Strava Analytics Engineering

A data engineering project that automates the extraction, transformation, and loading (ETL) of Strava activity data into Supabase for analytics and visualization.

Features

• Automated Data Pipeline: Fetches activities from Strava using their API.
• Data Transformation: Cleans and processes data using Polars.
• Database Integration: Stores processed data in Supabase (PostgreSQL backend).
• FastAPI Webhook: Enables real-time data updates when new activities are recorded.
• Data Visualization: Connects Supabase to Looker Studio for reporting.

Tech Stack

• Python: Core scripting language
• FastAPI: Webhook for real-time updates
• Polars: High-performance data transformation
• SQLAlchemy: Database interaction
• Supabase: PostgreSQL database
• Render: Deployment platform
• pytest: Testing code
• uv: Project and package management
• ngrok: For testing the FastAPI Webhook API locally

Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/strava-analytics-engineering.git
cd strava-analytics-engineering

2. Install Dependencies

Use uv for package mangement:

uv venv
source .venv/bin/activate  # On Windows, use '.venv\Scripts\activate'
uv pip install -r requirements.txt

3. Set Up Environment Variables

Create a .env file and add:

CLIENT_ID=your_strava_client_id
CLIENT_SECRET=your_strava_client_secret
REFRESH_TOKEN=your_strava_refresh_token
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_api_key
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_HOST=your_db_host
DB_PORT=your_db_port
DB_NAME=your_db_name

4. Run the ETL Pipeline

python local_etl.py

5. Start the FastAPI Webhook

uvicorn webhook:app --host 0.0.0.0 --port 8000

API Endpoints

• POST /webhook: Receives Strava activity updates.

Test webhook locally using ngrok

ngrok http 8000

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License

This project is licensed under the MIT License.