Version: v4.2.24

📊 InvenTag BOM Data Processor

The BOM Data Processor is InvenTag's central orchestrator for processing raw inventory data and coordinating with specialized analyzers to produce enriched, analysis-ready datasets. It serves as the unified processing pipeline that transforms raw AWS resource data into comprehensive Bill of Materials (BOM) reports with deep analysis and insights.

📋 Overview

The BOM Data Processor consists of three main components:

BOMDataProcessor: Central orchestrator that coordinates all processing activities
BOMProcessingConfig: Configuration dataclass for customizing processing behavior
BOMData: Structured output containing enriched resources and analysis results

🏗️ Architecture

┌─────────────────────┐    ┌─────────────────────┐    ┌─────────────────────┐
│   BOMDataProcessor  │    │  Analysis Components │    │    BOMData Output   │
│                     │    │                     │    │                     │
│ • Data Extraction   │───▶│ • NetworkAnalyzer   │───▶│ • Enriched Resources│
│ • Resource Cleanup  │    │ • SecurityAnalyzer  │    │ • Network Analysis  │
│ • Orchestration     │    │ • ServiceEnricher   │    │ • Security Analysis │
│ • Error Handling    │    │ • DescriptionMgr    │    │ • Compliance Summary│
│ • Statistics        │    │ • TagMappingEngine  │    │ • Processing Stats  │
└─────────────────────┘    └─────────────────────┘    └─────────────────────┘

🔧 Core Components

BOMProcessingConfig

Configuration dataclass that controls all aspects of the processing pipeline:

from inventag.reporting.bom_processor import BOMProcessingConfig

# Default configuration with all features enabled
config = BOMProcessingConfig()

# Custom configuration for specific use cases
config = BOMProcessingConfig(
    enable_network_analysis=True,
    enable_security_analysis=True,
    enable_service_enrichment=True,
    enable_service_descriptions=True,
    enable_tag_mapping=True,
    enable_parallel_processing=True,
    max_worker_threads=4,
    cache_results=True,
    processing_timeout=300,
    service_descriptions_config='config/service_descriptions.yaml',
    tag_mappings_config='config/tag_mappings.yaml'
)

Configuration Options

Option	Type	Default	Description
`enable_network_analysis`	bool	True	Enable VPC/subnet network analysis
`enable_security_analysis`	bool	True	Enable security posture assessment
`enable_service_enrichment`	bool	True	Enable deep service attribute extraction
`enable_service_descriptions`	bool	True	Enable intelligent resource descriptions
`enable_tag_mapping`	bool	True	Enable tag transformation and mapping
`enable_parallel_processing`	bool	True	Enable multi-threaded processing
`max_worker_threads`	int	4	Maximum number of worker threads
`cache_results`	bool	True	Enable result caching for performance
`service_descriptions_config`	str	None	Path to service descriptions config file
`tag_mappings_config`	str	None	Path to tag mappings config file
`processing_timeout`	int	300	Processing timeout in seconds

BOMData

Structured output containing all processed data and analysis results:

from inventag.reporting.bom_processor import BOMData

# Access processed data
bom_data = processor.process_inventory_data(resources)

# Enriched resources with all analysis applied
enriched_resources = bom_data.resources

# Network analysis results
network_summary = bom_data.network_analysis
print(f"Total VPCs: {network_summary.get('total_vpcs', 0)}")
print(f"Total Subnets: {network_summary.get('total_subnets', 0)}")

# Security analysis results
security_summary = bom_data.security_analysis
print(f"Security Groups: {security_summary.get('total_security_groups', 0)}")
print(f"High Risk Rules: {security_summary.get('high_risk_rules', 0)}")

# Compliance summary
compliance = bom_data.compliance_summary
print(f"Compliance Rate: {compliance.get('compliance_percentage', 0):.1f}%")

# Processing metadata
metadata = bom_data.generation_metadata
print(f"Generated at: {metadata.get('generated_at')}")
print(f"Processing time: {metadata.get('processing_time_seconds', 0):.2f}s")

BOMData Structure

Field	Type	Description
`resources`	List[Dict]	Enriched and processed resources
`network_analysis`	Dict	Network analysis results and summaries
`security_analysis`	Dict	Security analysis results and findings
`compliance_summary`	Dict	Compliance statistics and violations
`generation_metadata`	Dict	Processing metadata and timestamps
`custom_attributes`	List[str]	List of discovered custom attributes
`processing_statistics`	Dict	Detailed processing statistics
`error_summary`	Dict	Error and warning summaries

ProcessingStatistics

Detailed statistics about the processing operation:

from inventag.reporting.bom_processor import ProcessingStatistics

# Get processing statistics
stats = processor.get_processing_statistics()

print(f"Total Resources: {stats.total_resources}")
print(f"Successfully Processed: {stats.processed_resources}")
print(f"Failed Processing: {stats.failed_resources}")
print(f"Network Enriched: {stats.network_enriched}")
print(f"Security Enriched: {stats.security_enriched}")
print(f"Service Enriched: {stats.service_enriched}")
print(f"Description Enriched: {stats.description_enriched}")
print(f"Tag Mapped: {stats.tag_mapped}")
print(f"Processing Time: {stats.processing_time_seconds:.2f} seconds")

# Handle errors and warnings
if stats.errors:
    print(f"Errors: {len(stats.errors)}")
    for error in stats.errors:
        print(f"  - {error}")

if stats.warnings:
    print(f"Warnings: {len(stats.warnings)}")
    for warning in stats.warnings:
        print(f"  - {warning}")

🔄 Data Processing Pipeline

The BOM Data Processor follows a comprehensive 8-stage processing pipeline:

Stage 1: Data Extraction

Intelligently extracts resources from various input formats:

Direct Resource Lists: Simple list of resource dictionaries
Container Formats: Inventory data with all_discovered_resources wrapper
Compliance Formats: Compliance checker output with separate resource categories
Custom Formats: Flexible extraction from unknown data structures

# Handles multiple input formats automatically
inventory_format = {"all_discovered_resources": resources}
compliance_format = {
    "compliant_resources": compliant_resources,
    "non_compliant_resources": non_compliant_resources
}
direct_format = resources  # Direct list

# All formats processed seamlessly
bom_data = processor.process_inventory_data(any_format)

Stage 2: Resource Standardization

Normalizes resource data structure and attributes:

Service Name Standardization: Converts service names to consistent format
Resource Type Fixes: Standardizes resource type naming conventions
ID Extraction: Extracts resource IDs from ARNs when missing
Account ID Population: Extracts account IDs from ARNs
Field Normalization: Ensures consistent field naming and structure

# Before standardization
resource = {
    "service": "CloudFormation",  # Inconsistent naming
    "type": "LoadBalancer",       # Needs hyphenation
    "arn": "arn:aws:ec2:us-east-1:123456789012:instance/i-1234567890abcdef0"
    # Missing id and account_id fields
}

# After standardization
resource = {
    "service": "CLOUDFORMATION",  # Standardized
    "type": "Load-Balancer",      # Fixed naming
    "id": "i-1234567890abcdef0",  # Extracted from ARN
    "account_id": "123456789012", # Extracted from ARN
    "arn": "arn:aws:ec2:us-east-1:123456789012:instance/i-1234567890abcdef0"
}

Stage 3: Service Reclassification

Intelligently reclassifies VPC-related resources for better organization:

# VPC-related resources are reclassified from EC2 to VPC service
vpc_resources = ["VPC", "Subnet", "SecurityGroup", "NetworkAcl", "InternetGateway", "NatGateway"]

# Before reclassification
ec2_resources = [
    {"service": "EC2", "type": "VPC", "id": "vpc-123"},
    {"service": "EC2", "type": "Subnet", "id": "subnet-123"},
    {"service": "EC2", "type": "SecurityGroup", "id": "sg-123"}
]

# After reclassification
vpc_service_resources = [
    {"service": "VPC", "type": "VPC", "id": "vpc-123"},
    {"service": "VPC", "type": "Subnet", "id": "subnet-123"},
    {"service": "VPC", "type": "SecurityGroup", "id": "sg-123"}
]

Stage 4: Data Cleaning

Fixes common data issues and inconsistencies:

Missing ID Fields: Extracts IDs from ARNs using intelligent parsing
Account ID Population: Ensures all resources have account_id field
Name Field Generation: Creates name fields from tags or IDs when missing
Region Validation: Validates and standardizes region information

Stage 5: Deduplication

Advanced deduplication with preference for more complete resource records:

# Duplicate resources with different completeness levels
resources = [
    {
        "arn": "arn:aws:ec2:us-east-1:123456789012:instance/i-123",
        "id": "i-123",
        "name": "instance-1"
    },
    {
        "arn": "arn:aws:ec2:us-east-1:123456789012:instance/i-123",
        "id": "i-123",
        "name": "instance-1",
        "service_attributes": {...},  # More complete record
        "tags": {...}
    }
]

# Deduplication keeps the more complete record
deduplicated = [
    {
        "arn": "arn:aws:ec2:us-east-1:123456789012:instance/i-123",
        "id": "i-123",
        "name": "instance-1",
        "service_attributes": {...},  # Preserved
        "tags": {...}
    }
]

Stage 6: Analysis Coordination

Orchestrates multiple analysis components based on configuration:

Network Analysis

if config.enable_network_analysis:
    # Enrich resources with network information
    for resource in resources:
        enriched_resource = network_analyzer.enrich_resource_with_network_info(resource)
    
    # Generate network summary
    network_summary = network_analyzer.generate_network_summary(resources)

Security Analysis

if config.enable_security_analysis:
    # Enrich resources with security information
    for resource in resources:
        enriched_resource = security_analyzer.enrich_resource_with_security_info(resource)
    
    # Generate security summary
    security_summary = security_analyzer.generate_security_summary(resources)

Service Enrichment

if config.enable_service_enrichment:
    # Deep attribute extraction for each resource
    for resource in resources:
        enriched_resource = service_enricher.enrich_resource(resource)

Service Descriptions

if config.enable_service_descriptions:
    # Apply intelligent descriptions
    for resource in resources:
        described_resource = service_desc_manager.apply_description_to_resource(resource)

Tag Mapping

if config.enable_tag_mapping:
    # Apply tag transformations and mappings
    for resource in resources:
        mapped_resource = tag_mapping_engine.apply_mappings_to_resource(resource)

Stage 7: Result Aggregation

Combines analysis results into comprehensive BOM data:

# Aggregate all analysis results
bom_data = BOMData(
    resources=processed_resources,
    network_analysis=network_summary,
    security_analysis=security_summary,
    compliance_summary=compliance_summary,
    generation_metadata=processing_metadata,
    custom_attributes=extracted_attributes,
    processing_statistics=processing_stats,
    error_summary=error_summary
)

Stage 8: Metadata Generation

Creates comprehensive processing metadata and statistics:

# Processing metadata includes
metadata = {
    "generated_at": datetime.now(timezone.utc).isoformat(),
    "processing_version": "1.0",
    "total_resources": len(resources),
    "processing_time_seconds": processing_time,
    "configuration": config_summary,
    "processing_statistics": detailed_stats
}

🛠️ Usage Patterns

Basic Processing

from inventag.reporting.bom_processor import BOMDataProcessor, BOMProcessingConfig
import boto3

# Initialize with default configuration
config = BOMProcessingConfig()
processor = BOMDataProcessor(config, boto3.Session())

# Process inventory data
bom_data = processor.process_inventory_data(resources)

# Access results
print(f"Processed {len(bom_data.resources)} resources")
print(f"Network Analysis: {bom_data.network_analysis}")
print(f"Security Analysis: {bom_data.security_analysis}")

Selective Analysis

# Network-focused analysis only
network_config = BOMProcessingConfig(
    enable_network_analysis=True,
    enable_security_analysis=False,
    enable_service_enrichment=False,
    enable_service_descriptions=False,
    enable_tag_mapping=False
)

processor = BOMDataProcessor(network_config, session)
bom_data = processor.process_inventory_data(resources)

# Only network analysis will be performed
network_summary = bom_data.network_analysis
print(f"VPC Count: {network_summary.get('total_vpcs', 0)}")
print(f"Subnet Utilization: {network_summary.get('average_utilization', 0):.1f}%")

Performance Optimization

# High-performance configuration for large datasets
performance_config = BOMProcessingConfig(
    enable_parallel_processing=True,
    max_worker_threads=8,
    cache_results=True,
    processing_timeout=600
)

processor = BOMDataProcessor(performance_config, session)

# Monitor processing performance
import time
start_time = time.time()
bom_data = processor.process_inventory_data(large_dataset)
processing_time = time.time() - start_time

stats = processor.get_processing_statistics()
print(f"Processing completed in {processing_time:.2f} seconds")
print(f"Throughput: {stats.processed_resources / processing_time:.1f} resources/second")

Custom Configuration Files

# Use custom configuration files
config = BOMProcessingConfig(
    enable_service_descriptions=True,
    enable_tag_mapping=True,
    service_descriptions_config='config/custom_descriptions.yaml',
    tag_mappings_config='config/custom_tag_mappings.yaml'
)

processor = BOMDataProcessor(config, session)
bom_data = processor.process_inventory_data(resources)

# Resources will have custom descriptions and tag mappings applied
for resource in bom_data.resources:
    if 'service_description' in resource:
        print(f"{resource['id']}: {resource['service_description']}")

🎯 Integration Patterns

With Resource Discovery

from inventag import AWSResourceInventory
from inventag.reporting.bom_processor import BOMDataProcessor, BOMProcessingConfig

# Discover resources
inventory = AWSResourceInventory(regions=['us-east-1', 'us-west-2'])
raw_resources = inventory.discover_resources()

# Process with comprehensive analysis
config = BOMProcessingConfig(
    enable_network_analysis=True,
    enable_security_analysis=True,
    enable_service_enrichment=True,
    enable_service_descriptions=True,
    enable_tag_mapping=True
)

processor = BOMDataProcessor(config, inventory.session)
bom_data = processor.process_inventory_data(raw_resources)

# Access comprehensive analysis results
print(f"Discovered and processed {len(bom_data.resources)} resources")
print(f"Network Summary: {bom_data.network_analysis}")
print(f"Security Summary: {bom_data.security_analysis}")

With State Management

from inventag.state import StateManager

# Process and save enriched state
state_manager = StateManager()
bom_data = processor.process_inventory_data(resources)

# Save comprehensive state with all analysis
state_id = state_manager.save_state(
    resources=bom_data.resources,
    account_id='123456789012',
    regions=['us-east-1', 'us-west-2'],
    network_analysis=bom_data.network_analysis,
    security_analysis=bom_data.security_analysis,
    compliance_data=bom_data.compliance_summary,
    tags={'processing': 'bom_processor', 'version': '1.0'}
)

print(f"Saved enriched state: {state_id}")

With Compliance Checking

from inventag.compliance import ComprehensiveTagComplianceChecker

# Process resources first
bom_data = processor.process_inventory_data(resources)

# Enhanced compliance checking with enriched data
checker = ComprehensiveTagComplianceChecker(
    regions=['us-east-1', 'us-west-2'],
    config_file='config/tag_policy.yaml'
)

compliance_results = checker.check_compliance(bom_data.resources)

# Combine with BOM analysis for comprehensive reporting
enhanced_bom = BOMData(
    resources=bom_data.resources,
    network_analysis=bom_data.network_analysis,
    security_analysis=bom_data.security_analysis,
    compliance_summary=compliance_results['summary'],
    generation_metadata=bom_data.generation_metadata
)

With BOM Converter

from inventag.reporting import BOMConverter

# Process data with BOM processor
bom_data = processor.process_inventory_data(resources)

# Convert to Excel with enriched data
converter = BOMConverter(enrich_vpc_info=False)  # VPC info already enriched
converter.data = bom_data.resources

# Export with comprehensive analysis
converter.export_to_excel('comprehensive_bom_report.xlsx')

# Include analysis summaries in metadata sheet
analysis_summary = {
    'Network Analysis': bom_data.network_analysis,
    'Security Analysis': bom_data.security_analysis,
    'Compliance Summary': bom_data.compliance_summary,
    'Processing Statistics': bom_data.processing_statistics
}

🔒 Error Handling and Recovery

Comprehensive Error Handling

# Configure error handling
config = BOMProcessingConfig(
    enable_network_analysis=True,
    enable_security_analysis=True,
    processing_timeout=300
)

processor = BOMDataProcessor(config, session)

try:
    bom_data = processor.process_inventory_data(resources)
    
    # Check for processing errors
    if bom_data.error_summary.get('has_errors', False):
        print("Processing completed with errors:")
        for error in bom_data.error_summary.get('errors', []):
            print(f"  - {error}")
    
    # Check for warnings
    if bom_data.error_summary.get('has_warnings', False):
        print("Processing completed with warnings:")
        for warning in bom_data.error_summary.get('warnings', []):
            print(f"  - {warning}")
            
except Exception as e:
    print(f"Processing failed: {e}")
    
    # Get partial results if available
    stats = processor.get_processing_statistics()
    if stats.processed_resources > 0:
        print(f"Partial processing completed: {stats.processed_resources} resources")

Graceful Degradation

The processor is designed to handle component failures gracefully:

# If network analysis fails, other components continue
try:
    bom_data = processor.process_inventory_data(resources)
    
    # Check which components succeeded
    if bom_data.network_analysis:
        print("Network analysis completed successfully")
    else:
        print("Network analysis failed - check error summary")
    
    if bom_data.security_analysis:
        print("Security analysis completed successfully")
    else:
        print("Security analysis failed - check error summary")
        
    # Resources are still processed even if some analysis fails
    print(f"Successfully processed {len(bom_data.resources)} resources")
    
except Exception as e:
    print(f"Critical processing failure: {e}")

Partial Processing Recovery

# Handle partial processing failures
def process_with_recovery(processor, resources, max_retries=3):
    for attempt in range(max_retries):
        try:
            bom_data = processor.process_inventory_data(resources)
            
            # Check processing success rate
            stats = processor.get_processing_statistics()
            success_rate = stats.processed_resources / stats.total_resources
            
            if success_rate >= 0.9:  # 90% success threshold
                print(f"Processing successful: {success_rate:.1%} success rate")
                return bom_data
            else:
                print(f"Low success rate: {success_rate:.1%}, retrying...")
                processor.clear_cache()  # Clear cache for retry
                
        except Exception as e:
            print(f"Attempt {attempt + 1} failed: {e}")
            if attempt == max_retries - 1:
                raise
    
    return None

📊 Performance Optimization

Caching System

# Enable result caching for improved performance
config = BOMProcessingConfig(
    cache_results=True,
    enable_parallel_processing=True
)

processor = BOMDataProcessor(config, session)

# First processing run - builds cache
bom_data1 = processor.process_inventory_data(resources)

# Subsequent runs use cached results for better performance
bom_data2 = processor.process_inventory_data(similar_resources)

# Clear cache when needed
processor.clear_cache()

Parallel Processing

# Configure parallel processing for large datasets
config = BOMProcessingConfig(
    enable_parallel_processing=True,
    max_worker_threads=8  # Adjust based on system capabilities
)

processor = BOMDataProcessor(config, session)

# Process large dataset with parallel workers
large_dataset = load_large_resource_dataset()
bom_data = processor.process_inventory_data(large_dataset)

# Monitor parallel processing performance
stats = processor.get_processing_statistics()
print(f"Parallel processing completed in {stats.processing_time_seconds:.2f}s")
print(f"Average throughput: {stats.processed_resources / stats.processing_time_seconds:.1f} resources/second")

Memory Management

# Process large datasets in batches to manage memory
def process_large_dataset_in_batches(processor, large_dataset, batch_size=1000):
    all_results = []
    
    for i in range(0, len(large_dataset), batch_size):
        batch = large_dataset[i:i + batch_size]
        print(f"Processing batch {i // batch_size + 1}: {len(batch)} resources")
        
        # Process batch
        bom_data = processor.process_inventory_data(batch)
        all_results.extend(bom_data.resources)
        
        # Clear cache between batches to manage memory
        processor.clear_cache()
    
    return all_results

🚨 Troubleshooting

Common Issues

Component Initialization Failures

try:
    processor = BOMDataProcessor(config, session)
except Exception as e:
    print(f"Component initialization failed: {e}")
    
    # Check AWS credentials and permissions
    try:
        session.client('ec2').describe_regions()
        print("AWS credentials are valid")
    except Exception as cred_error:
        print(f"AWS credential issue: {cred_error}")

Processing Timeouts

# Increase timeout for large datasets
config = BOMProcessingConfig(
    processing_timeout=900,  # 15 minutes
    max_worker_threads=2     # Reduce threads to avoid resource contention
)

processor = BOMDataProcessor(config, session)

Memory Issues

# Monitor memory usage during processing
import psutil
import os

def monitor_memory_usage():
    process = psutil.Process(os.getpid())
    memory_mb = process.memory_info().rss / 1024 / 1024
    print(f"Current memory usage: {memory_mb:.1f} MB")

# Process with memory monitoring
monitor_memory_usage()
bom_data = processor.process_inventory_data(resources)
monitor_memory_usage()

# Clear cache if memory usage is high
if psutil.virtual_memory().percent > 80:
    processor.clear_cache()

Analysis Component Failures

# Check which components are failing
bom_data = processor.process_inventory_data(resources)

if not bom_data.network_analysis:
    print("Network analysis failed - check network analyzer configuration")

if not bom_data.security_analysis:
    print("Security analysis failed - check security analyzer permissions")

if bom_data.error_summary.get('has_errors'):
    for error in bom_data.error_summary.get('errors', []):
        print(f"Error: {error}")

Performance Issues

Slow Processing

# Profile processing performance
import time

start_time = time.time()
bom_data = processor.process_inventory_data(resources)
total_time = time.time() - start_time

stats = processor.get_processing_statistics()
print(f"Processing Statistics:")
print(f"  Total time: {total_time:.2f}s")
print(f"  Resources processed: {stats.processed_resources}")
print(f"  Throughput: {stats.processed_resources / total_time:.1f} resources/second")

# Identify bottlenecks
if stats.network_enriched < stats.processed_resources:
    print("Network analysis may be a bottleneck")

if stats.service_enriched < stats.processed_resources:
    print("Service enrichment may be a bottleneck")

High Memory Usage

# Optimize for memory usage
memory_optimized_config = BOMProcessingConfig(
    enable_parallel_processing=False,  # Reduce memory overhead
    cache_results=False,               # Disable caching
    max_worker_threads=1               # Single-threaded processing
)

processor = BOMDataProcessor(memory_optimized_config, session)

📈 Best Practices

Configuration Management

Use Environment-Specific Configs: Create different configurations for development, staging, and production
Enable Selective Analysis: Only enable analysis components you need for better performance
Configure Timeouts Appropriately: Set realistic timeouts based on dataset size
Use Configuration Files: Store service descriptions and tag mappings in external files
Monitor Resource Usage: Track memory and CPU usage during processing

Performance Optimization

Enable Caching: Use result caching for repeated processing operations
Parallel Processing: Enable parallel processing for large datasets
Batch Processing: Process large datasets in batches to manage memory
Clear Caches: Periodically clear caches to prevent memory issues
Monitor Statistics: Track processing statistics to identify bottlenecks

Error Handling

Graceful Degradation: Design workflows to handle component failures gracefully
Comprehensive Logging: Log all errors and warnings for troubleshooting
Retry Logic: Implement retry logic for transient failures
Partial Results: Use partial results when complete processing fails
Validation: Validate input data before processing

Integration Patterns

Modular Design: Use the processor as part of larger workflows
State Management: Combine with state management for change tracking
Compliance Integration: Enhance compliance checking with enriched data
Export Integration: Use processed data with BOM converters
CI/CD Integration: Integrate into automated compliance workflows

🎯 Interactive Demo

Explore the BOM Data Processor with a comprehensive interactive demo:

# Run the BOM processor demonstration
python examples/bom_processor_demo.py

The demo script provides:

Configuration Examples: Different configuration patterns for various use cases
Processing Pipeline Walkthrough: Step-by-step explanation of the processing stages
Analysis Component Integration: How different analyzers work together
Performance Monitoring: Real-time processing statistics and performance metrics
Error Handling Demonstration: How the processor handles various error conditions
Integration Patterns: Examples of using the processor with other InvenTag components

For more examples and advanced usage patterns, see the comprehensive test suite in tests/unit/test_bom_processor.py and the integration examples in the main documentation.

📋 Overview​

🏗️ Architecture​

🔧 Core Components​

BOMProcessingConfig​

Configuration Options​

BOMData​

BOMData Structure​

ProcessingStatistics​

🔄 Data Processing Pipeline​

Stage 1: Data Extraction​

Stage 2: Resource Standardization​

Stage 3: Service Reclassification​

Stage 4: Data Cleaning​

Stage 5: Deduplication​

Stage 6: Analysis Coordination​

Network Analysis​

Security Analysis​

Service Enrichment​

Service Descriptions​

Tag Mapping​

Stage 7: Result Aggregation​

Stage 8: Metadata Generation​

🛠️ Usage Patterns​

Basic Processing​

Selective Analysis​

Performance Optimization​

Custom Configuration Files​

🎯 Integration Patterns​

With Resource Discovery​

With State Management​

With Compliance Checking​

With BOM Converter​

🔒 Error Handling and Recovery​

Comprehensive Error Handling​

Graceful Degradation​

Partial Processing Recovery​

📊 Performance Optimization​

Caching System​

Parallel Processing​

Memory Management​

🚨 Troubleshooting​

Common Issues​

Performance Issues​

📈 Best Practices​

Configuration Management​

Performance Optimization​

Error Handling​

Integration Patterns​

🎯 Interactive Demo​

📋 Overview

🏗️ Architecture

🔧 Core Components

BOMProcessingConfig

Configuration Options

BOMData

BOMData Structure

ProcessingStatistics

🔄 Data Processing Pipeline

Stage 1: Data Extraction

Stage 2: Resource Standardization

Stage 3: Service Reclassification

Stage 4: Data Cleaning

Stage 5: Deduplication

Stage 6: Analysis Coordination

Network Analysis

Security Analysis

Service Enrichment

Service Descriptions

Tag Mapping

Stage 7: Result Aggregation

Stage 8: Metadata Generation

🛠️ Usage Patterns

Basic Processing

Selective Analysis

Performance Optimization

Custom Configuration Files

🎯 Integration Patterns

With Resource Discovery

With State Management

With Compliance Checking

With BOM Converter

🔒 Error Handling and Recovery

Comprehensive Error Handling

Graceful Degradation

Partial Processing Recovery

📊 Performance Optimization

Caching System

Parallel Processing

Memory Management

🚨 Troubleshooting

Common Issues

Performance Issues

📈 Best Practices

Configuration Management

Performance Optimization

Error Handling

Integration Patterns

🎯 Interactive Demo