ETL Studio

Build & Testing

---

Technology Stack

Lightweight Streamlit skeleton for a collaborative data platform. This repo only contains scaffolding so each team member can focus on their layer (ETL, ML, or UX) without stepping on each other's toes.

Getting started

Run with Docker Compose (Recommended)

Start all services (PostgreSQL, API, and Streamlit) with a single command:

docker-compose up --build

This will start:

• PostgreSQL on port 5432
• MLflow Server on port 5000
• FastAPI on port 80
• Streamlit on port 8501

Access the applications:

Streamlit UI: http://localhost:8501 MLflow UI: http://localhost:5000 FastAPI Docs: http://localhost:80/docs

To stop all services:

docker-compose down

To stop and clean up volumes (reset database):

docker-compose down -v

Local Development

For local development without Docker:

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
streamlit run src/etl_studio/app/main.py

Run the tests

pytest