Metadata-Version: 2.3
Name: Flowfile
Version: 0.2.2
Summary: Project combining flowfile core (backend) and flowfile_worker (compute offloader) and flowfile_frame (api)
Author: Edward van Eechoud
Author-email: evaneechoud@gmail.com
Requires-Python: >=3.10,<3.13
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Description-Content-Type: text/markdown

<h1 align="center">
  <img src=".github/images/logo.png" alt="Flowfile Logo" width="100">
  <br>
  Flowfile
</h1>
<p align="center">
  <b>Documentation</b>:
  <a href="https://edwardvaneechoud.github.io/Flowfile/">Website</a>
  -
  <a href="flowfile_core/README.md">Core</a>
  -
  <a href="flowfile_worker/README.md">Worker</a>
  -
  <a href="flowfile_frontend/README.md">Frontend</a>
  -
  <a href="https://dev.to/edwardvaneechoud/building-flowfile-architecting-a-visual-etl-tool-with-polars-576c">Technical Architecture</a>
</p>
<p>
Flowfile is a visual ETL tool that combines drag-and-drop workflow building with the speed of Polars dataframes. Build data pipelines visually, transform data using powerful nodes, and analyze results - all without writing code.
</p>

<div align="center">
  <img src=".github/images/group_by_screenshot.png" alt="Flowfile Interface" width="800"/>
</div>

## ⚡ Technical Design

Flowfile operates as three interconnected services:

- **Designer** (Electron + Vue): Visual interface for building data flows
- **Core** (FastAPI): ETL engine using Polars for data transformations (`:63578`)
- **Worker** (FastAPI): Handles computation and caching of data operations (`:63579`)

Each flow is represented as a directed acyclic graph (DAG), where nodes represent data operations and edges represent data flow between operations.

For a deeper dive into the technical architecture, check out [this article](https://dev.to/edwardvaneechoud/building-flowfile-architecting-a-visual-etl-tool-with-polars-576c) on how Flowfile leverages Polars for efficient data processing.

## 🔥 Example Use Cases

- **Data Cleaning & Transformation**
  - Complex joins (fuzzy matching)
  - Text to rows transformations
  - Advanced filtering and grouping
  - Custom formulas and expressions
  - Filter data based on conditions

<div align="center">
  <img src=".github/images/flowfile_demo_1.gif" alt="Flowfile Layout" width="800"/>
</div>

---

- **Performance**
  - Build to scale out of core
  - Using polars for data processing

<div align="center">
  <img src=".github/images/demo_flowfile_write.gif" alt="Flowfile Layout" width="800"/>
</div>

---

### **Data Integration**
  - Standardize data formats
  - Handle messy Excel files


<div align="center">
  <img src=".github/images/read_excel_flowfile.gif" alt="Flowfile Layout" width="800"/>
</div>


---

- **ETL Operations**
  - Data quality checks


## 🚀 Getting Started

### Prerequisites
- Python 3.10+
- Node.js 16+
- Poetry (Python package manager)
- Docker & Docker Compose (option, for Docker setup)
- Make (optional, for build automation)

### Installation Options

#### 1. Desktop Application
The desktop version offers the best experience with a native interface and integrated services. You can either:

**Option A: Download Pre-built Application** 
- Download the latest release from [GitHub Releases](https://github.com/Edwardvaneechoud/Flowfile/releases)
- Run the installer for your platform (Windows, macOS, or Linux)
  - Note: You may see security warnings since the installer isn't signed. On Windows, click "More info" then "Run anyway". On macOS, right-click the app, select "Open", then confirm. These warnings appear because the app isn't signed with a developer certificate.

**Option B: Build from Source:**
```bash
git clone https://github.com/edwardvaneechoud/Flowfile.git
cd Flowfile

# Build packaged executable
make    # Creates platform-specific executable

# Or manually:
poetry install
poetry run build_backends
cd flowfile_frontend
npm install
npm run build      # All platforms
```

#### 2. Docker Setup
Perfect for quick testing, development or deployment scenarios. Runs all services in containers with proper networking and volume management:
```bash
# Clone and start all services
git clone https://github.com/edwardvaneechoud/Flowfile.git
cd Flowfile
docker compose up -d

# Access services:
Frontend: http://localhost:8080 # main service
Core API: http://localhost:63578/docs
Worker API: http://localhost:63579/docs
```
Just place your files that you want to transform in the directory in shared_data and you're all set!

Docker Compose is also excellent for development, as it automatically sets up all required services and ensures proper communication between them. Code changes in the mounted volumes will be reflected in the running containers.

#### 3. Manual Setup (Development)
Ideal for development work when you need direct access to all services and hot-reloading:

```bash
git clone https://github.com/edwardvaneechoud/Flowfile.git
cd Flowfile

# Install Python dependencies
poetry install

# Start backend services
poetry run flowfile_worker  # Starts worker on :63579
poetry run flowfile_core   # Starts core on :63578

# Start web frontend
cd flowfile_frontend
npm install
npm run dev:web  # Starts web interface on :8080
```

## 📋 TODO

### Core Features
- [ ] Add cloud storage support
  - S3 integration
  - Azure Data Lake Storage (ADLS)
- [x] Multi-flow execution support
- [ ] Polars code reverse engineering
  - Generate Polars code from visual flows
  - Import existing Polars scripts

### Documentation
- [ ] Add comprehensive docstrings
- [x] Create detailed node documentation
- [x] Add architectural documentation
- [ ] Improve inline code comments
- [ ] Create user guides and tutorials

### Infrastructure
- [ ] Implement proper testing
- [x] Add CI/CD pipeline
- [x] Improve error handling
- [x] Add monitoring and logging

## 📝 License

[MIT License](LICENSE)

## Acknowledgments

Built with Polars, Vue.js, FastAPI, Vueflow and Electron.

