Testing and Validation

Spartera provides comprehensive testing and validation capabilities to
ensure your assets perform correctly before deployment. This guide covers
the preview/test functionality and full processing capabilities available
in the platform.

Overview of Testing Capabilities

Preview/Test Mode

Purpose: Quick validation of asset functionality
Data Scope: Tests on 10% of your data
Speed: Fast results for rapid iteration
Resource Usage: Minimal computational resources required

Process Mode

Purpose: Full-scale asset execution
Data Scope: Processes 100% of your data
Performance: Complete analytical results
Resource Usage: Full computational resources utilized

Preview/Test Functionality

How Preview Works

The Preview/Test feature provides a quick way to validate your assets:

Data Sampling: Automatically selects 10% of your data using
intelligent sampling
Query Execution: Runs your analytical logic on the sample
Result Validation: Checks if the asset returns expected outputs
Performance Metrics: Provides execution time and resource usage
statistics

Sampling Strategy

Statistical Sampling

Random Sampling: Ensures representative data distribution
Stratified Sampling: Maintains proportional representation across
segments
Time-Based Sampling: For temporal data, maintains chronological
distribution
Balanced Sampling: Ensures all data categories are represented

Data Quality Considerations

Completeness Checks: Validates sample has sufficient data
Distribution Validation: Ensures sample reflects full dataset
characteristics
Edge Case Coverage: Includes outliers and edge cases in sampling
Temporal Coverage: For time-series data, samples across time periods

Test Execution Process

Pre-Execution Validation

Connection Health: Verifies data connection is active
Permission Check: Confirms read access to required data
Schema Validation: Ensures data structure matches expectations
Resource Availability: Checks computational resources are available

During Execution

Query Performance: Monitors query execution time
Resource Utilization: Tracks CPU, memory, and I/O usage
Error Monitoring: Captures and logs any execution errors
Progress Tracking: Provides real-time execution status

Post-Execution Analysis

Result Validation: Checks output format and structure
Data Quality: Validates result completeness and accuracy
Performance Metrics: Records execution time and resource usage
Error Reporting: Documents any issues or warnings

Using Preview Results

Validation Checklist

Output Format: Confirm results match expected structure
Data Types: Verify result data types are correct
Value Ranges: Check if values fall within expected ranges
Null Handling: Ensure null values are handled appropriately
Error Conditions: Test behavior with problematic data

Iterative Development

Quick Feedback Loop: Make changes and re-test rapidly
Performance Optimization: Identify and resolve performance
bottlenecks
Logic Refinement: Adjust analytical algorithms based on results
Error Resolution: Fix issues before full deployment

Full Process Functionality

When to Use Process Mode

Use full processing for:

Final Validation: Complete testing before production deployment
Production Runs: Generating complete analytical results
Performance Testing: Evaluating full-scale performance
Comprehensive Analysis: When complete dataset analysis is required

Process Execution

Resource Management

Compute Allocation: Uses full computational resources
Memory Management: Handles large dataset memory requirements
I/O Optimization: Optimizes data read/write operations
Parallel Processing: Leverages parallel execution where possible

Monitoring and Observability

Real-Time Monitoring: Track execution progress and performance
Resource Usage: Monitor CPU, memory, and storage utilization
Error Detection: Immediate notification of execution problems
Performance Metrics: Detailed performance analytics

Result Handling

Complete Results: Full analytical output for entire dataset
Result Caching: Stores results for future quick retrieval
Export Options: Multiple formats for result consumption
Integration APIs: Direct API access to processed results

Testing Best Practices

Development Testing Strategy

Unit Testing

Logic Components: Test individual analytical components
Data Transformations: Validate each data transformation step
Calculations: Verify mathematical and statistical calculations
Error Handling: Test error conditions and edge cases

Integration Testing

Data Connections: Test with various data sources
End-to-End: Validate complete analytical workflows
Performance: Test under various load conditions
Compatibility: Ensure compatibility across different environments

User Acceptance Testing

Business Logic: Validate business requirements are met
Result Accuracy: Confirm analytical results are correct
Usability: Test ease of use and integration
Performance: Validate response times meet requirements

Performance Testing

Load Testing

Data Volume: Test with varying dataset sizes
Concurrent Users: Validate multi-user access patterns
Resource Scaling: Test resource utilization and scaling
Response Times: Measure performance under load

Stress Testing

Resource Limits: Test behavior at resource boundaries
Data Quality: Test with poor or incomplete data
Network Issues: Test behavior during connectivity problems
Error Recovery: Validate graceful degradation and recovery

Quality Assurance

Data Quality Testing

Accuracy: Verify analytical results are mathematically correct
Completeness: Ensure all required data is processed
Consistency: Validate consistent results across runs
Timeliness: Confirm data freshness requirements are met

Output Validation

Format Compliance: Ensure outputs match specified formats
Schema Validation: Verify output structure is correct
Value Validation: Check result values are within expected ranges
Error Messaging: Validate error messages are helpful and accurate

Automated Testing

Continuous Integration

Automated Test Runs: Integrate testing into CI/CD pipelines
Regression Testing: Automatically test for functionality regressions
Performance Monitoring: Continuous performance validation
Quality Gates: Prevent deployment of failing assets

Test Documentation

Test Plans: Document comprehensive testing strategies
Test Cases: Define specific test scenarios and expectations
Results Documentation: Record test results and decisions
Issue Tracking: Track and resolve testing issues

Troubleshooting Common Issues

Performance Issues

Slow Query Performance: Optimize database queries and indexes
Memory Constraints: Adjust memory allocation or data processing
approach
Network Bottlenecks: Optimize data transfer and connection pooling
Resource Contention: Balance resource usage across concurrent
operations

Data Issues

Missing Data: Implement robust null and missing data handling
Data Type Mismatches: Ensure proper data type handling and
conversion
Schema Changes: Handle evolving data schemas gracefully
Data Quality: Implement data validation and quality checks

Integration Issues

API Compatibility: Ensure API responses match consumer expectations
Authentication: Validate security and access control functionality
Version Compatibility: Test compatibility across different system
versions
Network Connectivity: Test various network conditions and
configurations

Comprehensive testing and validation ensures your assets are reliable,
performant, and ready for production use, providing confidence to both
asset creators and consumers.