Getting Started with Monetize360

This guide will help you understand the fundamentals of Monetize360 and create your first workflow.

What is Monetize360?

Monetize360 is an enterprise workflow automation platform that enables you to build complex business processes through a visual interface. It provides:

• Visual Workflow Designer: Drag-and-drop interface for building workflows
• Rich Function Library: 25+ pre-built functions for common operations
• Custom Data Models: Define Objects to store your business data
• Integration Capabilities: Connect with external APIs, databases, and services
• Approval Workflows: Built-in support for multi-stage approvals
• Real-time Execution: Monitor workflow execution in real-time

Key Components

Objects (Data Models)

Objects define your data structure. Each Object contains:

• Fields with specific data types (text, number, date, etc.)
• Reference fields for relationships
• Picklist fields for dropdown values
• Validation rules

MData (Records)

MData represents individual records stored in Objects. Each MData record:

• Has a unique UUID
• Contains data conforming to the Object schema
• Tracks creation and modification timestamps
• Supports relationships with other MData records

MFlows (Workflows)

MFlows are visual workflows composed of:

• Nodes: Function blocks that perform specific operations
• Edges: Connections between nodes defining execution flow
• Variables: Data passed between nodes
• Triggers: Events that start workflow execution

Creating Your First Workflow

Step 1: Define Your Data Model

First, create an Object to store your data:

1. Navigate to Data Models → New Object
2. Define your object name (e.g., "Customer")
3. Add fields (name, email, phone, etc.)
4. Save your Object

Step 2: Build a Workflow

Create a new workflow:

1. Go to Workflows → New Workflow
2. Add a Start node
3. Drag function nodes from the palette
4. Configure each node's parameters
5. Connect nodes to define flow

Step 3: Configure Functions

Each function node requires input configuration:

{
  "mobjectId": "uuid-of-your-mobject",
  "data": {
    "name": "John Doe",
    "email": "john@example.com"
  }
}

Step 4: Test Your Workflow

Test the workflow before deployment:

1. Click Test Run
2. Provide test input data
3. Monitor execution in real-time
4. Review outputs and logs

Step 5: Deploy and Monitor

Deploy your workflow:

1. Click Deploy
2. Configure triggers (API, schedule, manual)
3. Monitor executions in the dashboard
4. View logs and troubleshoot issues

Common Patterns

Insert and Fetch Pattern

Start → InsertMData → FetchMData → End

Insert a record and then retrieve it with related data.

Approval Workflow Pattern

Start → UserAction → ApprovalAction → InsertMData → End

Create a multi-stage approval process with email notifications.

Data Loading Pattern

Start → S3DataLoad → ForEach → InsertMData → End

Load data from S3 and process each record.

Best Practices

Error Handling

Always configure error handling for critical operations:

• Use condition nodes to check for errors
• Implement retry logic for API calls
• Log errors for debugging

Performance Optimization

• Use bulk operations when processing multiple records
• Leverage PipelineQuery for complex data operations
• Minimize external API calls

Security

• Store sensitive data in encrypted fields
• Use role-based access control
• Validate input data before processing

Next Steps

• Explore CRUD Operations
• Learn about System Functions
• Review External API Integration
• Understand User Interactions
• Check out API Reference