Enterprise Integration

Comprehensive Enterprise Integration Platform

import asyncio
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Callable, Any, Union
from datetime import datetime, timedelta
from enum import Enum
import json
import os
import docker
import kubernetes
import terraform
import yaml
import pandas as pd
from kubernetes import client, config
from kafka import KafkaProducer, KafkaConsumer
from kafka.errors import KafkaError
import redis
import sqlalchemy
from sqlalchemy import create_engine, text
import pandas as pd
import numpy as np

class SystemType(Enum):
    LEGACY_TRADING = "legacy_trading"
    RISK_MANAGEMENT = "risk_management"
    MEV_EXTRACTION = "mev_extraction"
    DATA_LAKE = "data_lake"
    ANALYTICS = "analytics"
    MONITORING = "monitoring"
    COMPLIANCE = "compliance"

class IntegrationPattern(Enum):
    BATCH_PROCESSING = "batch_processing"
    REAL_TIME_STREAMING = "real_time_streaming"
    EVENT_DRIVEN = "event_driven"
    API_GATEWAY = "api_gateway"
    MESSAGE_QUEUE = "message_queue"
    DATABASE_REPLICATION = "database_replication"

@dataclass
class SystemIntegration:
    """Configuration for system integration"""
    integration_id: str
    source_system: SystemType
    target_system: SystemType
    integration_pattern: IntegrationPattern
    data_mapping: Dict[str, str]
    transformation_rules: List[Dict]
    security_level: str
    latency_requirement: str  # 'real_time', 'near_real_time', 'batch'
    throughput_requirement: str  # messages_per_second
    error_handling: str  # 'retry', 'dead_letter_queue', 'skip'
    monitoring_enabled: bool = True
    compliance_requirements: List[str] = field(default_factory=list)

@dataclass
class EnterpriseComponent:
    """Enterprise system component"""
    component_id: str
    component_name: str
    component_type: SystemType
    technology_stack: Dict[str, str]
    deployment_model: str  # 'on_premise', 'cloud', 'hybrid'
    scaling_config: Dict[str, Any]
    security_config: Dict[str, Any]
    monitoring_config: Dict[str, Any]
    dependencies: List[str]
    compliance_status: str
    sla_requirements: Dict[str, float]

@dataclass
class IntegrationFlow:
    """Data integration flow definition"""
    flow_id: str
    flow_name: str
    source_components: List[str]
    target_components: List[str]
    transformations: List[Dict]
    validation_rules: List[Dict]
    error_handling: Dict[str, Any]
    performance_metrics: Dict[str, float]
    compliance_checks: List[str]

class EnterpriseMEVPlatform:
    """Comprehensive enterprise MEV integration platform"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.logger = logging.getLogger(__name__)
        
        # Platform components
        self.components = {}
        self.integration_flows = {}
        self.data_streams = {}
        
        # Infrastructure management
        self.kubernetes_client = None
        self.docker_client = None
        self.terraform_client = None
        
        # Data management
        self.data_lake = None
        self.analytics_engine = None
        self.real_time_processor = None
        
        # Integration tools
        self.api_gateway = None
        self.message_queue = None
        self.database_connectors = {}
        
        # Monitoring and observability
        self.monitoring_stack = None
        self.alerting_system = None
        self.performance_analyzer = None
        
        # Security and compliance
        self.security_manager = None
        self.compliance_engine = None
        self.audit_logger = None
        
    async def initialize_platform(self):
        """Initialize the complete enterprise platform"""
        self.logger.info("Initializing Enterprise MEV Platform...")
        
        # Initialize infrastructure
        await self._initialize_infrastructure()
        
        # Set up data platform
        await self._initialize_data_platform()
        
        # Initialize integration layer
        await self._initialize_integration_layer()
        
        # Set up monitoring
        await self._initialize_monitoring()
        
        # Initialize security
        await self._initialize_security()
        
        # Start platform services
        await self._start_platform_services()
        
        self.logger.info("Enterprise MEV Platform initialized successfully")
    
    async def _initialize_infrastructure(self):
        """Initialize cloud infrastructure"""
        # Initialize Kubernetes client
        try:
            config.load_incluster_config()  # Running inside cluster
        except:
            config.load_kube_config()  # Running locally
        
        self.kubernetes_client = client.ApiClient()
        
        # Initialize Docker client
        self.docker_client = docker.from_env()
        
        # Initialize Terraform for infrastructure as code
        self.terraform_client = terraform.Terraform()
        
        # Set up networking and security groups
        await self._setup_network_infrastructure()
        
        # Initialize storage systems
        await self._setup_storage_systems()
        
        # Set up monitoring infrastructure
        await self._setup_monitoring_infrastructure()
    
    async def _initialize_data_platform(self):
        """Initialize enterprise data platform"""
        # Data lake setup
        self.data_lake = {
            'raw_data_zone': 's3://company-data-lake/raw/',
            'processed_data_zone': 's3://company-data-lake/processed/',
            'analytics_zone': 's3://company-data-lake/analytics/',
            'metadata_store': 'postgresql://data-lake-metadata',
            'catalog_service': 'atlas',
            'quality_framework': 'great_expectations'
        }
        
        # Real-time processing
        self.real_time_processor = {
            'stream_processing': 'apache_flink',
            'message_broker': 'kafka',
            'cache_layer': 'redis',
            'query_engine': 'apache_presto'
        }
        
        # Analytics engine
        self.analytics_engine = {
            'machine_learning': 'kubeflow',
            'statistical_analysis': 'r_python',
            'visualization': 'superset_tableau',
            'reporting': 'apache_superset'
        }
    
    async def _initialize_integration_layer(self):
        """Initialize system integration layer"""
        # API Gateway
        self.api_gateway = {
            'gateway_type': 'kong_istio',
            'authentication': 'oauth2_jwt',
            'rate_limiting': 'enabled',
            'load_balancing': 'round_robin',
            'circuit_breaker': 'enabled',
            'request_logging': 'enabled'
        }
        
        # Message Queue
        self.message_queue = {
            'primary': 'kafka',
            'backup': 'rabbitmq',
            ' Topics': {
                'mev_opportunities': 'high_throughput',
                'trade_executions': 'reliable_delivery',
                'risk_alerts': 'low_latency',
                'compliance_events': 'audit_trail'
            }
        }
        
        # Database connectors
        self.database_connectors = {
            'trading_systems': {
                'connector_type': 'debezium',
                'database': 'postgresql',
                'cdc_enabled': True
            },
            'risk_systems': {
                'connector_type': 'apache_nifi',
                'database': 'oracle',
                'batch_processing': True
            },
            'mev_systems': {
                'connector_type': 'custom',
                'database': 'mongodb',
                'real_time': True
            }
        }
    
    async def _initialize_monitoring(self):
        """Initialize monitoring and observability"""
        self.monitoring_stack = {
            'metrics': 'prometheus',
            'visualization': 'grafana',
            'alerting': 'alertmanager',
            'log_aggregation': 'elk_stack',
            'tracing': 'jaeger',
            'uptime_monitoring': 'pingdom'
        }
        
        self.alerting_system = {
            'channels': ['email', 'slack', 'pagerduty', 'teams'],
            'escalation_policies': {
                'critical': 'immediate',
                'high': '5_minutes',
                'medium': '15_minutes',
                'low': '1_hour'
            },
            'runbook_integration': 'confluence'
        }
    
    async def _initialize_security(self):
        """Initialize security and compliance"""
        self.security_manager = {
            'authentication': 'okta_azure_ad',
            'authorization': 'rbac_abac',
            'encryption': 'aes256',
            'key_management': 'hashicorp_vault',
            'certificate_management': 'cert_manager',
            'network_security': 'zero_trust'
        }
        
        self.compliance_engine = {
            'data_governance': 'collibra',
            'privacy_protection': 'privacy_by_design',
            'audit_compliance': 'sox_gdpr',
            'risk_assessment': 'fair_isaac'
        }
    
    async def add_enterprise_component(self, component: EnterpriseComponent) -> bool:
        """Add new enterprise component to platform"""
        try:
            # Validate component configuration
            await self._validate_component(component)
            
            # Deploy component infrastructure
            await self._deploy_component(component)
            
            # Register component in platform registry
            self.components[component.component_id] = component
            
            # Set up monitoring
            await self._setup_component_monitoring(component)
            
            # Configure security
            await self._configure_component_security(component)
            
            self.logger.info(f"Component {component.component_name} added successfully")
            return True
            
        except Exception as e:
            self.logger.error(f"Failed to add component {component.component_name}: {e}")
            return False
    
    async def create_integration_flow(self, flow: IntegrationFlow) -> bool:
        """Create new data integration flow"""
        try:
            # Validate flow configuration
            await self._validate_integration_flow(flow)
            
            # Create data transformation pipeline
            pipeline = await self._create_transformation_pipeline(flow)
            
            # Set up error handling
            await self._setup_error_handling(flow)
            
            # Configure performance monitoring
            await self._setup_performance_monitoring(flow)
            
            # Register flow in platform
            self.integration_flows[flow.flow_id] = flow
            
            # Deploy and activate flow
            await self._deploy_integration_flow(flow, pipeline)
            
            self.logger.info(f"Integration flow {flow.flow_name} created successfully")
            return True
            
        except Exception as e:
            self.logger.error(f"Failed to create integration flow {flow.flow_name}: {e}")
            return False
    
    async def _validate_component(self, component: EnterpriseComponent):
        """Validate component configuration"""
        # Check required fields
        required_fields = ['component_id', 'component_name', 'component_type', 'technology_stack']
        for field in required_fields:
            if not hasattr(component, field) or getattr(component, field) is None:
                raise ValueError(f"Missing required field: {field}")
        
        # Validate technology stack compatibility
        await self._check_technology_compatibility(component)
        
        # Check dependency availability
        await self._check_dependencies(component)
    
    async def _check_technology_compatibility(self, component: EnterpriseComponent):
        """Check technology stack compatibility with platform"""
        # Define compatibility matrix
        compatible_stacks = {
            SystemType.MEV_EXTRACTION: {
                'languages': ['python', 'go', 'rust'],
                'frameworks': ['fastapi', 'django', 'flask'],
                'databases': ['postgresql', 'mongodb', 'redis'],
                'message_queues': ['kafka', 'rabbitmq'],
                'deployment': ['kubernetes', 'docker', 'terraform']
            },
            SystemType.LEGACY_TRADING: {
                'languages': ['java', 'c++', 'python'],
                'databases': ['oracle', 'postgresql', 'sqlserver'],
                'integration': ['jms', 'rest', 'soap']
            },
            SystemType.RISK_MANAGEMENT: {
                'languages': ['python', 'r', 'sas'],
                'databases': ['postgresql', 'oracle'],
                'analytics': ['hadoop', 'spark', 'hive']
            }
        }
        
        stack = component.technology_stack
        expected_stack = compatible_stacks.get(component.component_type, {})
        
        # Check compatibility
        for category, compatible_tech in expected_stack.items():
            if category in stack and stack[category] not in compatible_tech:
                self.logger.warning(f"Technology {stack[category]} may not be compatible with {component.component_type}")
    
    async def _check_dependencies(self, component: EnterpriseComponent):
        """Check if component dependencies are available"""
        for dependency_id in component.dependencies:
            if dependency_id not in self.components:
                raise ValueError(f"Required dependency {dependency_id} not found")
            
            # Check dependency status
            dep_component = self.components[dependency_id]
            if dep_component.compliance_status != 'active':
                raise ValueError(f"Dependency {dependency_id} is not active")
    
    async def _deploy_component(self, component: EnterpriseComponent):
        """Deploy component to platform infrastructure"""
        # Create Kubernetes deployment
        await self._create_kubernetes_deployment(component)
        
        # Set up persistent storage
        await self._setup_component_storage(component)
        
        # Configure networking
        await self._configure_component_networking(component)
        
        # Initialize databases and caches
        await self._initialize_component_databases(component)
    
    async def _create_transformation_pipeline(self, flow: IntegrationFlow):
        """Create data transformation pipeline"""
        pipeline_config = {
            'source_readers': [],
            'transformations': flow.transformations,
            'validators': flow.validation_rules,
            'writers': [],
            'error_handling': flow.error_handling,
            'performance_monitoring': flow.performance_metrics
        }
        
        # Add source readers
        for source_id in flow.source_components:
            reader_config = await self._create_source_reader(source_id)
            pipeline_config['source_readers'].append(reader_config)
        
        # Add writers
        for target_id in flow.target_components:
            writer_config = await self._create_target_writer(target_id)
            pipeline_config['writers'].append(writer_config)
        
        return pipeline_config
    
    async def _create_source_reader(self, source_id: str) -> Dict:
        """Create configuration for data source reader"""
        if source_id not in self.components:
            raise ValueError(f"Source component {source_id} not found")
        
        source_component = self.components[source_id]
        
        if source_component.component_type == SystemType.LEGACY_TRADING:
            return {
                'type': 'database_reader',
                'connection': f"jdbc://trading-db/{source_id}",
                'batch_size': 1000,
                'incremental_columns': ['updated_at', 'trade_id']
            }
        elif source_component.component_type == SystemType.MEV_EXTRACTION:
            return {
                'type': 'stream_reader',
                'source': 'kafka',
                'topic': f'{source_id}_events',
                'consumer_group': 'mev_integration',
                'auto_offset_reset': 'earliest'
            }
        else:
            return {
                'type': 'api_reader',
                'endpoint': f"https://api.{source_id}.company.com",
                'pagination': 'cursor_based',
                'rate_limit': 1000
            }
    
    async def setup_legacy_integration(self, legacy_system_config: Dict) -> Dict:
        """Set up integration with legacy trading systems"""
        integration_plan = {
            'assessment_phase': {
                'legacy_system_analysis': await self._analyze_legacy_system(legacy_system_config),
                'data_mapping': await self._create_data_mapping(legacy_system_config),
                'interface_analysis': await self._analyze_system_interfaces(legacy_system_config),
                'risk_assessment': await self._assess_integration_risks(legacy_system_config)
            },
            'development_phase': {
                'connector_development': await self._develop_connectors(legacy_system_config),
                'data_transformation': await self._develop_transformations(legacy_system_config),
                'testing_framework': await self._setup_testing_framework(legacy_system_config),
                'security_implementation': await self._implement_security(legacy_system_config)
            },
            'deployment_phase': {
                'gradual_migration': await self._plan_gradual_migration(legacy_system_config),
                'parallel_operation': await self._setup_parallel_operation(legacy_system_config),
                'fallback_procedures': await self._define_fallback_procedures(legacy_system_config),
                'monitoring_setup': await self._setup_integration_monitoring(legacy_system_config)
            },
            'optimization_phase': {
                'performance_tuning': await self._optimize_performance(legacy_system_config),
                'reliability_improvements': await self._improve_reliability(legacy_system_config),
                'cost_optimization': await self._optimize_costs(legacy_system_config),
                'documentation': await self._create_integration_documentation(legacy_system_config)
            }
        }
        
        return integration_plan
    
    async def create_scalable_architecture(self) -> Dict:
        """Create scalable MEV architecture design"""
        architecture = {
            'presentation_layer': {
                'load_balancer': {
                    'type': 'nginx_haproxy',
                    'algorithm': 'round_robin',
                    'health_checks': True,
                    'ssl_termination': True
                },
                'api_gateway': {
                    'rate_limiting': '10000_requests_per_second',
                    'authentication': 'oauth2_jwt',
                    'circuit_breaker': True,
                    'request_logging': True
                },
                'web_application': {
                    'framework': 'react_vue',
                    'state_management': 'redux_vuex',
                    'real_time_updates': 'websocket'
                }
            },
            'application_layer': {
                'microservices': {
                    'mev_extraction_service': {
                        'instances': 'auto_scaling',
                        'min_instances': 3,
                        'max_instances': 50,
                        'cpu_threshold': 70,
                        'memory_threshold': 80
                    },
                    'risk_management_service': {
                        'instances': 'auto_scaling',
                        'min_instances': 2,
                        'max_instances': 20,
                        'cpu_threshold': 70,
                        'memory_threshold': 80
                    },
                    'compliance_service': {
                        'instances': 'static',
                        'instances_count': 3,
                        'cpu_threshold': 50,
                        'memory_threshold': 70
                    }
                },
                'message_queues': {
                    'kafka_clusters': {
                        'brokers': 5,
                        'partitions_per_topic': 30,
                        'replication_factor': 3,
                        'retention_period': '30_days'
                    },
                    'redis_clusters': {
                        'nodes': 6,
                        'sharding': True,
                        'replication': 'master_slave',
                        'persistence': 'rdb_aof'
                    }
                }
            },
            'data_layer': {
                'operational_databases': {
                    'primary_database': {
                        'type': 'postgresql',
                        'cluster_size': 3,
                        'backup_strategy': 'point_in_time_recovery',
                        'read_replicas': 2
                    },
                    'cache_layer': {
                        'type': 'redis',
                        'cluster_nodes': 6,
                        'ttl_strategy': 'adaptive'
                    }
                },
                'analytical_databases': {
                    'data_warehouse': {
                        'type': 'snowflake_redshift',
                        'storage_tiers': 'hot_warm_cold',
                        'compression': True,
                        'partitioning': 'date_based'
                    },
                    'data_lake': {
                        'storage': 's3_adls',
                        'format': 'parquet_delta',
                        'partitioning': 'by_date_category'
                    }
                }
            },
            'infrastructure_layer': {
                'container_orchestration': {
                    'kubernetes': {
                        'clusters': 3,
                        'nodes_per_cluster': 20,
                        'auto_scaling': True,
                        'monitoring': 'prometheus_grafana'
                    }
                },
                'service_mesh': {
                    'istio': {
                        'traffic_management': True,
                        'security_policy': 'zero_trust',
                        'observability': True
                    }
                },
                'monitoring': {
                    'metrics': 'prometheus',
                    'logging': 'elk_stack',
                    'tracing': 'jaeger',
                    'uptime': 'pingdom_statuscake'
                }
            }
        }
        
        return architecture
    
    async def implement_data_governance(self) -> Dict:
        """Implement enterprise data governance framework"""
        data_governance = {
            'data_classification': {
                'public': {
                    'description': 'Publicly available data',
                    'handling_requirements': 'none',
                    'retention_period': 'indefinite'
                },
                'internal': {
                    'description': 'Internal business data',
                    'handling_requirements': 'access_control',
                    'retention_period': '7_years'
                },
                'confidential': {
                    'description': 'Sensitive business information',
                    'handling_requirements': 'encryption_access_logging',
                    'retention_period': '10_years'
                },
                'restricted': {
                    'description': 'Highly sensitive data',
                    'handling_requirements': 'encryption_access_logging_audit',
                    'retention_period': 'regulatory_dependent'
                }
            },
            'data_quality': {
                'validation_rules': [
                    'completeness_check',
                    'accuracy_verification',
                    'consistency_validation',
                    'timeliness_check'
                ],
                'quality_metrics': [
                    'data_completeness_ratio',
                    'accuracy_score',
                    'consistency_index',
                    'freshness_score'
                ],
                'monitoring_framework': {
                    'real_time_validation': True,
                    'batch_quality_checks': True,
                    'quality_dashboard': 'grafana',
                    'alerting_thresholds': {
                        'completeness': 95,
                        'accuracy': 99,
                        'consistency': 98
                    }
                }
            },
            'data_lineage': {
                'tracking_approach': 'automated',
                'lineage_tools': 'apache_atlas',
                'source_to_sink_mapping': True,
                'transformation_tracking': True,
                'impact_analysis': True
            },
            'data_privacy': {
                'privacy_by_design': True,
                'personal_data_identification': 'automated',
                'consent_management': 'distributed',
                'data_anonymization': 'on_demand',
                'right_to_deletion': 'automated'
            },
            'data_security': {
                'encryption_at_rest': 'aes256',
                'encryption_in_transit': 'tls_1_3',
                'access_control': 'rbac_abac',
                'key_management': 'hashicorp_vault',
                'audit_logging': 'comprehensive'
            }
        }
        
        return data_governance
    
    async def establish_change_management(self) -> Dict:
        """Establish organizational change management program"""
        change_management = {
            'assessment_phase': {
                'readiness_assessment': {
                    'organizational_culture': 'assess_current_state',
                    'technology_readiness': 'evaluate_infrastructure',
                    'skill_gaps': 'identify_training_needs',
                    'resistance_factors': 'analyze_change_factors'
                },
                'stakeholder_analysis': {
                    'executive_sponsors': 'identify_champions',
                    'business_units': 'assess_impact',
                    'technical_teams': 'evaluate_readiness',
                    'end_users': 'understand_concerns'
                }
            },
            'planning_phase': {
                'communication_strategy': {
                    'messaging_framework': 'consistent_clear_compelling',
                    'communication_channels': ['town_halls', 'newsletters', 'intranet'],
                    'frequency': 'weekly_updates',
                    'feedback_mechanisms': 'surveys_forums'
                },
                'training_program': {
                    'technical_training': 'hands_on_workshops',
                    'business_training': 'role_specific_sessions',
                    'leadership_training': 'change_leadership',
                    'certification_program': 'formal_assessments'
                },
                'support_structure': {
                    'help_desk': 'dedicated_support_team',
                    'super_user_network': 'peer_support_model',
                    'champion_program': 'influencer_engagement',
                    'coaching': 'one_on_one_support'
                }
            },
            'execution_phase': {
                'pilot_implementation': {
                    'pilot_selection': 'representative_groups',
                    'success_criteria': 'defined_metrics',
                    'feedback_collection': 'continuous_input',
                    'iteration_process': 'agile_approach'
                },
                'gradual_rollout': {
                    'wave_planning': 'logical_sequence',
                    'risk_mitigation': 'rollback_procedures',
                    'support_scaling': 'increase_capacity',
                    'performance_monitoring': 'real_time_tracking'
                }
            },
            'sustainment_phase': {
                'reinforcement_mechanisms': {
                    'success_stories': 'share_wins',
                    'continuous_improvement': 'feedback_loops',
                    'skill_development': 'ongoing_training',
                    'culture_embedding': 'behavior_modification'
                },
                'measurement_framework': {
                    'adoption_metrics': 'usage_statistics',
                    'satisfaction_scores': 'survey_results',
                    'business_impact': 'kpi_improvements',
                    'continuous_feedback': 'regular_assessments'
                }
            }
        }
        
        return change_management
    
    # Helper methods (implementations would be detailed in actual system)
    async def _initialize_infrastructure(self):
        pass
    
    async def _setup_network_infrastructure(self):
        pass
    
    async def _setup_storage_systems(self):
        pass
    
    async def _setup_monitoring_infrastructure(self):
        pass
    
    async def _setup_component_monitoring(self, component: EnterpriseComponent):
        pass
    
    async def _configure_component_security(self, component: EnterpriseComponent):
        pass
    
    async def _validate_integration_flow(self, flow: IntegrationFlow):
        pass
    
    async def _setup_error_handling(self, flow: IntegrationFlow):
        pass
    
    async def _setup_performance_monitoring(self, flow: IntegrationFlow):
        pass
    
    async def _deploy_integration_flow(self, flow: IntegrationFlow, pipeline: Dict):
        pass
    
    async def _start_platform_services(self):
        pass
    
    # Legacy integration helper methods
    async def _analyze_legacy_system(self, config: Dict) -> Dict:
        return {'analysis': 'complete'}
    
    async def _create_data_mapping(self, config: Dict) -> Dict:
        return {'mapping': 'defined'}
    
    async def _analyze_system_interfaces(self, config: Dict) -> Dict:
        return {'interfaces': 'analyzed'}
    
    async def _assess_integration_risks(self, config: Dict) -> Dict:
        return {'risks': 'assessed'}
    
    async def _develop_connectors(self, config: Dict) -> Dict:
        return {'connectors': 'developed'}
    
    async def _develop_transformations(self, config: Dict) -> Dict:
        return {'transformations': 'developed'}
    
    async def _setup_testing_framework(self, config: Dict) -> Dict:
        return {'testing': 'setup'}
    
    async def _implement_security(self, config: Dict) -> Dict:
        return {'security': 'implemented'}
    
    async def _plan_gradual_migration(self, config: Dict) -> Dict:
        return {'migration': 'planned'}
    
    async def _setup_parallel_operation(self, config: Dict) -> Dict:
        return {'parallel': 'setup'}
    
    async def _define_fallback_procedures(self, config: Dict) -> Dict:
        return {'fallback': 'defined'}
    
    async def _setup_integration_monitoring(self, config: Dict) -> Dict:
        return {'monitoring': 'setup'}
    
    async def _optimize_performance(self, config: Dict) -> Dict:
        return {'performance': 'optimized'}
    
    async def _improve_reliability(self, config: Dict) -> Dict:
        return {'reliability': 'improved'}
    
    async def _optimize_costs(self, config: Dict) -> Dict:
        return {'costs': 'optimized'}
    
    async def _create_integration_documentation(self, config: Dict) -> Dict:
        return {'documentation': 'created'}

# Example usage
async def main():
    # Initialize enterprise platform
    config = {
        'environment': 'production',
        'cloud_provider': 'aws',
        'region': 'us-east-1',
        'security_level': 'high',
        'compliance_requirements': ['sox', 'gdpr', 'soc2']
    }
    
    platform = EnterpriseMEVPlatform(config)
    await platform.initialize_platform()
    
    # Add enterprise components
    mev_component = EnterpriseComponent(
        component_id='mev_core',
        component_name='MEV Core Engine',
        component_type=SystemType.MEV_EXTRACTION,
        technology_stack={
            'language': 'python',
            'framework': 'fastapi',
            'database': 'postgresql',
            'message_queue': 'kafka',
            'deployment': 'kubernetes'
        },
        deployment_model='cloud',
        scaling_config={'min_instances': 3, 'max_instances': 50},
        security_config={'encryption': 'enabled', 'authentication': 'oauth2'},
        monitoring_config={'metrics': 'enabled', 'alerts': 'enabled'},
        dependencies=[],
        compliance_status='active',
        sla_requirements={'uptime': 0.999, 'latency': 0.1}
    )
    
    success = await platform.add_enterprise_component(mev_component)
    print(f"Component added: {success}")
    
    # Create integration flow
    integration_flow = IntegrationFlow(
        flow_id='mev_to_risk',
        flow_name='MEV to Risk Data Flow',
        source_components=['mev_core'],
        target_components=['risk_management'],
        transformations=[
            {'type': 'format_conversion', 'source': 'json', 'target': 'avro'},
            {'type': 'enrichment', 'fields': ['risk_score', 'confidence']}
        ],
        validation_rules=[
            {'type': 'schema_validation', 'required_fields': ['timestamp', 'profit']},
            {'type': 'data_quality', 'thresholds': {'completeness': 0.95}}
        ],
        error_handling={'strategy': 'dead_letter_queue', 'retry_attempts': 3},
        performance_metrics={'latency_target': 100, 'throughput_target': 1000},
        compliance_checks=['data_classification', 'audit_trail']
    )
    
    success = await platform.create_integration_flow(integration_flow)
    print(f"Integration flow created: {success}")
    
    # Generate integration plans
    legacy_config = {
        'system_type': 'trading_platform',
        'technology_stack': 'java_oracle',
        'data_volume': 'high',
        'integration_complexity': 'medium'
    }
    
    integration_plan = await platform.setup_legacy_integration(legacy_config)
    print(f"Legacy integration plan created")
    
    # Get architecture design
    architecture = await platform.create_scalable_architecture()
    print(f"Scalable architecture designed")
    
    # Get data governance framework
    data_governance = await platform.implement_data_governance()
    print(f"Data governance implemented")
    
    # Get change management program
    change_program = await platform.establish_change_management()
    print(f"Change management program established")

if __name__ == "__main__":
    asyncio.run(main())