Multi-Tenant Analytics Platform

A multi-tenant SaaS analytics platform built with Flask, enabling organizations to upload data, visualize metrics, and share embeddable dashboards — all with real-time updates powered by WebSockets.

Features

• Multi-tenant architecture — isolated data and dashboards per tenant
• Authentication — secure login and registration with hashed passwords via Flask-Bcrypt
• Data uploads — upload CSV/Excel files for analysis (up to 16MB)
• Analytics dashboard — visualize metrics and data sources per tenant
• Embeddable widgets — embed dashboards into external sites via the embed module
• Developer API — REST API for programmatic access to tenant data
• Real-time notifications — live updates via Flask-SocketIO and WebSockets
• Reports — generate and export reports using ReportLab
• Profile & settings — per-tenant profile and configuration management

Tech Stack

Layer	Technology
Backend	Python, Flask
Database	PostgreSQL (production), SQLite (local dev)
ORM	Flask-SQLAlchemy
Auth	Flask-Login, Flask-Bcrypt
Real-time	Flask-SocketIO, gevent
Data	pandas, numpy, openpyxl
Reports	ReportLab
Server	Gunicorn + gevent worker
Deployment	Railway (Nixpacks)

Project Structure

├── app.py                  # App factory and entry point
├── config.py               # Configuration (env vars, DB URI)
├── extensions.py           # Flask extensions (db, bcrypt, socketio)
├── login_manager.py        # Login manager setup
├── socket_events.py        # WebSocket event handlers
├── models/
│   ├── tenant.py           # Tenant (user) model
│   ├── metric.py           # Metric model
│   └── datasource.py       # DataSource model
├── routes/
│   ├── auth.py             # Login / register
│   ├── dashboard.py        # Main dashboard
│   ├── upload.py           # File uploads
│   ├── api.py              # Developer REST API
│   ├── embed.py            # Embeddable dashboard
│   ├── reports.py          # Report generation
│   ├── notifications.py    # Notification routes
│   ├── profile.py          # Profile management
│   ├── settings.py         # Settings management
│   └── developer.py        # Developer tools
├── static/                 # CSS, JS, assets
├── templates/              # Jinja2 HTML templates
├── requirements.txt
├── Procfile
└── railway.json

Local Development

Prerequisites

• Python 3.12+
• PostgreSQL (optional — SQLite used by default locally)

Setup

# Clone the repo
git clone https://github.com/charanachanta4-beep/Multi-Tenant-Analytics-Platform.git
cd Multi-Tenant-Analytics-Platform

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create a .env file
cp .env.example .env  # or create manually

Environment Variables

Create a .env file in the root:

SECRET_KEY=your_secret_key_here
DATABASE_URL=postgresql://user:password@localhost:5432/yourdb  # optional, defaults to SQLite

Run

python app.py

The app will be available at http://localhost:5000.

Deployment (Railway)

1. Add a PostgreSQL service

In your Railway project, click + New → Database → PostgreSQL. Railway will automatically provide a DATABASE_URL environment variable.

2. Link DATABASE_URL to your app

In your app service → Variables, add a reference to the Postgres service's DATABASE_URL.

3. Deploy

Push to your connected GitHub branch — Railway will build and deploy automatically using Nixpacks.

The Procfile defines the start command:

web: gunicorn -k gevent -w 1 --bind 0.0.0.0:$PORT app:app

Environment Variables Reference

Variable	Required	Description
SECRET_KEY	No	Flask secret key (defaults to a dev value)
DATABASE_URL	Yes (production)	PostgreSQL connection string

License

MIT