mage-ai

Why we included this project

Machine-learning teams often find the data plumbing around model training eats more time than the modeling itself. Mage tackles that with a notebook-style interface where each block is a real file, so you can load and clean data, transform it step by step, see what each stage does, then chain the steps into a scheduled pipeline. It works in Python, SQL, and PySpark and brings prebuilt connectors for databases, APIs, and cloud storage, so setup doesn't turn into a side project. You can run it locally with Docker or pip, no cloud account needed, which makes it a low-friction choice for small teams that want a visual ETL workspace without buying into a hosted platform. And if you already use Airflow or Prefect for orchestration, Mage can slot in just for the transformation work rather than replacing your scheduler.