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<div align="center"> <img height="60" src="https://amphi.ai/icons/amphi_logo_paths.svg"> Visual Data Transformation Powered by Python Designed for data preparation, reporting, and ETL. <a href="https://github.com/amphi-ai/amphi-etl/stargazers" target="_blank"> <img src="https://img.shields.io/github/stars/amphi-ai/amphi-etl?style=social&label=Star&maxAge=2592000" alt="Test"> </a> <a href="https://join.slack.com/t/amphi-ai/shared_invite/zt-2ci2ptvoy-FENw8AW4ISDXUmz8wcd3bw" target="_blank"> <img src="https://img.shields.io/badge/slack-join-white.svg?logo=slack" alt="Slack"> </a> <a href="https://github.com/amphi-ai/amphi-etl/blob/main/LICENSE" target="_blank"> <img src="https://img.shields.io/static/v1?label=license&message=ELv2&color=white" alt="License"> </a>

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English · Try the demo · Report Bug · Request Feature

</div> <details> <summary><kbd>Table of contents</kbd></summary>

📦 Installation

Amphi is available as both a standalone applicatiion or as a JupyterLab extension.

Amphi ETL (standalone)	Amphi for JupyterLab (extension)

`pip install amphi-etl`	`pip install jupyterlab-amphi`
`pip install --upgrade amphi-etl`	`pip install --upgrade jupyterlab-amphi`

[!NOTE]

If you prefer to install Amphi's Jupyterlab extension through the extension manager, make sure to install jupyerlab-amphi package

🔨 Usage

To start Amphi ETL (standalone), simply run:

amphi start

Use the following parameters to specify your:

workspace (where you can access files and create pipelines on your system),
IP address to expose
port to use

Deploy on your local machine

amphi start -w /your/workspace/path

Deploy on a server

For deploying on a server, you need to specify -i 0.0.0.0 to expose Amphi and access it through the internet. Optionaly specify a different port.

amphi start -w /your/workspace/path -i 0.0.0.0 -p 8888

📚 Documentation
🚀 Getting Started

To update Amphi ETL run the following:

pip install --upgrade amphi-etl

✨ Features

[!NOTE]

Amphi focuses on data transformation for data preparation, reporting and ETL. It aims to empower data analysts, scientists and data engineers to easily develop pipelines with an intuitive low-code interface while generating Python code you can deploy anywhere.

Data Transformation solution for the AI age:

Modern ETL for the AI age:

🧑‍💻 Visual Interface / Low-code: Accelerate data pipeline development and reduce maintenance time.
🐍 Python-code Generation: Generate native Python code leveraging common libraries such as pandas, DuckDB that you can run anywhere.
🔒 Private and Secure: Self-host Amphi on your laptop or in the cloud for complete privacy and security over your data.

generate-python-code-amphi

Features In Progress

Custom components - Add the ability to develop your own component and wrap configured ones
Implement connections - ~~Add the ability to securely create connections to reuse in components~~
Developer documentation - Write comprehensive documentation to allow extensions
Save Components - ~~Save components and reuse them in other pipelines~~

🤝 Contributing

Use and Innovate: Try Amphi and share your use case with us. Your real-world usage and feedback help us improve our product.
Voice Your Insights: Encounter a glitch? Have a query? Share them by submitting issues and help us enhance the user experience.
Shape the Future: Have code enhancements or feature ideas? We invite you to propose pull requests and contribute directly.

Every contribution, big or small, is celebrated. Join us in our mission to refine and elevate the world of ETL for data and AI. 😃

🛣️ Ecosystem

Amphi is available as an extension for Jupyterlab, and Amphi ETL is based on Jupyterlab. Therefore Jupyterlab extensions can be installed on Amphi ETL.

Jupyterlab - JupyterLab computational environment.
jupyterlab-git - A Git extension for JupyterLab.