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🚀 Quick Start Guide¶

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

Get your Microsoft Fabric Casino Analytics POC running in under 15 minutes.

📌 This guide follows Path B (Quickstart) — no Azure infrastructure deployment required. You only need a Fabric capacity and workspace. For the production-aligned Path A (Bicep + ADLS shortcuts), see Tutorial 00 Step 4.

For the full walkthrough with screenshots and explanations, see Tutorial 00: Environment Setup.

📑 Table of Contents¶

• 📋 Prerequisites
• 🔧 Step 1: Clone and Configure
• ☁️ Step 2: Deploy Infrastructure
• 🏗️ Step 3: Create Workspace and Lakehouses
• 🎲 Step 4: Generate Sample Data
• 📓 Step 5: Run Your First Notebook
• 🗺️ Where to Go Next
• 🔧 Troubleshooting

📋 Prerequisites¶

No Fabric capacity yet? Start a 60-day Fabric trial for free.

🔧 Step 1: Clone and Configure¶

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

Copy the environment template and fill in your values:

cp .env.sample .env

Required .env values:

AZURE_SUBSCRIPTION_ID=<your-subscription-id>
AZURE_TENANT_ID=<your-tenant-id>
AZURE_LOCATION=eastus2
FABRIC_CAPACITY_SKU=F64
PROJECT_PREFIX=casinopoc

Checkpoint: You should be in the Suppercharge_Microsoft_Fabric/ directory with a populated .env file.

☁️ Step 2: Deploy Infrastructure¶

az login

# Register providers (first time only — takes ~2 minutes)
az provider register --namespace Microsoft.Fabric
az provider register --namespace Microsoft.Storage

# Preview what will be created
az deployment sub what-if \
  --location eastus2 \
  --template-file infra/main.bicep \
  --parameters infra/environments/dev/dev.bicepparam

# Deploy (~10 minutes)
az deployment sub create \
  --location eastus2 \
  --template-file infra/main.bicep \
  --parameters infra/environments/dev/dev.bicepparam

Checkpoint: Run az deployment sub show --name main --query properties.provisioningState — should return "Succeeded".

🏗️ Step 3: Create Workspace and Lakehouses¶

This step is done in the Fabric portal — Bicep deploys the capacity, but you create the workspace manually.

1. Open app.fabric.microsoft.com
2. Click Workspaces in the left nav, then + New workspace
3. Set the name to casino-fabric-poc, assign your Fabric capacity, click Apply
4. Inside the workspace, create three Lakehouses (+ New > Lakehouse):

Checkpoint: Your workspace item list shows lh_bronze, lh_silver, and lh_gold.

🎲 Step 4: Generate Sample Data¶

Choose one of the three paths below.

Option A: Local Python¶

# From the repo root (Suppercharge_Microsoft_Fabric/)
python -m venv .venv

# Windows
.venv\Scripts\activate
# macOS / Linux
source .venv/bin/activate

pip install -r requirements.txt

# Generate 1 day of casino data — outputs to data_generation/output/
python data_generation/generate.py --all --days 1

Option B: Docker¶

# Uses the v2 'docker compose' command (not the deprecated docker-compose)
docker compose up data-generator

Option C: Skip (use Fabric notebooks directly)¶

The Bronze notebooks can generate synthetic data inline. Skip this step and proceed to Step 5.

Checkpoint (Options A/B): The data_generation/output/ directory contains CSV/Parquet files for slots, players, and transactions.

📓 Step 5: Run Your First Notebook¶

Fabric notebooks are not imported as files. You create a new notebook and paste the code.

1. Open app.fabric.microsoft.com and navigate to the casino-fabric-poc workspace
2. Click + New > Notebook
3. In the notebook's Lakehouse explorer panel (left side), click Add and attach lh_bronze
4. Open notebooks/bronze/01_bronze_slot_telemetry.py from this repo in any text editor
5. Copy the cell contents into the notebook cells (each # COMMAND ---------- separator marks a new cell)
6. Click Run All

First-time notebook execution takes 2-3 minutes while Fabric provisions a Spark cluster. Subsequent runs are faster.

Checkpoint: After the run completes, expand Tables in the Lakehouse explorer — you should see bronze_slot_telemetry with data.

Verify with a query¶

Create a new cell in the same notebook and run:

df = spark.read.format("delta").load("Tables/bronze_slot_telemetry")
print(f"Row count: {df.count()}")
display(df.limit(5))

You should see rows with columns like machine_id, casino_id, event_type, amount, and timestamp.

🗺️ Where to Go Next¶

🔧 Troubleshooting¶

📚 Related Documentation¶

⬆️ Back to Top | 📚 Docs | 🏠 Home

📖 Documentation maintained by: Microsoft Fabric POC Team 🔗 Repository: Suppercharge_Microsoft_Fabric

Total Time: ~15 minutes (mostly waiting for Bicep deployment and Spark cold start).