Home > POC Agenda > Day 3: BI, Governance & Mirroring

📅 Day 3: BI, Governance & Database Mirroring¶

Last Updated: 2026-04-15 | Version: 2.0 Status: ✅ Final | Maintainer: Documentation Team

🏠 Home > 📆 POC Agenda > 📊 Day 3

Date: [INSERT DATE] Duration: 8 hours (9:00 AM - 5:00 PM) Focus: Direct Lake Power BI, Purview governance, Database Mirroring Audience: Data Architects (4) + BI Developers (2) + Data Governance (4+)

📊 Day 3 Progress Tracker¶

Day 3 Progress: ░░░░░░░░░░ 0% Complete
────────────────────────────────────────────
🌅 Morning Session 1:   ░░░░░░░░░░  Direct Lake & Power BI
🌅 Morning Session 2:   ░░░░░░░░░░  Power BI Reports
☀️ Afternoon Session 3: ░░░░░░░░░░  Purview Governance
☀️ Afternoon Session 4: ░░░░░░░░░░  Database Mirroring

📋 Materials Checklist¶

Before starting, ensure the following are ready:

Prerequisites from Day 2¶

• Gold layer tables populated and optimized
• Real-time analytics operational
• All participants have workspace access

Day 3 Materials¶

• DAX measure templates prepared
• Report layout templates available
• Purview account configured
• SQL Server source (if available for mirroring demo)

Expanded Audience¶

• BI Developers notified for 9:00 AM start
• Governance team notified for 1:30 PM start
• All stakeholders notified for final session

🗓️ Daily Schedule At-a-Glance¶

Day 3 Timeline¶

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timeline
    title Day 3: BI, Governance & Mirroring Journey

    section Morning Session 1<br/>9:00-10:30<br/>👥 Data Architects + BI Devs (6)
        Create Semantic Model : Direct Lake mode
                               : No refresh needed
        Define Relationships : Star schema
                            : Player-Slot linkage
        Implement DAX : Casino KPIs
                      : Time intelligence
                      : 20+ measures

    section Morning Session 2<br/>10:45-12:30<br/>👥 Data Architects + BI Devs (6)
        Executive Dashboard : Net Win
                           : Hold %
                           : Zone Performance
        Slot Operations Report : Performance matrix
                              : Hourly activity
                              : Hold variance
        Player Analytics : Player 360
                        : Churn risk
                        : Value scoring

    section Afternoon Session 3<br/>13:30-15:00<br/>👥 All Participants (10+)
        Connect Purview : Register Fabric source
                       : Configure scan
        Review Classifications : PII detection
                              : Financial data
                              : Auto-classification
        Business Glossary : Create terms
                         : Gaming metrics
                         : Compliance definitions
        Data Lineage : Bronze to Gold flow
                    : Impact analysis
                    : Transformation tracking

    section Afternoon Session 4<br/>15:15-16:30<br/>👥 All Participants (10+)
        Mirroring Concepts : Near real-time sync
                          : No ETL code
                          : Auto schema sync
        SQL Server Demo : Enable CDC
                       : Configure mirror
                       : Query mirrored data
        Hybrid Architecture : Multi-source strategy
                           : OneLake integration
                           : Production patterns

    section Wrap-up<br/>16:30-17:00<br/>🎉 All Participants
        POC Review : 3 days complete
                  : Medallion built
                  : Reports delivered
        Next Steps : Production planning
                  : Week 1-4 roadmap
                  : Go/No-Go decision

Key Milestones: - ☕ 9:00 AM: BI Developers join (6 total participants) - 🍽️ 12:30 PM: Lunch break - 👥 1:30 PM: Governance team joins (10+ participants) - 🎉 4:30 PM: POC completion celebration!

👥 Audience Expansion Schedule¶

┌─────────────────────────────────────────────────────────────────┐
│  9:00 AM ────────────────────────────────────────────── 5:00 PM │
├─────────────────────────────────────────────────────────────────┤
│  Data Architects (4)  ████████████████████████████████████████  │
│  BI Developers (2)    ████████████████████████████████████████  │
│  Governance Team (4+) ░░░░░░░░░░░░░░░░██████████████████████████│
│                                       ↑                         │
│                                    1:30 PM                      │
└─────────────────────────────────────────────────────────────────┘

🌅 Morning Session 1: Direct Lake & Power BI (9:00 - 10:30)¶

🎯 Session Objectives¶

📝 Activity 9.1: Create Semantic Model (30 min)¶

Source: Direct Lake Overview

Steps:

1. Open lh_gold Lakehouse
2. Click New semantic model
3. Select tables:
4. gold_slot_performance
5. gold_player_360
6. gold_compliance_reporting
7. Name: Casino Analytics Model

Verify Direct Lake Mode:

• Open model settings
• Confirm "Storage mode: Direct Lake"
• Note: No scheduled refresh needed

💡 Direct Lake Advantage: Data is read directly from Delta tables with no import or refresh required.

📝 Activity 9.2: Define Relationships (15 min)¶

Create star schema relationships:

┌──────────────────┐
│   dim_date       │
│   (date_key)     │
└────────┬─────────┘
         │
         │ 1:M
         ▼
┌──────────────────────┐     ┌──────────────────┐
│ gold_slot_performance│────▶│   dim_machine    │
│   (business_date)    │ M:1 │   (machine_id)   │
└──────────────────────┘     └──────────────────┘
         │
         │ 1:M
         ▼
┌──────────────────────┐
│  gold_player_360     │
│    (player_id)       │
└──────────────────────┘

📝 Activity 9.3: Create DAX Measures (45 min)¶

Slot Performance Measures¶

// Total Coin In
Total Coin In = SUM(gold_slot_performance[total_coin_in])

// Total Coin Out
Total Coin Out = SUM(gold_slot_performance[total_coin_out])

// Net Win
Net Win = [Total Coin In] - [Total Coin Out]

// Hold Percentage
Hold % = DIVIDE([Net Win], [Total Coin In], 0) * 100

// Theoretical Win (assuming 8% hold)
Theoretical Win = [Total Coin In] * 0.08

// Hold Variance
Hold Variance = [Net Win] - [Theoretical Win]

// Win Per Machine Per Day
Win Per Machine =
DIVIDE(
    [Net Win],
    DISTINCTCOUNT(gold_slot_performance[machine_id]),
    0
)

// Games Played
Total Games = SUM(gold_slot_performance[total_games])

// Average Bet
Avg Bet = DIVIDE([Total Coin In], [Total Games], 0)

Player Measures¶

// Total Players
Total Players = COUNTROWS(gold_player_360)

// VIP Players
VIP Players =
CALCULATE(
    COUNTROWS(gold_player_360),
    gold_player_360[loyalty_tier] IN {"Platinum", "Diamond"}
)

// High Churn Risk
High Churn Risk Players =
CALCULATE(
    COUNTROWS(gold_player_360),
    gold_player_360[churn_risk] = "High"
)

// Average Player Value
Avg Player Value = AVERAGE(gold_player_360[player_value_score])

// Active Players (30 days)
Active Players 30D =
CALCULATE(
    COUNTROWS(gold_player_360),
    gold_player_360[days_since_visit] <= 30
)

Time Intelligence Measures¶

// Coin In MTD
Coin In MTD = TOTALMTD([Total Coin In], dim_date[date_key])

// Coin In YTD
Coin In YTD = TOTALYTD([Total Coin In], dim_date[date_key])

// Net Win vs Prior Month
Net Win PM =
CALCULATE(
    [Net Win],
    DATEADD(dim_date[date_key], -1, MONTH)
)

// Net Win Growth %
Net Win Growth % =
DIVIDE(
    [Net Win] - [Net Win PM],
    [Net Win PM],
    0
) * 100

// 7-Day Rolling Average
Coin In 7D Avg =
AVERAGEX(
    DATESINPERIOD(dim_date[date_key], MAX(dim_date[date_key]), -7, DAY),
    [Total Coin In]
)

🌅 Morning Session 2: Power BI Reports (10:45 - 12:30)¶

🎯 Session Objectives¶

📝 Activity 10.1: Executive Dashboard (45 min)¶

Page Layout:

┌────────────────────────────────────────────────────────────┐
│  CASINO EXECUTIVE DASHBOARD              [Date Slicer]     │
├──────────┬──────────┬──────────┬──────────┬───────────────┤
│ Net Win  │ Hold %   │ Players  │ Games    │ [Zone Slicer] │
│  $2.5M   │   8.2%   │  12,450  │  1.2M    │               │
├──────────┴──────────┴──────────┴──────────┴───────────────┤
│                    NET WIN TREND                           │
│  [Line Chart - Daily Net Win with comparison]             │
├───────────────────────────┬────────────────────────────────┤
│   ZONE PERFORMANCE        │   TOP 10 MACHINES              │
│   [Bar Chart]             │   [Table]                      │
├───────────────────────────┼────────────────────────────────┤
│   PLAYER TIER MIX         │   COMPLIANCE SUMMARY           │
│   [Donut Chart]           │   CTR: 45  SAR: 12  W2G: 234  │
└───────────────────────────┴────────────────────────────────┘

Create Visuals:

📝 Activity 10.2: Slot Operations Report (45 min)¶

Page Layout:

┌────────────────────────────────────────────────────────────┐
│  SLOT OPERATIONS                    [Date] [Zone] [Denom] │
├────────────────────────────────────────────────────────────┤
│                  PERFORMANCE MATRIX                        │
│  [Matrix: Zone rows x Denomination columns]               │
│  Values: Coin In, Net Win, Hold %, Games                  │
├───────────────────────────┬────────────────────────────────┤
│   HOURLY ACTIVITY         │   HOLD VARIANCE ANALYSIS      │
│   [Area Chart by hour]    │   [Scatter: Theo vs Actual]   │
├───────────────────────────┼────────────────────────────────┤
│   MANUFACTURER BREAKDOWN  │   JACKPOT SUMMARY             │
│   [Clustered Bar]         │   [Table with sparklines]     │
└───────────────────────────┴────────────────────────────────┘

📝 Activity 10.3: Player Analytics Report (30 min)¶

Page Layout:

┌────────────────────────────────────────────────────────────┐
│  PLAYER ANALYTICS                         [Tier Slicer]   │
├──────────┬──────────┬──────────┬──────────────────────────┤
│  Total   │   VIP    │ At Risk  │  Avg Value Score        │
│ Players  │ Players  │ (Churn)  │                          │
├──────────┴──────────┴──────────┴──────────────────────────┤
│           PLAYER VALUE DISTRIBUTION                        │
│  [Histogram of player_value_score]                        │
├───────────────────────────┬────────────────────────────────┤
│   TIER BREAKDOWN          │   CHURN RISK BY TIER          │
│   [Stacked Column]        │   [100% Stacked Bar]          │
├───────────────────────────┴────────────────────────────────┤
│             TOP 20 PLAYERS BY THEO                         │
│  [Table: player_id, tier, theo, visits, last_visit, risk] │
└────────────────────────────────────────────────────────────┘

☀️ Afternoon Session 3: Purview Integration (13:30 - 15:00)¶

🎯 Session Objectives¶

👥 Audience Expansion: All participants (10+) join for this session

📝 Activity 11.1: Connect Purview to Fabric (20 min)¶

Source: Microsoft Purview Governance Portal

Steps:

1. Open Microsoft Purview Portal
2. Navigate to Data Map > Sources
3. Click Register > Microsoft Fabric
4. Configure:
5. Name: Fabric-Casino-POC
6. Tenant: Select your tenant
7. Workspace: casino-fabric-poc

📝 Activity 11.2: Create and Run Scan (20 min)¶

Steps:

1. On registered source, click New scan
2. Configure:
3. Name: fabric-full-scan
4. Scope: All Lakehouses
5. Scan rule set: System default
6. Set trigger: Run once now
7. Click Save and Run

📝 Activity 11.3: Review Auto-Classifications (20 min)¶

Navigate to scanned tables and verify:

📝 Activity 11.4: Create Glossary Terms (30 min)¶

Business Glossary Hierarchy:

Casino Operations
├── Gaming Metrics
│   ├── Coin In
│   ├── Coin Out
│   ├── Net Win
│   ├── Hold Percentage
│   └── Theoretical Win
├── Player Management
│   ├── Player Theo
│   ├── Player LTV
│   └── Churn Risk
└── Compliance
    ├── CTR (Currency Transaction Report)
    ├── SAR (Suspicious Activity Report)
    └── W-2G

Sample Term: Coin In

Name: Coin In
Definition: Total amount wagered by players on gaming devices.
            Represents the handle or total bets placed.
Acronym: CI
Status: Approved
Steward: Casino Analytics Team
Related Terms: Coin Out, Net Win, Hold Percentage
Resources: NIGC MICS Section 543.24

📝 Activity 11.5: View Data Lineage (20 min)¶

1. Navigate to gold_slot_performance in catalog
2. Click Lineage tab
3. Observe flow:

bronze_slot_telemetry
        ↓
silver_slot_cleansed
        ↓
gold_slot_performance
        ↓
Casino Analytics Model
        ↓
Executive Dashboard

1. Click on each node to see transformation details
2. Use Impact Analysis to understand downstream effects

☀️ Afternoon Session 4: Database Mirroring (15:15 - 17:00)¶

🎯 Session Objectives¶

📝 Activity 12.1: Mirroring Overview (20 min)¶

What is Database Mirroring?

Database mirroring in Microsoft Fabric provides near real-time replication of operational databases to OneLake.

Source: CI/CD for Data Pipelines

Source Database → Change Data Capture → OneLake → Delta Tables
      │                    │                │           │
   SQL Server         Continuous         Automatic   Query with
   Snowflake          Replication        Conversion   Spark/SQL
   Cosmos DB

Benefits:

📝 Activity 12.2: SQL Server Mirroring Demo (40 min)¶

Prerequisites (if available): - SQL Server 2016+ or Azure SQL Database - CDC enabled on source

Enable CDC on Source:

-- Enable CDC on database
EXEC sys.sp_cdc_enable_db;

-- Enable CDC on tables
EXEC sys.sp_cdc_enable_table
    @source_schema = N'gaming',
    @source_name = N'slot_transactions',
    @role_name = NULL,
    @supports_net_changes = 1;

Create Mirrored Database in Fabric:

1. Open workspace casino-fabric-poc
2. Click + New > Mirrored database
3. Select Azure SQL Database
4. Configure connection:
5. Server: your-sql-server.database.windows.net
6. Database: CasinoOperational
7. Authentication: SQL or Microsoft Entra ID
8. Select tables to mirror
9. Click Mirror database

Monitor Replication:

1. Open mirrored database
2. View Replication status
3. Check:
4. Initial snapshot progress
5. Ongoing replication lag
6. Table sync status

📝 Activity 12.3: Query Mirrored Data (20 min)¶

# Read mirrored table
df = spark.table("lh_mirrored.slot_transactions")

# Join with Gold layer
df_enriched = df.join(
    spark.table("lh_gold.gold_player_360"),
    "player_id",
    "left"
)

# Real-time analysis
df.filter(col("transaction_time") > "2024-01-01") \
  .groupBy("machine_id") \
  .agg(sum("amount").alias("total_amount")) \
  .orderBy(col("total_amount").desc()) \
  .show(10)

📝 Activity 12.4: Snowflake & Cosmos DB Overview (20 min)¶

Snowflake Mirroring: - Connect to Snowflake account - Select databases/schemas/tables - Automatic data type mapping

Cosmos DB Mirroring: - Uses Change Feed - Handles nested documents - Arrays become separate rows - Schema inference automatic

Hybrid Architecture Pattern:

┌─────────────────────────────────────────────────────────────┐
│                     HYBRID ARCHITECTURE                      │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│   Operational Sources              Fabric OneLake           │
│                                                              │
│   ┌──────────┐                    ┌──────────────┐          │
│   │SQL Server│─── Mirroring ────▶│ lh_mirrored  │          │
│   └──────────┘                    └──────┬───────┘          │
│                                          │                   │
│   ┌──────────┐                    ┌──────▼───────┐          │
│   │Snowflake │─── Mirroring ────▶│  Medallion   │          │
│   └──────────┘                    │  Architecture│          │
│                                   └──────┬───────┘          │
│   ┌──────────┐                          │                   │
│   │Cosmos DB │─── Mirroring ────▶       ▼                   │
│   └──────────┘                    ┌──────────────┐          │
│                                   │ Power BI     │          │
│                                   │ Direct Lake  │          │
│                                   └──────────────┘          │
│                                                              │
└─────────────────────────────────────────────────────────────┘

✅ Day 3 Validation Checklist¶

Direct Lake & Power BI¶

Purview Governance¶

Database Mirroring¶

🏆 POC Completion Summary¶

What We Built¶

Architecture Delivered¶

flowchart TB
    subgraph Sources
        SQL[SQL Server]
        Files[Landing Files]
        Stream[Real-Time]
    end

    subgraph Fabric
        Bronze[Bronze Layer]
        Silver[Silver Layer]
        Gold[Gold Layer]
        RT[Eventhouse]
        DL[Direct Lake]
        PBI[Power BI]
    end

    subgraph Governance
        Purview[Microsoft Purview]
    end

    SQL --> Bronze
    Files --> Bronze
    Stream --> RT
    Bronze --> Silver
    Silver --> Gold
    Gold --> DL
    RT --> DL
    DL --> PBI

    Bronze & Silver & Gold --> Purview

Key Metrics Demonstrated¶

🚀 Next Steps & Recommendations¶

Immediate (Week 1-2)¶

Short-Term (Month 1)¶

Long-Term (Quarter 1)¶

📚 Resources & Documentation¶

Tutorials¶

Microsoft Documentation¶

Support Contacts¶

📖 Related Documents¶

Day 3:       ██████████ 100% Complete
Overall POC: ██████████ 100% Complete

🎉 Congratulations! 🎉