Transform order data from multiple sources into analytics-ready format.
Input Data:
{
"order_id": "ORD-12345",
"customer_uuid": "550e8400-e29b-41d4-a716-446655440000",
"items": [
{ "sku": "PROD-001", "quantity": 2, "unit_price": 29.99 },
{ "sku": "PROD-002", "quantity": 1, "unit_price": 19.99 }
],
"payment_status": "completed",
"shipping_address": {
"street": "123 Main St",
"city": "New York",
"zip": "10001"
},
"created_at": "2024-01-15T10:30:00Z"
}
Output Data:
{
"order_id": "ORD-12345",
"customer_id": "550e8400-e29b-41d4-a716-446655440000",
"total_amount": 79.97,
"item_count": 3,
"average_item_price": 26.66,
"is_repeat_customer": true,
"shipping_region": "Northeast",
"order_date": "2024-01-15",
"revenue_category": "medium"
}
Implementation:
const ecommercePipeline = await mindMap.createDataPipeline(
'E-commerce Analytics Pipeline',
orderData,
analyticsOutput,
{
dataSources: [
{
type: 'database',
connection: { connectionString: process.env.ORDERS_DB_URL }
},
{
type: 'rest-api',
connection: { apiEndpoint: 'https://api.payment.com/webhooks' }
}
]
}
);
Real-time market data processing for trading decisions.
Market Input:
{
"symbol": "AAPL",
"price": 150.25,
"volume": 1000000,
"bid": 150.20,
"ask": 150.30,
"timestamp": "2024-01-15T15:30:00.123Z",
"exchange": "NASDAQ"
}
Risk Output:
{
"instrument": "AAPL",
"current_price": 150.25,
"price_volatility": 0.023,
"volume_profile": "high",
"risk_score": 0.15,
"compliance_status": "approved",
"last_updated": "2024-01-15T15:30:00.123Z"
}
Predictive maintenance from sensor data.
Sensor Input:
{
"machine_id": "LINE_01_PRESS",
"temperature": 85.5,
"pressure": 120.3,
"vibration_x": 0.02,
"vibration_y": 0.015,
"vibration_z": 0.008,
"timestamp": "2024-01-15T08:15:30.456Z"
}
Maintenance Output:
{
"equipment": "LINE_01_PRESS",
"health_score": 0.92,
"anomaly_detected": false,
"maintenance_due_days": 12,
"recommended_action": "continue_operation",
"alert_level": "green",
"last_analysis": "2024-01-15T08:15:30.456Z"
}
{
"topic": "Software Development Project",
"summary": "Full stack web application development",
"children": [
{
"topic": "Frontend Development",
"summary": "User interface and experience",
"skills": ["React", "TypeScript", "CSS"],
"children": [
{
"topic": "Component Architecture",
"summary": "Reusable UI components",
"skills": ["React Hooks", "Context API", "Component Design"]
},
{
"topic": "State Management",
"summary": "Application state handling",
"skills": ["Redux", "Zustand", "Local State"]
}
]
},
{
"topic": "Backend Development",
"summary": "Server-side logic and APIs",
"skills": ["Node.js", "Express", "Database"],
"children": [
{
"topic": "API Design",
"summary": "RESTful service architecture",
"skills": ["REST", "GraphQL", "Authentication"]
},
{
"topic": "Database Design",
"summary": "Data modeling and optimization",
"skills": ["PostgreSQL", "MongoDB", "Redis"]
}
]
}
]
}
{
"topic": "Data Science Learning Path",
"summary": "Comprehensive data science curriculum",
"children": [
{
"topic": "Mathematics Foundation",
"skills": ["Statistics", "Linear Algebra", "Calculus"],
"children": [
{ "topic": "Descriptive Statistics" },
{ "topic": "Probability Theory" },
{ "topic": "Matrix Operations" }
]
},
{
"topic": "Programming Skills",
"skills": ["Python", "R", "SQL"],
"children": [
{ "topic": "Data Manipulation" },
{ "topic": "Visualization Libraries" },
{ "topic": "Machine Learning Frameworks" }
]
},
{
"topic": "Machine Learning",
"skills": ["Supervised Learning", "Unsupervised Learning", "Deep Learning"],
"children": [
{ "topic": "Classification Algorithms" },
{ "topic": "Regression Models" },
{ "topic": "Neural Networks" }
]
}
]
}
Merge customer data from multiple sources (CRM, Support, Analytics).
Process application logs for monitoring and alerting.
Transform operational data for analytical processing.
Collect and normalize data from multiple external APIs.
Transform patient data for analytics while maintaining HIPAA compliance.
Real-time inventory updates from POS systems and warehouses.
Aggregate campaign performance data from multiple advertising platforms.
Track and analyze supply chain data for optimization opportunities.
Map code review workflows and approval processes.
Visualize deployment pipelines and automation workflows.
Create organizational charts and responsibility matrices.
Structure documentation and learning resources.
| Aspect | Traditional ETL | NodeQ Smart Pipeline |
|---|---|---|
| Development Time | Weeks to Months | Minutes to Hours |
| Code Maintenance | High (Manual) | Low (Auto-generated) |
| Error Handling | Manual Setup | Built-in Intelligence |
| Schema Changes | Requires Redevelopment | Auto-adaptation |
| Performance | Manual Tuning | ML-optimized |
| Monitoring | Custom Implementation | Built-in Metrics |
| Testing | Extensive Manual | AI-validated |