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Arkaid - Game Performance Analytics Platform

Dashboard

Arkaid is a comprehensive game performance analytics platform developed as part of the Information Integration Architecture course (IIA - CSE656). It employs a data warehouse approach to analyze and integrate gaming data from multiple sources, providing insights through an AI-powered interface.

Project Overview

Arkaid combines data from multiple gaming platforms and sources, utilizing ETL processes and materialized views to create a unified analytical platform. The project features natural language query capabilities powered by TogetherAI, enabling users to easily access and analyze gaming performance data.

Key Features

  • Multi-Source Data Integration: Combines data from Steam and Epic Games platforms
  • Advanced Analytics: Game performance metrics, player behavior analysis, and content creator insights
  • Natural Language Queries: AI-powered conversion of natural language to SQL queries
  • Real-time Data Processing: ETL pipelines for continuous data updates
  • Interactive Interface: Web-based interface for data exploration and analysis

Architecture

The project is organized into three main modules:

1. Data Generation

Located in /Data_Generation

  • Generates and manages gaming-related datasets
  • Creates realistic test data for development and testing
  • Handles data for games, players, developers, publishers, and content creators
  • More details

2. ETL (Extract, Transform, Load)

Located in /ETL

  • Manages data flow between different databases
  • Creates and maintains materialized views
  • Handles schema matching and data transformation
  • Ensures data consistency and integrity
  • More details

3. Interface

Located in /Interface

  • Provides web-based user interface
  • Integrates TogetherAI for natural language processing
  • Executes and visualizes query results
  • Manages database connections and query optimization
  • More details

Database Structure

The project utilizes three PostgreSQL databases:

  1. DB1: Epic Games data source
  2. DB2: Steam data source
  3. DB3: Centralized warehouse with materialized views

Technologies Used

  • Backend: Python, PostgreSQL
  • ETL: Custom Python ETL framework
  • Frontend: Flask, HTML/CSS
  • AI Integration: TogetherAI API
  • Data Processing: pandas, numpy
  • Database: psycopg2, SQLAlchemy

Setup

  1. Clone the repository:
git clone https://github.com/lakshaybhushan/Arkaid-IIA.git
cd Arkaid-IIA
  1. Set up each module:
# Set up Data Generation
cd Data_Generation
pip install -r requirements.txt

# Set up ETL
cd ../ETL
cp .env.example .env
# Edit .env with your database credentials

# Set up Interface
cd ../Interface
cp .env.example .env
# Edit .env with your database and TogetherAI API credentials
  1. Configure databases:
cd Interface
python db_config_generator.py
python connection_tester.py
  1. Start the application:
# In the Interface directory
python app.py

Usage

  1. Data Generation:

    • Generate test data using the scripts in the Data_Generation module
    • Update and maintain data sources as needed

  2. ETL Processes:

    • Run ETL scripts to process and transform data
    • Manage materialized views for optimized queries

  3. Interface:

    • Access the web interface at http://localhost:4321
    • Use natural language to query the database
    • Explore predefined queries and visualizations

Contributors