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Mage

Build pipelines in a notebook IDE — the modern tool for data engineers and scientists.

0Open Source
Workflow Orchestration
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Overview

Mage is an open-source data pipeline tool for building, running, and managing data workflows with a built-in notebook-style IDE, making it easy for data engineers and scientists to collaborate on ETL, ML, and analytics pipelines.

Key Features

  • Built-in browser-based IDE for coding pipelines interactively
  • Block-based pipeline design (Data Loader, Transformer, Data Exporter)
  • Real-time kernel execution with instant block output preview
  • Backfill, trigger, and schedule pipelines from the UI
  • Streaming pipeline support for Kafka, Kinesis, and websockets
  • Mage Pro cloud platform for team collaboration and managed infra

Real-World Workflows

Interactive data pipeline development

  1. 1Launch Mage locally with Docker and open the browser IDE
  2. 2Create a new pipeline and add a Data Loader block for Postgres
  3. 3Add a Transformer block to clean and reshape the data
  4. 4Add a Data Exporter block to write to Snowflake or S3
  5. 5Run and validate the pipeline end-to-end in the IDE

ML feature engineering pipeline

  1. 1Load raw feature data from S3 with a Data Loader block
  2. 2Write feature transformations in Python Transformer blocks
  3. 3Save outputs as Parquet features to a feature store
  4. 4Schedule daily runs and monitor in Mage's pipeline view

Getting Started

# Start Mage with Docker
docker pull mageai/mageai:latest
docker run -it \
  -p 6789:6789 \
  -v $(pwd):/home/src \
  mageai/mageai:latest mage start my_project

# Open at http://localhost:6789

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