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

[Image placeholder: Monetize360 Architecture Overview]

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

[Image placeholder: MFlow components diagram]

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

[Image placeholder: Object creation screen]

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

[Image placeholder: Workflow designer interface]

Step 3: Configure Functions

Each function node requires input configuration:

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

[Image placeholder: Function configuration panel]

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

[Image placeholder: Test execution screen]

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.

[Image placeholder: Insert-Fetch pattern diagram]

Approval Workflow Pattern

Start → UserAction → ApprovalAction → InsertMData → End

Create a multi-stage approval process with email notifications.

[Image placeholder: Approval workflow diagram]

Data Loading Pattern

Start → S3DataLoad → ForEach → InsertMData → End

Load data from S3 and process each record.

[Image placeholder: Data loading pattern diagram]

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 Data Operations Functions
• Learn about Workflow Control
• Review External Integration
• Check out API Reference

[Image placeholder: Learning path diagram]