- Notifications
You must be signed in to change notification settings - Fork828
🧙 Build, run, and manage data pipelines for integrating and transforming data.
License
mage-ai/mage-ai
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
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.
 For teams. Fully managed platform for integrating and transforming data. |  Self-hosted. System to build, run, and manage data pipelines. |
For documentation on getting started, how to develop, and how to deploy to production check out the live
Developer documentation portal.
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? Thefastest way to get started is by checking out our documentationhere.
Looking for quick examples? Open ademo project right in your browser or check out ourguides.
Build and run a data pipeline with ourdemo app.
WARNING
The live demo is public to everyone, please don’t save anything sensitive (e.g. passwords, secrets, etc).
Click the image to play video
| 🎶 | 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 ➝
- Load data ➝
@data_loaderdefload_csv_from_file()->pl.DataFrame:returnpl.read_csv('default_repo/titanic.csv')
- Transform data ➝
@transformerdefselect_columns_from_df(df:pl.DataFrame,*args)->pl.DataFrame:returndf[['Age','Fare','Survived']]
- Export data ➝
@data_exporterdefexport_titanic_data_to_disk(df:pl.DataFrame)->None:df.to_csv('default_repo/titanic_transformed.csv')
About
🧙 Build, run, and manage data pipelines for integrating and transforming data.