Flowfile 0.5.1

Project combining flowfile core (backend) and flowfile_worker (compute offloader) and flowfile_frame (api)

Flowfile

Main Repository: Edwardvaneechoud/Flowfile
Documentation: Website - Core - Worker - Frontend - Technical Architecture

Flowfile is a visual ETL tool and Python library suite that combines drag-and-drop workflow building with the speed of Polars dataframes. Build data pipelines visually, transform data using powerful nodes, or define data flows programmatically with Python and analyze results - all with high-performance data processing. Export your visual flows as standalone Python/Polars code for production deployment.

🚀 Getting Started

Installation

Install Flowfile directly from PyPI:

pip install Flowfile

Quick Start: Web UI

The easiest way to get started is by launching the web-based UI:

# Start the Flowfile web UI with integrated services
flowfile run ui

This will:

• Start the combined core and worker services
• Launch a web interface in your browser
• Provide access to the full visual ETL capabilities

Options:

# Customize host
flowfile run ui --host 0.0.0.0

# Start without opening a browser
flowfile run ui --no-browser

You can also start the web UI programmatically:

import flowfile

# Start with default settings
flowfile.start_web_ui()

# Or customize
flowfile.start_web_ui(open_browser=False)

Using the FlowFrame API

Flowfile provides a Polars-like API for defining data pipelines programmatically:

import flowfile as ff
from flowfile import col, open_graph_in_editor

# Create a data pipeline
df = ff.from_dict({
    "id": [1, 2, 3, 4, 5],
    "category": ["A", "B", "A", "C", "B"],
    "value": [100, 200, 150, 300, 250]
})

# Process the data
result = df.filter(col("value") > 150).with_columns([
    (col("value") * 2).alias("double_value")
])

# Open the graph in the web UI (starts the server if needed)
open_graph_in_editor(result.flow_graph)

📦 Package Components

The Flowfile PyPI package includes:

• Core Service (flowfile_core): The main ETL engine using Polars
• Worker Service (flowfile_worker): Handles computation-intensive tasks
• Web UI: Browser-based visual ETL interface
• FlowFrame API (flowfile_frame): Polars-like API for Python coding

✨ Key Features

Visual ETL with Web UI

• No Installation Required: Launch directly from the pip package
• Drag-and-Drop Interface: Build data pipelines visually
• Integrated Services: Combined core and worker services
• Browser-Based: Access from any device on your network
• Code Generation: Export visual flows as Python/Polars scripts

FlowFrame API

• Familiar Syntax: Polars-like API makes it easy to learn
• ETL Graph Generation: Automatically builds visual workflows
• Lazy Evaluation: Operations are not executed until needed
• Interoperability: Move between code and visual interfaces

Data Operations

• Data Cleaning & Transformation: Complex joins, filtering, etc.
• High Performance: Built on Polars for efficient processing
• Data Integration: Handle various file formats
• ETL Pipeline Building: Create reusable workflows

🔄 Common FlowFrame Operations

import flowfile as ff
from flowfile import col, when, lit

# Read data
df = ff.from_dict({
    "id": [1, 2, 3, 4, 5],
    "category": ["A", "B", "A", "C", "B"],
    "value": [100, 200, 150, 300, 250]
})
# df_parquet = ff.read_parquet("data.parquet")
# df_csv = ff.read_csv("data.csv")

other_df = ff.from_dict({
    "product_id": [1, 2, 3, 4, 6],
    "product_name": ["WidgetA", "WidgetB", "WidgetC", "WidgetD", "WidgetE"],
    "supplier": ["SupplierX", "SupplierY", "SupplierX", "SupplierZ", "SupplierY"]
}, flow_graph=df.flow_graph  # Assign the data to the same graph
)

# Filter
filtered = df.filter(col("value") > 150)

# Transform
result = df.select(
    col("id"),
    (col("value") * 2).alias("double_value")
)

# Conditional logic
with_status = df.with_columns([
    when(col("value") > 200).then(lit("High")).otherwise(lit("Low")).alias("status")
])

# Group and aggregate
by_category = df.group_by("category").agg([
    col("value").sum().alias("total"),
    col("value").mean().alias("average")
])

# Join data
joined = df.join(other_df, left_on="id", right_on="product_id")

joined.flow_graph.flow_settings.execution_location = "auto"
joined.flow_graph.flow_settings.execution_mode = "Development"
ff.open_graph_in_editor(joined.flow_graph)  # opens the graph in the UI!

📝 Code Generation

Export your visual flows as standalone Python/Polars code for production use:

Simply click the "Generate code" button in the visual editor to:

• Generate clean, readable Python/Polars code
• Export flows without Flowfile dependencies
• Deploy workflows in any Python environment
• Share ETL logic with team members

🧰 Command-Line Interface

# Show help and version info
flowfile

# Start the web UI
flowfile run ui [options]

# Run individual services
flowfile run core --host 0.0.0.0 --port 63578
flowfile run worker --host 0.0.0.0 --port 63579

📚 Resources

• Main Repository: Latest code and examples
• Documentation: Comprehensive guides
• Technical Architecture: Design overview

🖥️ Full Application Options

For the complete visual ETL experience, you have additional options:

• Desktop Application: Download from the main repository
• Docker Setup: Run with Docker Compose
• Manual Setup: For development environments

📋 Development Roadmap

See the main repository for the latest development roadmap and TODO list.