Visual Editor Guide

Build powerful data pipelines without writing code using Flowfile's intuitive drag-and-drop interface.

What You'll Learn

• Build flows visually - Drag, drop, and connect nodes
• Transform data - Filter, aggregate, join, and more
• Connect to data sources - Databases, files, and cloud storage
• Preview results - See data at each step
• Export to code - Generate Python code from your visual flows

Getting Started

Your First Flow

1. Create a new flow - Click "Create" in the toolbar
2. Add an input node - Drag a "Read Data" node from the left panel
3. Configure the node - Click it and set file path in the right panel
4. Add transformations - Connect filter, sort, or other nodes
5. Run the flow - Click "Run" and see your results

Interface Overview

• Left Panel: Node library organized by category
• Center Canvas: Build your flow here
• Right Panel: Configure selected nodes
• Bottom Panel: Preview data and logs

Core Concepts

Nodes

Each node represents a data operation:

• Input nodes - Load data from files, databases, APIs
• Transform nodes - Modify and clean your data
• Combine nodes - Join and merge datasets
• Aggregate nodes - Summarize and group data
• Output nodes - Save or export results

Connections

Draw lines between nodes to define data flow. Data moves from top to bottom, left to right.

Execution

• Development mode - See data at every step (great for debugging)
• Performance mode - Optimized execution for large datasets

Learn More

More Resources

• Building Flows - Detailed workflow guide
• Node Reference - Complete documentation of all nodes

Tutorials

• Connect to Databases - PostgreSQL, MySQL, and more
• Cloud Storage Setup - Work with S3 data
• Export to Python - Convert visual flows to code

Tips for Success

1. Start simple - Build basic flows before adding complexity
2. Use descriptions - Document nodes for your future self
3. Preview often - Check data at each transformation
4. Save regularly - Flows are saved as .flowfile files
5. Try both modes - Development for testing, Performance for production

Visual vs Code

Wondering when to use visual vs Python? Here's a quick guide:

Use Visual Editor when:

• Exploring new datasets
• Building one-off analyses
• Collaborating with non-technical users
• Creating documented workflows
• Learning data transformations

Consider Python API when:

• Integrating with existing code
• Building programmatic pipelines
• Need version control
• Require advanced custom logic
• Automating workflows

Remember, you can always switch between them!

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Ready to build? Start with Building Flows or explore the Node Reference.