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🎮 Learn Microsoft Fabric by Playing - Gamified tutorials and demos

Project description

🎮 Fabric Arcade

Learn Microsoft Fabric by Playing - A gamified catalog of projects to learn Real-Time Intelligence, Data Engineering, Power BI and Data Science through fun experiences.

PyPI version Python License: MIT


🚀 Quick Start (Fabric Notebook)

Just 3 lines of code to install a complete learning environment in your Fabric workspace!

# Cell 1 - Install the package
%pip install -q fabric-arcade
# Cell 2 - Import and explore
from fabric_arcade import arcade

# List all available games
arcade.list()
# Cell 3 - Install a game in your current workspace!
arcade.install("fabric-racing-game")

That's it! The game assets (Eventhouse, KQL Database, tables, notebooks) are automatically created in your workspace.


🎯 What You Learn

Instead of boring technical tutorials, you learn by building:

Game You Learn Workloads
🏎️ Fabric Racing Game Custom Endpoints, JSON mapping, streaming dashboards RTI
🏛️ Calc Groups Cathedral Calculation Groups on Direct Lake semantic models PBI
🏙️ City Builder Warehouse modeling & medallion analytics DW, DE
🕵️ Ontology Detective Digital Twin Builder ontologies & relationships RTI
🧙‍♂️ Monster Breach Pipelines, Dataflows & real-time defense DE, DF, RTI

📋 Requirements

Requirement Detail
Fabric Capacity F2 or higher (trial works!)
Workspace Any workspace where you have Contributor access

No local installation needed - everything runs inside Fabric notebooks!


🎮 API Reference

arcade.list()

Display all available games with their difficulty and duration.

arcade.info(game_id)

Show detailed information about a specific game.

arcade.info("fabric-racing-game")

arcade.install(game_id, workspace_id=None)

Install a game in a workspace. If workspace_id is not provided, uses the current notebook's workspace.

# Install in current workspace
arcade.install("fabric-racing-game")

# Install in a specific workspace
arcade.install("fabric-racing-game", workspace_id="your-workspace-guid")

🎲 Game Catalog

Game Type Difficulty Duration Status
🏎️ Fabric Racing Game Mission ⭐⭐ 30 min ✅ Available
🏛️ Calc Groups Cathedral Puzzle ⭐⭐⭐ 60 min ✅ Available
🏙️ City Builder Mission ⭐⭐⭐ 50 min ✅ Available
🕵️ Ontology Detective Mission ⭐⭐⭐ 45 min ✅ Available
🧙‍♂️ Monster Breach Quest ⭐⭐⭐ 60 min ✅ Available
🕹️ Retro Arcade Arcade ⭐⭐ 45 min ✅ Available
🔮 Oracle's Forge Mission ⭐⭐⭐⭐ 75 min 🔜 Coming Soon
🛡️ Sentinel Grid Mission ⭐⭐⭐ 🔜 Coming Soon
🧪 Purity Protocol Challenge ⭐⭐⭐ 🔜 Coming Soon
🌀 Portal Nexus Mission ⭐⭐⭐ 🔜 Coming Soon
🔐 Vault Keeper Mission ⭐⭐⭐ 45 min 🔜 Coming Soon
🦁 The Sphinx Challenge ⭐⭐⭐ 40 min 🔜 Coming Soon

Workload Legend:

  • RTI = Real-Time Intelligence (Eventstream, Eventhouse, KQL)
  • DE = Data Engineering (Spark, Lakehouse, Notebooks)
  • DS = Data Science (ML Models, Predictions)
  • DF = Data Factory (Pipelines, Dataflows)
  • PBI = Power BI (Reports, Dashboards)

🛠️ Local Development (CLI)

For contributors or local testing:

# Clone and install
git clone https://github.com/maenglar78/fabric-arcade.git
cd fabric-arcade
pip install -e .

# Login to Azure
az login

# Use CLI
arcade list
arcade install fabric-racing-game -w "My Workspace"

🤝 Contributing

Want to create a new game? See CONTRIBUTING.md.

Project Structure

catalog/
└── my-new-game/
    ├── manifest.json       # Game metadata
    ├── notebooks/          # Fabric notebooks
    ├── schemas/            # KQL table schemas
    └── eventstream/        # Eventstream definitions

📜 License

MIT License - see LICENSE for details.


Made with ❤️ for the Fabric Community

"Data is more fun when you're playing with it!"

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