Creating Assets

Assets are the core analytical components in Spartera that transform your
data into consumable insights. This comprehensive guide covers the asset
creation process from conception to deployment.

Asset Creation Workflow

Step 1: Select Connection

Choose the data connection you want to use for your asset:

Connection Selection: Pick from your established connections
Data Source Validation: Ensure the connection is active and healthy
Performance Considerations: Consider data volume and query complexity
Access Verification: Confirm you have necessary permissions

Step 2: Choose Asset Type

Select the appropriate asset type based on your analytical needs:

Calculation Assets

Best for analytical computations and data processing:

Statistical analysis and modeling
Business metric calculations
Data transformations and aggregations
Machine learning inference
Financial calculations and risk assessments

Visualization Assets

Ideal for presenting data insights visually:

Interactive dashboards and reports
Real-time monitoring displays
Comparative analysis charts
Executive summary presentations
Trend analysis graphs

Step 3: Asset Configuration

Unique Asset Name

Create a descriptive, unique identifier:

Format: Use clear, descriptive names
Conventions: Follow consistent naming patterns
Uniqueness: Ensure names are unique across your organization
URL Slug: A URL-friendly slug is automatically generated

Example Names:

customer-churn-prediction
monthly-revenue-analysis
inventory-optimization-model
user-engagement-dashboard

Description

Provide a comprehensive description that includes:

Purpose: What business problem the asset solves
Methodology: How the analysis is performed
Inputs: What data is required
Outputs: What results are provided
Use Cases: How the asset should be used

Example Description:

Predictive model that identifies customers at risk of churning based on 
transaction history, support interactions, and engagement metrics. 
Returns churn probability score (0-1) and key risk factors. 
Updated daily with latest customer data.

Step 4: Categorization and Discovery

Category Selection

Choose appropriate categories for your asset:

Sales & Marketing: Customer analytics, campaign performance
Finance & Operations: Financial modeling, operational metrics
Product & Engineering: Usage analytics, performance monitoring
Human Resources: Workforce analytics, performance metrics
Risk & Compliance: Risk assessment, regulatory reporting

Asset Development Best Practices

Data Quality Considerations

Data Validation: Implement checks for data quality and completeness
Error Handling: Handle missing or invalid data gracefully
Data Freshness: Consider data recency requirements
Consistency: Ensure consistent data processing logic

Performance Optimization

Query Efficiency: Write optimized queries for your data platform
Resource Usage: Consider computational requirements and limits
Caching Strategy: Implement appropriate result caching
Scalability: Design for varying data volumes

Documentation Standards

Code Documentation: Comment complex analytical logic
Methodology Notes: Document statistical or ML approaches
Assumption Documentation: Record key assumptions and limitations
Change History: Track modifications and version changes

Asset Configuration Options

Processing Parameters

Configure how your asset processes data:

Aggregation Level: Define granularity of results
Time Windows: Specify time ranges for analysis
Filtering Criteria: Set default filters and parameters
Output Format: Define result structure and format

Security and Access

Access Controls: Define who can use the asset
Data Sensitivity: Mark assets containing sensitive data
Compliance Tags: Add regulatory compliance indicators
Privacy Controls: Implement data privacy protections

Integration Settings

API Configuration: Set up REST endpoint parameters
Response Formats: Define JSON structure for responses
Rate Limits: Configure usage limits and throttling
Error Responses: Define error handling and messaging

Testing and Validation

Before publishing, thoroughly test your asset:

Functional Testing

Logic Validation: Verify analytical calculations are correct
Edge Cases: Test with unusual or extreme data scenarios
Error Conditions: Ensure graceful handling of errors
Performance: Validate response times meet requirements

Data Testing

Sample Validation: Test with representative data samples
Volume Testing: Verify performance with full datasets
Quality Checks: Ensure results meet quality standards
Consistency: Verify consistent results across runs

Version Management

Version Control

Semantic Versioning: Use version numbers (e.g., 1.0.0, 1.1.0)
Change Documentation: Document what changed in each version
Backward Compatibility: Maintain compatibility when possible
Migration Guides: Provide guidance for breaking changes

Deployment Strategy

Staging Environment: Test in staging before production
Gradual Rollout: Consider phased deployment approaches
Rollback Plans: Have plans for reverting problematic versions
Monitoring: Monitor asset performance after deployment

Common Asset Patterns

Real-Time Analytics

For assets requiring current data:

Streaming Data: Connect to real-time data sources
Low Latency: Optimize for quick response times
Resource Management: Handle varying load efficiently
Data Freshness: Ensure data is current and relevant

Batch Analytics

For complex analytical processing:

Scheduled Processing: Run analysis on regular schedules
Large Datasets: Handle substantial data volumes efficiently
Complex Logic: Implement sophisticated analytical algorithms
Result Caching: Store results for quick retrieval

Hybrid Assets

Combining real-time and historical analysis:

Streaming + Batch: Combine real-time and historical data
Contextual Insights: Provide current data in historical context
Adaptive Processing: Adjust processing based on data characteristics
Comprehensive Results: Deliver complete analytical pictures

Asset Lifecycle Management

Development Phase

Initial asset creation and configuration
Core logic implementation and testing
Documentation and validation

Testing Phase

Preview functionality with sample data
Performance validation and optimization
User acceptance testing

Deployment Phase

Production deployment and monitoring
Integration with consuming systems
Performance monitoring and optimization

Maintenance Phase

Regular updates and improvements
Performance monitoring and tuning
Documentation updates and user support

Creating well-designed assets is crucial for maximizing the value of your
analytics platform and ensuring successful adoption by consumers.