Mage AI

Give your data team magical powers.

Mage is a hybrid framework for transforming and integrating data. It combines the best of both worlds: the flexibility of notebooks with the rigor of modular code.

• Extract and synchronize data from 3rd party sources.
• Transform data with real-time and batch pipelines using Python, SQL, and R.
• Load data into your data warehouse or data lake using our pre-built connectors.
• Run, monitor, and orchestrate thousands of pipelines without losing sleep.

Plus hundreds of enterprise-class features, infrastructure innovations, and magical surprises.

Available in two spellbinding versions

For teams. Fully managed platform for integrating and transforming data.

Self-hosted. System to build, run, and manage data pipelines.

It’s magic.

For documentation on getting started, how to develop, and how to deploy to production check out the live
Developer documentation portal.

🏃‍♀️ Install

The recommended way to install the latest version of Mage is through Docker with the following command:

docker pull mageai/mageai:latest

You can also install Mage using pip or conda, though this may cause dependency issues without the proper environment.

pip install mage-ai

conda install -c conda-forge mage-ai

Looking for help? The fastest way to get started is by checking out our documentation here.

Looking for quick examples? Open a demo project right in your browser or check out our guides.

🎮 Demo

Live demo

Build and run a data pipeline with our demo app.

WARNING

The live demo is public to everyone, please don’t save anything sensitive (e.g. passwords, secrets, etc).

Demo video (5 min)

_{Click the image to play video}

🔮 Features

🎶	Orchestration	Schedule and manage data pipelines with observability.
📓	Notebook	Interactive Python, SQL, & R editor for coding data pipelines.
🏗️	Data integrations	Synchronize data from 3rd party sources to your internal destinations.
🚰	Streaming pipelines	Ingest and transform real-time data.
❎	dbt	Build, run, and manage your dbt models with Mage.

A sample data pipeline defined across 3 files ➝

1. Load data ➝

   @data_loader
   def load_csv_from_file() -> pl.DataFrame:
       return pl.read_csv('default_repo/titanic.csv')

2. Transform data ➝

   @transformer
   def select_columns_from_df(df: pl.DataFrame, *args) -> pl.DataFrame:
       return df[['Age', 'Fare', 'Survived']]

3. Export data ➝

   @data_exporter
   def export_titanic_data_to_disk(df: pl.DataFrame) -> None:
       df.to_csv('default_repo/titanic_transformed.csv')