</td> <td>State Regs</td> </tr> <tr> <td><b>Compliance</b></td> <td align="center">📋</td> <td>CTR/SAR reporting, W-2G tax forms, regulatory filings</td> <td>CTRs, SARs, W-2Gs, Audits</td> <td>Federal/State</td> </tr> </table>

---

📂 Repository Structure

Suppercharge_Microsoft_Fabric/
│
├── 📁 .devcontainer/                  # 💻 Dev Container configuration
│   ├── devcontainer.json              # VS Code/Codespaces config
│   └── Dockerfile                     # Container image definition
│
├── 📁 .vscode/                        # ⚙️ VS Code settings
│   ├── settings.json                  # Workspace settings
│   ├── extensions.json                # Recommended extensions
│   └── launch.json                    # Debug configurations
│
├── 📁 docker/                         # 🐳 Docker configurations
│   ├── entrypoint.sh                  # Container entrypoint
│   └── generate-all.sh                # Data generation script
│
├── 📁 scripts/                        # 📜 Automation scripts
│   ├── deploy.ps1                     # Deployment automation
│   ├── generate-data.ps1              # Data generation wrapper
│   └── validate.ps1                   # Validation runner
│
├── 📁 infra/                          # 🚀 Infrastructure as Code (Bicep)
│   ├── main.bicep                     # Root orchestration template
│   ├── 📁 modules/                    # Reusable Bicep modules
│   └── 📁 environments/               # Environment-specific parameters
│       ├── dev/                       # Development configuration
│       ├── staging/                   # Staging configuration
│       └── prod/                      # Production configuration
│
├── 📁 docs/                           # 📚 Documentation
│   ├── ARCHITECTURE.md                # Detailed architecture guide
│   ├── DEPLOYMENT.md                  # Deployment procedures
│   ├── SECURITY.md                    # Security & compliance guide
│   ├── PREREQUISITES.md               # Setup requirements
│   └── COST_ESTIMATION.md             # Azure cost planning
│
├── 📁 tutorials/                      # 📖 Step-by-step tutorials
│   ├── 00-environment-setup/          # Initial setup
│   ├── 01-bronze-layer/               # Bronze implementation
│   ├── 02-silver-layer/               # Silver transformations
│   ├── 03-gold-layer/                 # Gold aggregations
│   ├── 04-real-time-analytics/        # Streaming analytics
│   ├── 05-direct-lake-powerbi/        # Power BI integration
│   ├── 06-data-pipelines/             # Pipeline orchestration
│   ├── 07-governance-purview/         # Data governance
│   ├── 08-database-mirroring/         # SQL mirroring
│   ├── 09-advanced-ai-ml/             # Machine learning
│   ├── 10-teradata-migration/        # Teradata modernization
│   ├── 24-snowflake-to-fabric/       # Snowflake migration
│   ├── 25-ibm-db2-source/            # IBM DB2 connectivity
│   ├── 26-multi-source-streaming/    # CDC & IoT streaming
│   ├── 27-video-security-analytics/  # AI video pipeline
│   ├── 28-people-movement-analytics/ # Foot traffic analytics
│   ├── 29-geolocation-analytics/     # H3 & geofencing
│   ├── 30-tribal-healthcare/         # HIPAA-compliant IHS
│   └── 31-federal-dot-faa/           # FedRAMP aviation
│
├── 📁 sample-data/                    # 📁 Pre-generated sample data
│   ├── bronze/                        # Bronze layer samples
│   └── schemas/                       # Schema definitions
│
├── 📁 reports/                        # 📊 Power BI templates
│   ├── report-definitions/            # Report .pbip files
│   └── semantic-model/                # Direct Lake model definitions
│       ├── tables/                    # Table definitions
│       └── measures/                  # DAX measures
│
├── 📁 poc-agenda/                     # 📅 3-Day workshop materials
├── 📁 data_generation/                # 🎲 Synthetic data generators
├── 📁 notebooks/                      # 📓 Fabric-importable notebooks
├── 📁 validation/                     # ✅ Testing & data quality
│
├── 🐳 Dockerfile                      # Data generator Docker image
├── 🐳 docker-compose.yml              # Multi-service orchestration
└── 📄 CHANGELOG.md                    # Version history

---

📊 3-Day POC Agenda

A structured workshop to experience the full Microsoft Fabric platform:

Day	Theme	Focus Areas	Key Deliverables
Day 1	🏗️ Foundation	Environment setup, Bronze & Silver layers	Working Lakehouse, data ingestion pipeline
Day 2	⚙️ Transformation	Gold layer, Real-time analytics	Business-ready datasets, streaming dashboard
Day 3	📊 Intelligence	Direct Lake, Power BI, Purview	Semantic model, reports, governance catalog

<details> <summary><b>📅 View Detailed Agenda</b></summary>

Day 1: Medallion Foundation (8 hours)

• Morning: Environment provisioning, workspace setup
• Afternoon: Bronze layer implementation, batch ingestion
• Wrap-up: Silver layer transformations, data quality

Day 2: Transformations & Real-Time (8 hours)

• Morning: Gold layer aggregations, star schema
• Afternoon: Eventstreams, Eventhouse, KQL queries
• Wrap-up: Real-time dashboard prototyping

Day 3: BI & Governance (8 hours)

• Morning: Direct Lake semantic model creation
• Afternoon: Power BI reports, Purview integration
• Wrap-up: Review, Q&A, next steps

</details>

👉 See POC Agenda for complete schedules and materials.

---

📖 Tutorials

Learning Path

flowchart LR
    subgraph L1["🟢 Level 1: Foundation"]
        T00[00-Setup]
        T01[01-Bronze]
    end

    subgraph L2["🟡 Level 2: Core"]
        T02[02-Silver]
        T03[03-Gold]
    end

    subgraph L3["🟠 Level 3: Advanced"]
        T04[04-Real-Time]
        T05[05-Direct Lake]
    end

    subgraph L4["🔴 Level 4: Enterprise"]
        T06[06-Pipelines]
        T07[07-Governance]
        T08[08-Mirroring]
        T09[09-AI/ML]
    end

    subgraph L5["🟣 Migration & Streaming"]
        T10[10-Teradata]
        T24[24-Snowflake]
        T25[25-DB2]
        T26[26-Streaming]
    end

    subgraph L6["🔵 Analytics & Expansions"]
        T27[27-Video]
        T28[28-Movement]
        T29[29-Geo]
        T30[30-Healthcare]
        T31[31-DOT/FAA]
    end

    T00 --> T01 --> T02 --> T03 --> T04 --> T05
    T05 --> T06 --> T07 --> T08 --> T09
    T09 --> T10 --> T24 --> T25 --> T26
    T26 --> T27 --> T28 --> T29 --> T30 --> T31

<table> <tr> <th>🎯 Level</th> <th>📖 Tutorial</th> <th>📝 Description</th> <th>⏱️ Duration</th> </tr> <tr> <td rowspan="2"><b>🟢 Foundation</b><br/>_{Start here}</td> <td><a href="tutorials/00-environment-setup/README.md"><b>00 - Environment Setup</b></a></td> <td>Azure & Fabric workspace provisioning</td> <td><code>~1 hour</code></td> </tr> <tr> <td><a href="tutorials/01-bronze-layer/README.md"><b>01 - Bronze Layer</b></a></td> <td>Raw data ingestion patterns</td> <td><code>~2 hours</code></td> </tr> <tr> <td rowspan="2"><b>🟡 Core</b><br/>_{Essential skills}</td> <td><a href="tutorials/02-silver-layer/README.md"><b>02 - Silver Layer</b></a></td> <td>Data cleansing & validation</td> <td><code>~2 hours</code></td> </tr> <tr> <td><a href="tutorials/03-gold-layer/README.md"><b>03 - Gold Layer</b></a></td> <td>Business aggregations & KPIs</td> <td><code>~2 hours</code></td> </tr> <tr> <td rowspan="2"><b>🟠 Advanced</b><br/>_{Real-time & BI}</td> <td><a href="tutorials/04-real-time-analytics/README.md"><b>04 - Real-Time Analytics</b></a></td> <td>Eventstreams & Eventhouse</td> <td><code>~3 hours</code></td> </tr> <tr> <td><a href="tutorials/05-direct-lake-powerbi/README.md"><b>05 - Direct Lake & Power BI</b></a></td> <td>Semantic models & reports</td> <td><code>~2 hours</code></td> </tr> <tr> <td rowspan="4"><b>🔴 Enterprise</b><br/>_{Production-ready}</td> <td><a href="tutorials/06-data-pipelines/README.md"><b>06 - Data Pipelines</b></a></td> <td>Orchestration & scheduling</td> <td><code>~2 hours</code></td> </tr> <tr> <td><a href="tutorials/07-governance-purview/README.md"><b>07 - Governance & Purview</b></a></td> <td>Data catalog & lineage</td> <td><code>~2 hours</code></td> </tr> <tr> <td><a href="tutorials/08-database-mirroring/README.md"><b>08 - Database Mirroring</b></a></td> <td>SQL Server replication</td> <td><code>~1 hour</code></td> </tr> <tr> <td><a href="tutorials/09-advanced-ai-ml/README.md"><b>09 - Advanced AI/ML</b></a></td> <td>Machine learning integration</td> <td><code>~3 hours</code></td> </tr> <tr> <td rowspan="4"><b>🟣 Migration</b><br/>_{Platform migration}</td> <td><a href="tutorials/10-teradata-migration/README.md"><b>10 - Teradata Migration</b></a></td> <td>Teradata to Fabric modernization</td> <td><code>~3 hours</code></td> </tr> <tr> <td><a href="tutorials/24-snowflake-to-fabric/README.md"><b>24 - Snowflake to Fabric</b></a></td> <td>Snowflake migration & cost comparison</td> <td><code>~3 hours</code></td> </tr> <tr> <td><a href="tutorials/25-ibm-db2-source/README.md"><b>25 - IBM DB2 Source</b></a></td> <td>DB2 connectivity & CDC patterns</td> <td><code>~3 hours</code></td> </tr> <tr> <td><a href="tutorials/26-multi-source-streaming/README.md"><b>26 - Multi-Source Streaming</b></a></td> <td>8 CDC & IoT streaming connectors</td> <td><code>~3 hours</code></td> </tr> <tr> <td rowspan="5"><b>🔵 Analytics & Expansions</b><br/>_{Industry verticals}</td> <td><a href="tutorials/27-video-security-analytics/README.md"><b>27 - Video Security</b></a></td> <td>AI video pipeline & edge processing</td> <td><code>~2.5 hours</code></td> </tr> <tr> <td><a href="tutorials/28-people-movement-analytics/README.md"><b>28 - People Movement</b></a></td> <td>Foot traffic & queue detection</td> <td><code>~2 hours</code></td> </tr> <tr> <td><a href="tutorials/29-geolocation-analytics/README.md"><b>29 - Geolocation Analytics</b></a></td> <td>H3 indexing & geofencing</td> <td><code>~2.5 hours</code></td> </tr> <tr> <td><a href="tutorials/30-tribal-healthcare/README.md"><b>30 - Tribal Healthcare</b></a></td> <td>HIPAA-compliant IHS analytics</td> <td><code>~3 hours</code></td> </tr> <tr> <td><a href="tutorials/31-federal-dot-faa/README.md"><b>31 - Federal DOT/FAA</b></a></td> <td>FedRAMP aviation analytics</td> <td><code>~2.5 hours</code></td> </tr> </table>

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📚 Documentation Site

This repository includes a full MkDocs Material documentation site with search, dark mode, and comprehensive navigation.

Local Preview

# Install documentation dependencies
pip install -r requirements-docs.txt

# Start local documentation server
mkdocs serve

Then open http://127.0.0.1:8000 in your browser.

Quick References

Resource	Description
⚡ 5-Minute Quick Start	Fastest path to generating data and exploring the POC
📋 Cheat Sheet	Commands, shortcuts, and quick reference for all components

Build Documentation

# Build static site
mkdocs build

# Deploy to GitHub Pages
mkdocs gh-deploy

Live Site: Coming soon via GitHub Pages

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📜 Compliance Frameworks

This POC addresses regulatory requirements across gaming jurisdictions:

<table> <tr> <td align="center" width="25%"> <h3>🏛️ NIGC MICS</h3> _{Minimum Internal Control Standards}<br/> _{Gaming machine & table game controls} </td> <td align="center" width="25%"> <h3>🏦 FinCEN BSA</h3> _{Bank Secrecy Act}<br/> _{CTR/SAR reporting thresholds} </td> <td align="center" width="25%"> <h3>💳 PCI-DSS</h3> _{Payment Card Industry}<br/> _{Card data security standards} </td> <td align="center" width="25%"> <h3>🏴 State Gaming</h3> _{Jurisdiction Requirements}<br/> _{State-specific regulations} </td> </tr> </table>

[!TIP] Phase 7 also addresses HIPAA (Tribal Healthcare), FedRAMP (DOT/FAA), 42 CFR Part 2 (Behavioral Health), and FISMA/NIST 800-53 compliance requirements.

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🏛️ Completed Expansions

Phase 7 delivered industry expansions beyond the core Casino/Gaming POC:

Expansion	Compliance	Key Capabilities	Tutorial
🌾 USDA	NASS, FSIS	Crop production, food safety recalls	Tutorial 32
💼 SBA	PPP, 7(a)	Loan analytics, 20 NAICS codes	Tutorial 33
🌊 NOAA	CDO API	Weather observations, storm events	Tutorial 34
🏭 EPA	AirNow, TRI	Air quality (AQI), water quality (MCL)	Tutorial 35
🏔️ DOI	USGS, BLM	Earthquakes, land use management	Tutorial 36
🏥 Tribal Healthcare	HIPAA, 42 CFR	IHS encounters, PHI masking, FHIR	Tutorial 30
✈️ DOT/FAA	FedRAMP, FISMA	Flight ops, safety, carrier analytics	Tutorial 31
⚖️ DOJ	FBI NIBRS, USSC	Crime stats, sentencing, antitrust, DEA	Tutorial 38
📹 Video Analytics	—	YOLO/DeepSORT, 50 cameras, 8 event types	Tutorial 27
🚶 People Movement	—	30 zones, queue detection, heat maps	Tutorial 28
📍 Geolocation	—	H3 indexing, geofencing, proximity triggers	Tutorial 29

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🤝 Contributing

We welcome contributions! Please read our Contributing Guide before submitting pull requests.

<table> <tr> <td>

Ways to Contribute:

• 🐛 Report bugs and issues
• 💡 Suggest new features
• 📝 Improve documentation
• 🔧 Submit pull requests

</td> <td>

Get Started:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

</td> </tr> </table>

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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