# Advanced Monitoring & Analytics ## Enterprise-Scale Monitoring and Analytics Platform for Enhanced BMAD System The Advanced Monitoring & Analytics module provides sophisticated enterprise-grade monitoring, observability, and analytics capabilities that enable comprehensive visibility, predictive insights, and intelligent automation across all systems, processes, and business operations with real-time analytics and AI-powered monitoring. ### Advanced Monitoring & Analytics Architecture #### Comprehensive Monitoring and Analytics Platform ```yaml advanced_monitoring_analytics: monitoring_domains: infrastructure_monitoring: - system_performance_monitoring: "Real-time system performance monitoring and alerting" - network_monitoring_and_analysis: "Network performance monitoring and traffic analysis" - storage_monitoring_and_optimization: "Storage performance monitoring and capacity planning" - cloud_infrastructure_monitoring: "Multi-cloud infrastructure monitoring and optimization" - container_and_kubernetes_monitoring: "Container orchestration monitoring and observability" application_monitoring: - application_performance_monitoring: "APM with distributed tracing and profiling" - user_experience_monitoring: "Real user monitoring and synthetic testing" - api_monitoring_and_analytics: "API performance monitoring and usage analytics" - microservices_observability: "Microservices monitoring and service mesh observability" - database_performance_monitoring: "Database performance monitoring and optimization" business_process_monitoring: - business_transaction_monitoring: "End-to-end business transaction monitoring" - workflow_performance_monitoring: "Workflow execution monitoring and optimization" - sla_and_kpi_monitoring: "SLA compliance and KPI performance monitoring" - customer_journey_analytics: "Customer journey monitoring and experience analytics" - operational_efficiency_monitoring: "Operational process efficiency monitoring" security_monitoring: - security_event_monitoring: "Real-time security event monitoring and correlation" - threat_detection_and_analysis: "Advanced threat detection and behavioral analysis" - compliance_monitoring: "Continuous compliance monitoring and reporting" - access_pattern_monitoring: "User access pattern monitoring and anomaly detection" - data_security_monitoring: "Data access monitoring and protection analytics" log_management_and_analysis: - centralized_log_aggregation: "Centralized log collection and aggregation" - log_parsing_and_enrichment: "Intelligent log parsing and data enrichment" - log_analytics_and_insights: "Advanced log analytics and pattern recognition" - audit_trail_management: "Comprehensive audit trail management and analysis" - log_retention_and_archival: "Intelligent log retention and archival strategies" analytics_capabilities: real_time_analytics: - streaming_data_processing: "Real-time streaming data processing and analysis" - event_correlation_and_analysis: "Real-time event correlation and impact analysis" - anomaly_detection_algorithms: "ML-powered anomaly detection and alerting" - threshold_based_alerting: "Intelligent threshold-based monitoring and alerting" - real_time_dashboard_updates: "Real-time dashboard updates and visualizations" predictive_analytics: - capacity_planning_predictions: "Predictive capacity planning and resource forecasting" - performance_degradation_prediction: "Performance degradation prediction and prevention" - failure_prediction_and_prevention: "System failure prediction and proactive prevention" - demand_forecasting: "Demand forecasting and resource optimization" - trend_analysis_and_projection: "Trend analysis and future projection modeling" behavioral_analytics: - user_behavior_analytics: "User behavior analysis and pattern recognition" - system_behavior_profiling: "System behavior profiling and deviation detection" - application_usage_analytics: "Application usage patterns and optimization insights" - resource_utilization_patterns: "Resource utilization pattern analysis and optimization" - performance_pattern_recognition: "Performance pattern recognition and correlation" business_analytics: - operational_intelligence: "Operational intelligence and business insights" - customer_analytics: "Customer behavior analytics and segmentation" - financial_performance_analytics: "Financial performance monitoring and analysis" - market_intelligence_integration: "Market intelligence integration and analysis" - competitive_analysis_monitoring: "Competitive landscape monitoring and analysis" observability_platform: distributed_tracing: - end_to_end_request_tracing: "End-to-end request tracing across microservices" - service_dependency_mapping: "Service dependency mapping and visualization" - performance_bottleneck_identification: "Performance bottleneck identification and analysis" - error_propagation_tracking: "Error propagation tracking and root cause analysis" - trace_sampling_and_optimization: "Intelligent trace sampling and storage optimization" metrics_collection_and_analysis: - custom_metrics_definition: "Custom business and technical metrics definition" - metrics_aggregation_and_rollup: "Metrics aggregation and time-series rollup" - multi_dimensional_metrics: "Multi-dimensional metrics collection and analysis" - metrics_correlation_analysis: "Cross-metrics correlation and relationship analysis" - metrics_based_alerting: "Metrics-based intelligent alerting and escalation" event_driven_monitoring: - event_stream_processing: "Real-time event stream processing and analysis" - complex_event_processing: "Complex event processing and pattern matching" - event_correlation_engines: "Multi-source event correlation and analysis" - event_driven_automation: "Event-driven automation and response systems" - event_sourcing_and_replay: "Event sourcing and historical event replay" visualization_and_dashboards: - interactive_dashboard_creation: "Interactive dashboard creation and customization" - real_time_data_visualization: "Real-time data visualization and updates" - drill_down_and_exploration: "Multi-level drill-down and data exploration" - mobile_responsive_dashboards: "Mobile-responsive dashboard interfaces" - collaborative_dashboard_sharing: "Collaborative dashboard sharing and annotation" automation_and_intelligence: intelligent_alerting: - smart_alert_correlation: "Smart alert correlation and noise reduction" - contextual_alert_enrichment: "Contextual alert enrichment and prioritization" - predictive_alerting: "Predictive alerting based on trend analysis" - escalation_and_routing: "Intelligent alert escalation and routing" - alert_feedback_learning: "Alert feedback learning and optimization" automated_remediation: - self_healing_systems: "Self-healing system automation and recovery" - automated_scaling_responses: "Automated scaling responses to demand changes" - performance_optimization_automation: "Automated performance optimization actions" - security_response_automation: "Automated security incident response" - workflow_automation_triggers: "Monitoring-driven workflow automation triggers" machine_learning_integration: - anomaly_detection_models: "ML-powered anomaly detection and classification" - predictive_maintenance_models: "Predictive maintenance and lifecycle management" - optimization_recommendation_engines: "ML-driven optimization recommendation engines" - natural_language_processing: "NLP for log analysis and alert interpretation" - reinforcement_learning_optimization: "RL-based system optimization and tuning" aiops_capabilities: - intelligent_incident_management: "AI-powered incident management and resolution" - root_cause_analysis_automation: "Automated root cause analysis and diagnosis" - performance_optimization_ai: "AI-driven performance optimization recommendations" - capacity_planning_ai: "AI-powered capacity planning and resource optimization" - predictive_analytics_ai: "AI-enhanced predictive analytics and forecasting" ``` #### Advanced Monitoring & Analytics Implementation ```python import asyncio import pandas as pd import numpy as np from typing import Dict, List, Any, Optional, Tuple, Union from dataclasses import dataclass, field from enum import Enum from datetime import datetime, timedelta import json import uuid from collections import defaultdict, deque import logging from abc import ABC, abstractmethod import time import threading import multiprocessing from concurrent.futures import ThreadPoolExecutor import psutil import networkx as nx from sklearn.ensemble import IsolationForest from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler import plotly.graph_objects as go import plotly.express as px from scipy import stats import warnings warnings.filterwarnings('ignore') class MonitoringType(Enum): INFRASTRUCTURE = "infrastructure" APPLICATION = "application" BUSINESS = "business" SECURITY = "security" NETWORK = "network" USER_EXPERIENCE = "user_experience" class AlertSeverity(Enum): CRITICAL = "critical" HIGH = "high" MEDIUM = "medium" LOW = "low" INFO = "info" class MetricType(Enum): COUNTER = "counter" GAUGE = "gauge" HISTOGRAM = "histogram" SUMMARY = "summary" TIMER = "timer" class AnalyticsType(Enum): DESCRIPTIVE = "descriptive" DIAGNOSTIC = "diagnostic" PREDICTIVE = "predictive" PRESCRIPTIVE = "prescriptive" @dataclass class MonitoringMetric: """ Represents a monitoring metric with metadata and configuration """ metric_id: str name: str type: MetricType monitoring_type: MonitoringType description: str unit: str collection_interval: int # seconds retention_period: int # days labels: Dict[str, str] = field(default_factory=dict) thresholds: Dict[str, float] = field(default_factory=dict) aggregation_rules: List[str] = field(default_factory=list) alerting_enabled: bool = True @dataclass class MonitoringAlert: """ Represents a monitoring alert with context and severity """ alert_id: str title: str description: str severity: AlertSeverity monitoring_type: MonitoringType triggered_time: datetime source_metric: str current_value: float threshold_value: float labels: Dict[str, str] = field(default_factory=dict) context: Dict[str, Any] = field(default_factory=dict) escalation_rules: List[Dict[str, Any]] = field(default_factory=list) resolution_time: Optional[datetime] = None acknowledged: bool = False @dataclass class AnalyticsInsight: """ Represents an analytics insight generated from monitoring data """ insight_id: str title: str description: str analytics_type: AnalyticsType confidence_score: float impact_level: str # high, medium, low time_horizon: str # immediate, short_term, medium_term, long_term affected_systems: List[str] = field(default_factory=list) recommendations: List[str] = field(default_factory=list) supporting_data: Dict[str, Any] = field(default_factory=dict) created_time: datetime = field(default_factory=datetime.utcnow) class AdvancedMonitoringAnalytics: """ Enterprise-scale monitoring and analytics platform """ def __init__(self, claude_code_interface, config=None): self.claude_code = claude_code_interface self.config = config or { 'real_time_processing': True, 'predictive_analytics': True, 'anomaly_detection': True, 'automated_remediation': True, 'alert_correlation': True, 'data_retention_days': 365, 'metrics_collection_interval': 60, 'alert_evaluation_interval': 30, 'ml_model_training_interval_hours': 24 } # Core monitoring components self.metrics_collector = MetricsCollector(self.claude_code, self.config) self.log_manager = LogManager(self.config) self.event_processor = EventProcessor(self.config) self.trace_manager = DistributedTraceManager(self.config) # Analytics engines self.real_time_analytics = RealTimeAnalyticsEngine(self.config) self.predictive_analytics = PredictiveAnalyticsEngine(self.config) self.behavioral_analytics = BehavioralAnalyticsEngine(self.config) self.business_analytics = BusinessAnalyticsEngine(self.config) # Alerting and automation self.alert_manager = AlertManager(self.config) self.automation_engine = MonitoringAutomationEngine(self.config) self.remediation_engine = AutomatedRemediationEngine(self.config) self.escalation_manager = EscalationManager(self.config) # Observability platform self.observability_platform = ObservabilityPlatform(self.config) self.dashboard_service = MonitoringDashboardService(self.config) self.visualization_engine = VisualizationEngine(self.config) self.reporting_engine = MonitoringReportingEngine(self.config) # AI and ML components self.anomaly_detector = AnomalyDetector(self.config) self.ml_engine = MonitoringMLEngine(self.config) self.aiops_engine = AIOpsEngine(self.config) self.nlp_processor = LogNLPProcessor(self.config) # State management self.metric_repository = MetricRepository() self.alert_repository = AlertRepository() self.insight_repository = InsightRepository() self.monitoring_state = MonitoringState() # Integration and data management self.data_pipeline = MonitoringDataPipeline(self.config) self.integration_manager = MonitoringIntegrationManager(self.config) self.storage_manager = MonitoringStorageManager(self.config) async def setup_comprehensive_monitoring(self, monitoring_scope, requirements): """ Setup comprehensive monitoring across all domains """ monitoring_setup = { 'setup_id': generate_uuid(), 'start_time': datetime.utcnow(), 'monitoring_scope': monitoring_scope, 'requirements': requirements, 'infrastructure_monitoring': {}, 'application_monitoring': {}, 'business_monitoring': {}, 'security_monitoring': {}, 'analytics_configuration': {} } try: # Analyze monitoring requirements monitoring_analysis = await self.analyze_monitoring_requirements( monitoring_scope, requirements ) monitoring_setup['monitoring_analysis'] = monitoring_analysis # Setup infrastructure monitoring infrastructure_monitoring = await self.setup_infrastructure_monitoring( monitoring_analysis ) monitoring_setup['infrastructure_monitoring'] = infrastructure_monitoring # Setup application monitoring application_monitoring = await self.setup_application_monitoring( monitoring_analysis ) monitoring_setup['application_monitoring'] = application_monitoring # Setup business process monitoring business_monitoring = await self.setup_business_monitoring( monitoring_analysis ) monitoring_setup['business_monitoring'] = business_monitoring # Setup security monitoring security_monitoring = await self.setup_security_monitoring( monitoring_analysis ) monitoring_setup['security_monitoring'] = security_monitoring # Configure analytics and AI analytics_configuration = await self.configure_monitoring_analytics( monitoring_analysis ) monitoring_setup['analytics_configuration'] = analytics_configuration # Setup alerting and automation alerting_setup = await self.setup_alerting_and_automation( monitoring_setup ) monitoring_setup['alerting_setup'] = alerting_setup # Configure dashboards and visualization dashboard_setup = await self.setup_monitoring_dashboards( monitoring_setup ) monitoring_setup['dashboard_setup'] = dashboard_setup # Initialize data pipeline data_pipeline_setup = await self.initialize_monitoring_data_pipeline( monitoring_setup ) monitoring_setup['data_pipeline_setup'] = data_pipeline_setup except Exception as e: monitoring_setup['error'] = str(e) finally: monitoring_setup['end_time'] = datetime.utcnow() monitoring_setup['setup_duration'] = ( monitoring_setup['end_time'] - monitoring_setup['start_time'] ).total_seconds() return monitoring_setup async def analyze_monitoring_requirements(self, monitoring_scope, requirements): """ Analyze monitoring requirements and scope """ monitoring_analysis = { 'infrastructure_requirements': {}, 'application_requirements': {}, 'business_requirements': {}, 'compliance_requirements': {}, 'performance_requirements': {}, 'scalability_requirements': {}, 'integration_requirements': {} } # Analyze infrastructure requirements infrastructure_requirements = await self.analyze_infrastructure_monitoring_requirements( monitoring_scope, requirements ) monitoring_analysis['infrastructure_requirements'] = infrastructure_requirements # Analyze application requirements application_requirements = await self.analyze_application_monitoring_requirements( monitoring_scope, requirements ) monitoring_analysis['application_requirements'] = application_requirements # Analyze business requirements business_requirements = await self.analyze_business_monitoring_requirements( monitoring_scope, requirements ) monitoring_analysis['business_requirements'] = business_requirements # Analyze compliance requirements compliance_requirements = await self.analyze_compliance_monitoring_requirements( requirements ) monitoring_analysis['compliance_requirements'] = compliance_requirements return monitoring_analysis async def perform_real_time_analytics(self, data_stream): """ Perform real-time analytics on streaming monitoring data """ analytics_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'data_points_processed': 0, 'anomalies_detected': [], 'patterns_identified': [], 'alerts_generated': [], 'insights_generated': [] } try: # Process data stream in real-time async for data_batch in data_stream: # Update data points counter analytics_session['data_points_processed'] += len(data_batch) # Perform anomaly detection anomalies = await self.anomaly_detector.detect_anomalies_batch(data_batch) if anomalies: analytics_session['anomalies_detected'].extend(anomalies) # Generate alerts for anomalies anomaly_alerts = await self.generate_anomaly_alerts(anomalies) analytics_session['alerts_generated'].extend(anomaly_alerts) # Identify patterns patterns = await self.real_time_analytics.identify_patterns(data_batch) analytics_session['patterns_identified'].extend(patterns) # Generate real-time insights insights = await self.generate_real_time_insights( data_batch, anomalies, patterns ) analytics_session['insights_generated'].extend(insights) # Update monitoring state await self.monitoring_state.update_from_batch(data_batch) # Process alerts and automation for alert in anomaly_alerts: await self.alert_manager.process_alert(alert) except Exception as e: analytics_session['error'] = str(e) finally: analytics_session['end_time'] = datetime.utcnow() analytics_session['processing_duration'] = ( analytics_session['end_time'] - analytics_session['start_time'] ).total_seconds() return analytics_session async def generate_predictive_insights(self, historical_data, prediction_horizon="7d"): """ Generate predictive insights from historical monitoring data """ prediction_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'prediction_horizon': prediction_horizon, 'data_analyzed': len(historical_data), 'predictions_generated': [], 'risk_assessments': [], 'recommendations': [] } try: # Prepare data for prediction prepared_data = await self.predictive_analytics.prepare_prediction_data( historical_data ) # Generate capacity predictions capacity_predictions = await self.predictive_analytics.predict_capacity_requirements( prepared_data, prediction_horizon ) prediction_session['predictions_generated'].extend(capacity_predictions) # Generate performance predictions performance_predictions = await self.predictive_analytics.predict_performance_trends( prepared_data, prediction_horizon ) prediction_session['predictions_generated'].extend(performance_predictions) # Generate failure risk predictions failure_predictions = await self.predictive_analytics.predict_failure_risks( prepared_data, prediction_horizon ) prediction_session['predictions_generated'].extend(failure_predictions) # Assess risks based on predictions risk_assessments = await self.assess_prediction_risks( prediction_session['predictions_generated'] ) prediction_session['risk_assessments'] = risk_assessments # Generate recommendations recommendations = await self.generate_predictive_recommendations( prediction_session['predictions_generated'], risk_assessments ) prediction_session['recommendations'] = recommendations # Create predictive alerts predictive_alerts = await self.create_predictive_alerts( prediction_session['predictions_generated'], risk_assessments ) prediction_session['predictive_alerts'] = predictive_alerts except Exception as e: prediction_session['error'] = str(e) finally: prediction_session['end_time'] = datetime.utcnow() prediction_session['prediction_duration'] = ( prediction_session['end_time'] - prediction_session['start_time'] ).total_seconds() return prediction_session async def setup_infrastructure_monitoring(self, monitoring_analysis): """ Setup comprehensive infrastructure monitoring """ infrastructure_monitoring = { 'system_monitoring': {}, 'network_monitoring': {}, 'storage_monitoring': {}, 'cloud_monitoring': {}, 'container_monitoring': {} } # Setup system performance monitoring system_monitoring = await self.setup_system_monitoring() infrastructure_monitoring['system_monitoring'] = system_monitoring # Setup network monitoring network_monitoring = await self.setup_network_monitoring() infrastructure_monitoring['network_monitoring'] = network_monitoring # Setup storage monitoring storage_monitoring = await self.setup_storage_monitoring() infrastructure_monitoring['storage_monitoring'] = storage_monitoring # Setup cloud monitoring cloud_monitoring = await self.setup_cloud_monitoring() infrastructure_monitoring['cloud_monitoring'] = cloud_monitoring return infrastructure_monitoring async def setup_system_monitoring(self): """ Setup system performance monitoring """ system_monitoring = { 'cpu_monitoring': True, 'memory_monitoring': True, 'disk_monitoring': True, 'process_monitoring': True, 'service_monitoring': True } # Configure CPU monitoring cpu_metrics = [ MonitoringMetric( metric_id="system_cpu_usage", name="CPU Usage Percentage", type=MetricType.GAUGE, monitoring_type=MonitoringType.INFRASTRUCTURE, description="System CPU utilization percentage", unit="percent", collection_interval=60, retention_period=90, thresholds={ 'warning': 70.0, 'critical': 90.0 } ), MonitoringMetric( metric_id="system_load_average", name="System Load Average", type=MetricType.GAUGE, monitoring_type=MonitoringType.INFRASTRUCTURE, description="System load average (1, 5, 15 minutes)", unit="load", collection_interval=60, retention_period=90, thresholds={ 'warning': 2.0, 'critical': 4.0 } ) ] # Configure memory monitoring memory_metrics = [ MonitoringMetric( metric_id="system_memory_usage", name="Memory Usage Percentage", type=MetricType.GAUGE, monitoring_type=MonitoringType.INFRASTRUCTURE, description="System memory utilization percentage", unit="percent", collection_interval=60, retention_period=90, thresholds={ 'warning': 80.0, 'critical': 95.0 } ) ] # Register metrics for metric in cpu_metrics + memory_metrics: await self.metric_repository.register_metric(metric) system_monitoring['metrics_configured'] = len(cpu_metrics + memory_metrics) return system_monitoring async def setup_application_monitoring(self, monitoring_analysis): """ Setup comprehensive application monitoring """ application_monitoring = { 'apm_configuration': {}, 'user_experience_monitoring': {}, 'api_monitoring': {}, 'database_monitoring': {}, 'microservices_monitoring': {} } # Configure APM apm_configuration = await self.configure_application_performance_monitoring() application_monitoring['apm_configuration'] = apm_configuration # Configure user experience monitoring ux_monitoring = await self.configure_user_experience_monitoring() application_monitoring['user_experience_monitoring'] = ux_monitoring # Configure API monitoring api_monitoring = await self.configure_api_monitoring() application_monitoring['api_monitoring'] = api_monitoring return application_monitoring async def configure_application_performance_monitoring(self): """ Configure application performance monitoring """ apm_config = { 'distributed_tracing': True, 'transaction_profiling': True, 'error_tracking': True, 'performance_profiling': True, 'dependency_mapping': True } # Configure application metrics app_metrics = [ MonitoringMetric( metric_id="app_response_time", name="Application Response Time", type=MetricType.HISTOGRAM, monitoring_type=MonitoringType.APPLICATION, description="Application response time distribution", unit="milliseconds", collection_interval=30, retention_period=30, thresholds={ 'warning': 1000.0, 'critical': 5000.0 } ), MonitoringMetric( metric_id="app_throughput", name="Application Throughput", type=MetricType.COUNTER, monitoring_type=MonitoringType.APPLICATION, description="Application requests per second", unit="requests/second", collection_interval=30, retention_period=30, thresholds={ 'warning': 100.0, 'critical': 50.0 } ), MonitoringMetric( metric_id="app_error_rate", name="Application Error Rate", type=MetricType.GAUGE, monitoring_type=MonitoringType.APPLICATION, description="Application error rate percentage", unit="percent", collection_interval=30, retention_period=90, thresholds={ 'warning': 1.0, 'critical': 5.0 } ) ] # Register application metrics for metric in app_metrics: await self.metric_repository.register_metric(metric) apm_config['metrics_configured'] = len(app_metrics) return apm_config class MetricsCollector: """ Collects metrics from various sources """ def __init__(self, claude_code, config): self.claude_code = claude_code self.config = config self.collection_tasks = {} async def start_collection(self, metrics_configuration): """ Start metrics collection based on configuration """ collection_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'metrics_configured': len(metrics_configuration), 'collection_tasks_started': 0, 'data_points_collected': 0 } # Start collection tasks for each metric type for metric_config in metrics_configuration: if metric_config.monitoring_type == MonitoringType.INFRASTRUCTURE: task = asyncio.create_task( self.collect_infrastructure_metrics(metric_config) ) elif metric_config.monitoring_type == MonitoringType.APPLICATION: task = asyncio.create_task( self.collect_application_metrics(metric_config) ) else: task = asyncio.create_task( self.collect_generic_metrics(metric_config) ) self.collection_tasks[metric_config.metric_id] = task collection_session['collection_tasks_started'] += 1 return collection_session async def collect_infrastructure_metrics(self, metric_config): """ Collect infrastructure metrics """ while True: try: # Collect system metrics based on metric type if 'cpu' in metric_config.metric_id: value = psutil.cpu_percent(interval=1) elif 'memory' in metric_config.metric_id: value = psutil.virtual_memory().percent elif 'disk' in metric_config.metric_id: value = psutil.disk_usage('/').percent elif 'load' in metric_config.metric_id: value = psutil.getloadavg()[0] if hasattr(psutil, 'getloadavg') else 0.0 else: value = 0.0 # Default value # Create metric data point data_point = { 'metric_id': metric_config.metric_id, 'timestamp': datetime.utcnow(), 'value': value, 'labels': metric_config.labels } # Store metric data point await self.store_metric_data_point(data_point) # Wait for next collection interval await asyncio.sleep(metric_config.collection_interval) except Exception as e: logging.error(f"Error collecting metric {metric_config.metric_id}: {e}") await asyncio.sleep(metric_config.collection_interval) async def store_metric_data_point(self, data_point): """ Store metric data point """ # In practice, this would store to a time-series database # For now, we'll just log it logging.info(f"Metric collected: {data_point}") class AnomalyDetector: """ AI-powered anomaly detection for monitoring data """ def __init__(self, config): self.config = config self.models = {} self.scaler = StandardScaler() async def detect_anomalies_batch(self, data_batch): """ Detect anomalies in a batch of monitoring data """ anomalies = [] try: # Prepare data for anomaly detection df = pd.DataFrame(data_batch) if len(df) < 10: # Need minimum data points return anomalies # Extract numerical features numerical_features = df.select_dtypes(include=[np.number]).columns if len(numerical_features) == 0: return anomalies # Normalize data normalized_data = self.scaler.fit_transform(df[numerical_features]) # Use Isolation Forest for anomaly detection iso_forest = IsolationForest(contamination=0.1, random_state=42) anomaly_labels = iso_forest.fit_predict(normalized_data) # Identify anomalies for i, label in enumerate(anomaly_labels): if label == -1: # Anomaly detected anomaly = { 'anomaly_id': generate_uuid(), 'data_point': data_batch[i], 'anomaly_score': iso_forest.score_samples([normalized_data[i]])[0], 'detection_time': datetime.utcnow(), 'features_affected': numerical_features.tolist() } anomalies.append(anomaly) except Exception as e: logging.error(f"Error in anomaly detection: {e}") return anomalies def generate_uuid(): """Generate a UUID string""" return str(uuid.uuid4()) # Additional classes would be implemented here: # - LogManager # - EventProcessor # - DistributedTraceManager # - RealTimeAnalyticsEngine # - PredictiveAnalyticsEngine # - BehavioralAnalyticsEngine # - BusinessAnalyticsEngine # - AlertManager # - MonitoringAutomationEngine # - AutomatedRemediationEngine # - EscalationManager # - ObservabilityPlatform # - MonitoringDashboardService # - VisualizationEngine # - MonitoringReportingEngine # - MonitoringMLEngine # - AIOpsEngine # - LogNLPProcessor # - MetricRepository # - AlertRepository # - InsightRepository # - MonitoringState # - MonitoringDataPipeline # - MonitoringIntegrationManager # - MonitoringStorageManager ``` ### Advanced Monitoring & Analytics Commands ```bash # Infrastructure monitoring setup bmad monitor infrastructure --setup --comprehensive --predictive bmad monitor system --cpu --memory --disk --network --real-time bmad monitor cloud --multi-cloud --auto-scaling --cost-optimization # Application performance monitoring bmad monitor application --apm --distributed-tracing --profiling bmad monitor api --performance --usage-analytics --error-tracking bmad monitor user-experience --real-user --synthetic --journey-analytics # Business process monitoring bmad monitor business --transactions --workflows --kpis bmad monitor operations --efficiency --sla-compliance --process-analytics bmad monitor customer --journey --satisfaction --behavior-analytics # Security and compliance monitoring bmad monitor security --events --threats --behavioral-analytics bmad monitor compliance --continuous --regulatory --audit-trail bmad monitor access --patterns --anomalies --privilege-escalation # Real-time analytics and insights bmad analytics real-time --streaming --event-correlation --anomaly-detection bmad analytics predictive --capacity-planning --failure-prediction bmad analytics behavioral --user-patterns --system-behavior --optimization # AI-powered monitoring and AIOps bmad monitor ai --anomaly-detection --root-cause-analysis --auto-remediation bmad monitor ml --pattern-recognition --predictive-maintenance bmad monitor nlp --log-analysis --alert-interpretation --insights # Alerting and automation bmad alert setup --intelligent --correlation --escalation bmad alert automate --response --remediation --workflows bmad alert optimize --noise-reduction --context-enrichment # Dashboards and visualization bmad monitor dashboard --create --real-time --executive --operational bmad monitor visualize --interactive --drill-down --mobile-responsive bmad monitor report --automated --stakeholder-specific --scheduled # Data management and integration bmad monitor data --pipeline --integration --retention --archival bmad monitor integrate --tools --platforms --apis --webhooks bmad monitor storage --time-series --optimization --compression ``` This Advanced Monitoring & Analytics module provides sophisticated enterprise-grade monitoring, observability, and analytics capabilities that enable comprehensive visibility, predictive insights, and intelligent automation across all systems, processes, and business operations with real-time analytics and AI-powered monitoring throughout the entire enterprise ecosystem.