Advanced FinOps on OCI: AI-Driven Cost Optimization and Cloud Financial Intelligence

import oci
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import warnings
warnings.filterwarnings('ignore')

class OCIFinOpsAnalytics:
    def __init__(self, config_file="~/.oci/config"):
        """
        Initialize OCI FinOps Analytics with advanced ML capabilities
        """
        self.config = oci.config.from_file(config_file)
        self.usage_client = oci.usage_api.UsageapiClient(self.config)
        self.monitoring_client = oci.monitoring.MonitoringClient(self.config)
        self.analytics_client = oci.analytics.AnalyticsClient(self.config)
        
        # Initialize ML models for anomaly detection and forecasting
        self.anomaly_detector = IsolationForest(contamination=0.1, random_state=42)
        self.cost_forecaster = LinearRegression()
        self.scaler = StandardScaler()
        
    def collect_comprehensive_usage_data(self, tenancy_id, days_back=90):
        """
        Collect detailed usage and cost data across all OCI services
        """
        end_time = datetime.utcnow()
        start_time = end_time - timedelta(days=days_back)
        
        # Request detailed usage data
        request_usage_details = oci.usage_api.models.RequestSummarizedUsagesDetails(
            tenant_id=tenancy_id,
            time_usage_started=start_time,
            time_usage_ended=end_time,
            granularity="DAILY",
            group_by=["service", "resourceId", "compartmentName"]
        )
        
        try:
            usage_response = self.usage_client.request_summarized_usages(
                request_usage_details
            )
            
            # Convert to structured data
            usage_data = []
            for item in usage_response.data.items:
                usage_data.append({
                    'date': item.time_usage_started.date(),
                    'service': item.service,
                    'resource_id': item.resource_id,
                    'compartment': item.compartment_name,
                    'computed_amount': float(item.computed_amount) if item.computed_amount else 0,
                    'computed_quantity': float(item.computed_quantity) if item.computed_quantity else 0,
                    'unit': item.unit,
                    'currency': item.currency
                })
            
            return pd.DataFrame(usage_data)
            
        except Exception as e:
            print(f"Error collecting usage data: {e}")
            return pd.DataFrame()
    
    def perform_anomaly_detection(self, cost_data):
        """
        Use ML to detect cost anomalies and unusual spending patterns
        """
        # Prepare features for anomaly detection
        daily_costs = cost_data.groupby(['date', 'service'])['computed_amount'].sum().reset_index()
        
        # Create feature matrix
        features_list = []
        for service in daily_costs['service'].unique():
            service_data = daily_costs[daily_costs['service'] == service].copy()
            service_data = service_data.sort_values('date')
            
            # Calculate rolling statistics
            service_data['rolling_mean_7d'] = service_data['computed_amount'].rolling(7, min_periods=1).mean()
            service_data['rolling_std_7d'] = service_data['computed_amount'].rolling(7, min_periods=1).std()
            service_data['rolling_mean_30d'] = service_data['computed_amount'].rolling(30, min_periods=1).mean()
            
            # Calculate percentage change
            service_data['pct_change'] = service_data['computed_amount'].pct_change()
            service_data['days_since_start'] = (service_data['date'] - service_data['date'].min()).dt.days
            
            # Create features for anomaly detection
            features = service_data[['computed_amount', 'rolling_mean_7d', 'rolling_std_7d', 
                                   'rolling_mean_30d', 'pct_change', 'days_since_start']].fillna(0)
            
            if len(features) > 5:  # Need sufficient data points
                # Scale features
                features_scaled = self.scaler.fit_transform(features)
                
                # Detect anomalies
                anomalies = self.anomaly_detector.fit_predict(features_scaled)
                
                service_data['anomaly'] = anomalies
                service_data['anomaly_score'] = self.anomaly_detector.decision_function(features_scaled)
                
                features_list.append(service_data)
        
        if features_list:
            return pd.concat(features_list, ignore_index=True)
        else:
            return pd.DataFrame()
    
    def forecast_costs_with_ml(self, cost_data, forecast_days=30):
        """
        Generate ML-powered cost forecasts with confidence intervals
        """
        forecasts = {}
        
        # Group by service for individual forecasting
        for service in cost_data['service'].unique():
            service_data = cost_data[cost_data['service'] == service].copy()
            daily_costs = service_data.groupby('date')['computed_amount'].sum().reset_index()
            daily_costs = daily_costs.sort_values('date')
            
            if len(daily_costs) < 14:  # Need minimum data for reliable forecast
                continue
                
            # Prepare features for forecasting
            daily_costs['days_since_start'] = (daily_costs['date'] - daily_costs['date'].min()).dt.days
            daily_costs['day_of_week'] = daily_costs['date'].dt.dayofweek
            daily_costs['month'] = daily_costs['date'].dt.month
            daily_costs['rolling_mean_7d'] = daily_costs['computed_amount'].rolling(7, min_periods=1).mean()
            daily_costs['rolling_mean_14d'] = daily_costs['computed_amount'].rolling(14, min_periods=1).mean()
            
            # Features for training
            feature_cols = ['days_since_start', 'day_of_week', 'month', 'rolling_mean_7d', 'rolling_mean_14d']
            X = daily_costs[feature_cols].fillna(method='ffill').fillna(0)
            y = daily_costs['computed_amount']
            
            # Train forecasting model
            self.cost_forecaster.fit(X, y)
            
            # Generate forecasts
            last_date = daily_costs['date'].max()
            forecast_dates = [last_date + timedelta(days=i) for i in range(1, forecast_days + 1)]
            
            forecast_features = []
            for i, future_date in enumerate(forecast_dates):
                last_row = daily_costs.iloc[-1].copy()
                
                features = {
                    'days_since_start': last_row['days_since_start'] + i + 1,
                    'day_of_week': future_date.weekday(),
                    'month': future_date.month,
                    'rolling_mean_7d': last_row['rolling_mean_7d'],
                    'rolling_mean_14d': last_row['rolling_mean_14d']
                }
                forecast_features.append(features)
            
            forecast_df = pd.DataFrame(forecast_features)
            predictions = self.cost_forecaster.predict(forecast_df[feature_cols])
            
            # Calculate confidence intervals (simplified approach)
            residuals = y - self.cost_forecaster.predict(X)
            std_residual = np.std(residuals)
            
            forecasts[service] = {
                'dates': forecast_dates,
                'predictions': predictions,
                'lower_bound': predictions - 1.96 * std_residual,
                'upper_bound': predictions + 1.96 * std_residual,
                'model_score': self.cost_forecaster.score(X, y)
            }
        
        return forecasts
    
    def analyze_resource_efficiency(self, cost_data, performance_data=None):
        """
        Analyze resource efficiency and identify optimization opportunities
        """
        efficiency_insights = {
            'underutilized_resources': [],
            'oversized_instances': [],
            'cost_optimization_opportunities': [],
            'efficiency_scores': {}
        }
        
        # Analyze cost trends by resource
        resource_analysis = cost_data.groupby(['service', 'resource_id']).agg({
            'computed_amount': ['sum', 'mean', 'std'],
            'computed_quantity': ['sum', 'mean', 'std']
        }).reset_index()
        
        resource_analysis.columns = ['service', 'resource_id', 'total_cost', 'avg_daily_cost', 
                                   'cost_volatility', 'total_usage', 'avg_daily_usage', 'usage_volatility']
        
        # Identify underutilized resources (high cost, low usage variance)
        for _, resource in resource_analysis.iterrows():
            if resource['total_cost'] > 100:  # Focus on significant costs
                efficiency_score = resource['avg_daily_usage'] / (resource['total_cost'] / 30)  # Usage per dollar
                
                if resource['usage_volatility'] < resource['avg_daily_usage'] * 0.1:  # Low usage variance
                    efficiency_insights['underutilized_resources'].append({
                        'service': resource['service'],
                        'resource_id': resource['resource_id'],
                        'total_cost': resource['total_cost'],
                        'efficiency_score': efficiency_score,
                        'recommendation': 'Consider downsizing or scheduled shutdown'
                    })
                
                efficiency_insights['efficiency_scores'][resource['resource_id']] = efficiency_score
        
        return efficiency_insights
    
    def generate_intelligent_recommendations(self, cost_data, anomalies, forecasts, efficiency_analysis):
        """
        Generate AI-powered cost optimization recommendations
        """
        recommendations = {
            'immediate_actions': [],
            'strategic_initiatives': [],
            'budget_adjustments': [],
            'automation_opportunities': []
        }
        
        # Immediate actions based on anomalies
        if not anomalies.empty:
            recent_anomalies = anomalies[anomalies['anomaly'] == -1]
            recent_anomalies = recent_anomalies[recent_anomalies['date'] >= (datetime.now().date() - timedelta(days=7))]
            
            for _, anomaly in recent_anomalies.iterrows():
                recommendations['immediate_actions'].append({
                    'priority': 'HIGH',
                    'service': anomaly['service'],
                    'issue': f"Cost anomaly detected: ${anomaly['computed_amount']:.2f} vs expected ${anomaly['rolling_mean_7d']:.2f}",
                    'action': 'Investigate resource usage and check for misconfiguration',
                    'potential_savings': abs(anomaly['computed_amount'] - anomaly['rolling_mean_7d'])
                })
        
        # Strategic initiatives based on forecasts
        total_forecasted_cost = 0
        for service, forecast in forecasts.items():
            monthly_forecast = sum(forecast['predictions'])
            total_forecasted_cost += monthly_forecast
            
            if monthly_forecast > 10000:  # High-cost services
                recommendations['strategic_initiatives'].append({
                    'service': service,
                    'forecasted_monthly_cost': monthly_forecast,
                    'confidence': forecast['model_score'],
                    'recommendation': 'Consider reserved capacity or committed use discounts',
                    'potential_savings': monthly_forecast * 0.2  # Assume 20% savings potential
                })
        
        # Budget adjustments
        if total_forecasted_cost > 0:
            recommendations['budget_adjustments'].append({
                'current_trend': 'INCREASING' if total_forecasted_cost > cost_data['computed_amount'].sum() else 'STABLE',
                'forecasted_monthly_spend': total_forecasted_cost,
                'recommended_budget': total_forecasted_cost * 1.15,  # 15% buffer
                'confidence_level': 'MEDIUM'
            })
        
        # Automation opportunities based on efficiency analysis
        for resource in efficiency_analysis['underutilized_resources'][:5]:  # Top 5 opportunities
            recommendations['automation_opportunities'].append({
                'resource_id': resource['resource_id'],
                'service': resource['service'],
                'automation_type': 'AUTO_SCALING',
                'estimated_savings': resource['total_cost'] * 0.3,  # Conservative 30% savings
                'implementation_complexity': 'MEDIUM'
            })
        
        return recommendations

def create_advanced_cost_dashboard(finops_analytics, tenancy_id):
    """
    Create a comprehensive FinOps dashboard with AI insights
    """
    print("🔄 Collecting comprehensive usage data...")
    cost_data = finops_analytics.collect_comprehensive_usage_data(tenancy_id, days_back=60)
    
    if cost_data.empty:
        print("❌ No cost data available")
        return
    
    print(f"✅ Collected {len(cost_data)} cost records")
    
    print("🤖 Performing AI-powered anomaly detection...")
    anomalies = finops_analytics.perform_anomaly_detection(cost_data)
    
    print("📈 Generating ML-powered cost forecasts...")
    forecasts = finops_analytics.forecast_costs_with_ml(cost_data, forecast_days=30)
    
    print("⚡ Analyzing resource efficiency...")
    efficiency_analysis = finops_analytics.analyze_resource_efficiency(cost_data)
    
    print("🧠 Generating intelligent recommendations...")
    recommendations = finops_analytics.generate_intelligent_recommendations(
        cost_data, anomalies, forecasts, efficiency_analysis
    )
    
    # Display results
    print("\n" + "="*60)
    print("FINOPS INTELLIGENCE DASHBOARD")
    print("="*60)
    
    # Cost Summary
    total_cost = cost_data['computed_amount'].sum()
    avg_daily_cost = cost_data.groupby('date')['computed_amount'].sum().mean()
    
    print(f"\n💰 COST SUMMARY")
    print(f"Total Cost (60 days): ${total_cost:,.2f}")
    print(f"Average Daily Cost: ${avg_daily_cost:,.2f}")
    print(f"Projected Monthly Cost: ${avg_daily_cost * 30:,.2f}")
    
    # Top services by cost
    top_services = cost_data.groupby('service')['computed_amount'].sum().sort_values(ascending=False).head(5)
    print(f"\n📊 TOP 5 SERVICES BY COST:")
    for service, cost in top_services.items():
        percentage = (cost / total_cost) * 100
        print(f"  {service}: ${cost:,.2f} ({percentage:.1f}%)")
    
    # Anomaly alerts
    if not anomalies.empty:
        recent_anomalies = anomalies[anomalies['anomaly'] == -1]
        recent_anomalies = recent_anomalies[recent_anomalies['date'] >= (datetime.now().date() - timedelta(days=7))]
        
        if not recent_anomalies.empty:
            print(f"\n🚨 RECENT COST ANOMALIES ({len(recent_anomalies)}):")
            for _, anomaly in recent_anomalies.head(3).iterrows():
                print(f"  {anomaly['service']}: ${anomaly['computed_amount']:.2f} on {anomaly['date']}")
                print(f"    Expected: ${anomaly['rolling_mean_7d']:.2f} (Deviation: {((anomaly['computed_amount']/anomaly['rolling_mean_7d'])-1)*100:.1f}%)")
    
    # Forecast summary
    if forecasts:
        print(f"\n📈 30-DAY COST FORECASTS:")
        for service, forecast in list(forecasts.items())[:3]:
            monthly_forecast = sum(forecast['predictions'])
            confidence = forecast['model_score']
            print(f"  {service}: ${monthly_forecast:,.2f} (Confidence: {confidence:.2f})")
    
    # Immediate recommendations
    if recommendations['immediate_actions']:
        print(f"\n⚡ IMMEDIATE ACTIONS REQUIRED:")
        for action in recommendations['immediate_actions'][:3]:
            print(f"  🔥 {action['priority']}: {action['issue']}")
            print(f"     Potential Savings: ${action['potential_savings']:.2f}")
    
    # Efficiency insights
    if efficiency_analysis['underutilized_resources']:
        print(f"\n💡 TOP OPTIMIZATION OPPORTUNITIES:")
        for resource in efficiency_analysis['underutilized_resources'][:3]:
            print(f"  {resource['service']} - {resource['resource_id'][:20]}...")
            print(f"    Cost: ${resource['total_cost']:.2f}, Efficiency Score: {resource['efficiency_score']:.3f}")
    
    return {
        'cost_data': cost_data,
        'anomalies': anomalies,
        'forecasts': forecasts,
        'efficiency_analysis': efficiency_analysis,
        'recommendations': recommendations
    }

from oci.resource_manager import ResourceManagerClient
from oci.identity import IdentityClient
from oci.budget import BudgetClient
import json

class OCIFinOpsGovernance:
    def __init__(self, config_file="~/.oci/config"):
        """
        Initialize automated governance framework for cost control
        """
        self.config = oci.config.from_file(config_file)
        self.budget_client = BudgetClient(self.config)
        self.identity_client = IdentityClient(self.config)
        self.resource_manager_client = ResourceManagerClient(self.config)
    
    def create_intelligent_budgets(self, compartment_id, forecasted_costs):
        """
        Create adaptive budgets based on ML forecasts
        """
        budgets_created = []
        
        for service, forecast_data in forecasted_costs.items():
            monthly_forecast = sum(forecast_data['predictions'])
            
            # Calculate adaptive budget with confidence intervals
            upper_bound = sum(forecast_data['upper_bound'])
            recommended_budget = upper_bound * 1.1  # 10% buffer above upper bound
            
            # Create budget
            budget_details = oci.budget.models.CreateBudgetDetails(
                compartment_id=compartment_id,
                display_name=f"AI-Driven Budget - {service}",
                description=f"Intelligent budget based on ML forecast for {service}",
                amount=recommended_budget,
                reset_period="MONTHLY",
                budget_processing_period_start_offset=1,
                processing_period_type="INVOICE",
                targets=[compartment_id],
                target_type="COMPARTMENT"
            )
            
            try:
                budget_response = self.budget_client.create_budget(budget_details)
                
                # Create alert rules
                alert_rules = [
                    {
                        'threshold': 70,
                        'threshold_type': 'PERCENTAGE',
                        'type': 'ACTUAL',
                        'message': f'AI Alert: {service} spending at 70% of forecasted budget'
                    },
                    {
                        'threshold': 90,
                        'threshold_type': 'PERCENTAGE', 
                        'type': 'ACTUAL',
                        'message': f'Critical: {service} spending at 90% of forecasted budget'
                    },
                    {
                        'threshold': 100,
                        'threshold_type': 'PERCENTAGE',
                        'type': 'FORECAST',
                        'message': f'Forecast Alert: {service} projected to exceed budget'
                    }
                ]
                
                self._create_budget_alerts(budget_response.data.id, alert_rules)
                
                budgets_created.append({
                    'service': service,
                    'budget_id': budget_response.data.id,
                    'amount': recommended_budget,
                    'forecast_accuracy': forecast_data['model_score']
                })
                
            except Exception as e:
                print(f"Failed to create budget for {service}: {e}")
        
        return budgets_created
    
    def _create_budget_alerts(self, budget_id, alert_rules):
        """
        Create comprehensive alert rules for budget monitoring
        """
        for rule in alert_rules:
            alert_rule_details = oci.budget.models.CreateAlertRuleDetails(
                budget_id=budget_id,
                type=rule['type'],
                threshold=rule['threshold'],
                threshold_type=rule['threshold_type'],
                display_name=f"AI Alert - {rule['threshold']}% {rule['type']}",
                message=rule['message'],
                description=f"Automated alert generated by AI-driven FinOps system"
            )
            
            try:
                self.budget_client.create_alert_rule(alert_rule_details)
            except Exception as e:
                print(f"Failed to create alert rule: {e}")
    
    def implement_cost_policies(self, compartment_id, efficiency_analysis):
        """
        Implement automated cost control policies based on efficiency analysis
        """
        policies = []
        
        # Policy for underutilized resources
        if efficiency_analysis['underutilized_resources']:
            underutilized_policy = {
                'name': 'Underutilized Resource Management',
                'rules': [
                    'Require approval for instances with efficiency score < 0.1',
                    'Automatic shutdown of unused resources after 7 days',
                    'Mandatory rightsizing assessment for resources with efficiency < 0.2'
                ],
                'enforcement': 'AUTOMATIC'
            }
            policies.append(underutilized_policy)
        
        # Policy for cost anomalies
        anomaly_policy = {
            'name': 'Cost Anomaly Response',
            'rules': [
                'Automatic notification for cost increases > 50%',
                'Require justification for anomalous spending',
                'Emergency budget freeze for critical anomalies'
            ],
            'enforcement': 'SEMI_AUTOMATIC'
        }
        policies.append(anomaly_policy)
        
        # Policy for resource optimization
        optimization_policy = {
            'name': 'Continuous Cost Optimization',
            'rules': [
                'Weekly efficiency assessment for all resources',
                'Automatic reserved capacity recommendations',
                'Mandatory cost-benefit analysis for new deployments'
            ],
            'enforcement': 'ADVISORY'
        }
        policies.append(optimization_policy)
        
        return policies
    
    def setup_automated_actions(self, compartment_id, recommendations):
        """
        Configure automated actions based on AI recommendations
        """
        automated_actions = []
        
        for opportunity in recommendations.get('automation_opportunities', []):
            if opportunity['automation_type'] == 'AUTO_SCALING':
                action = {
                    'resource_id': opportunity['resource_id'],
                    'action_type': 'CONFIGURE_AUTOSCALING',
                    'parameters': {
                        'min_instances': 1,
                        'max_instances': 10,
                        'target_utilization': 70,
                        'scale_down_enabled': True
                    },
                    'estimated_savings': opportunity['estimated_savings'],
                    'status': 'PENDING_APPROVAL'
                }
                automated_actions.append(action)
        
        return automated_actions

from oci.monitoring import MonitoringClient
from oci.logging import LoggingManagementClient
import asyncio
from datetime import datetime, timedelta

class OCIFinOpsObservability:
    def __init__(self, config_file="~/.oci/config"):
        """
        Initialize advanced observability for cost correlation
        """
        self.config = oci.config.from_file(config_file)
        self.monitoring_client = MonitoringClient(self.config)
        self.logging_client = LoggingManagementClient(self.config)
    
    def create_cost_performance_correlation(self, compartment_id, resource_ids):
        """
        Correlate cost metrics with performance metrics for efficiency analysis
        """
        end_time = datetime.utcnow()
        start_time = end_time - timedelta(days=7)
        
        correlations = {}
        
        for resource_id in resource_ids:
            try:
                # Get cost metrics
                cost_query = oci.monitoring.models.SummarizeMetricsDataDetails(
                    namespace="oci_billing",
                    query=f'costs[1d].sum() where resourceId = "{resource_id}"',
                    compartment_id=compartment_id,
                    start_time=start_time,
                    end_time=end_time
                )
                
                cost_response = self.monitoring_client.summarize_metrics_data(cost_query)
                
                # Get performance metrics (CPU, Memory, Network)
                performance_queries = {
                    'cpu': f'CpuUtilization[1d].mean() where resourceId = "{resource_id}"',
                    'memory': f'MemoryUtilization[1d].mean() where resourceId = "{resource_id}"',
                    'network': f'NetworksBytesIn[1d].sum() where resourceId = "{resource_id}"'
                }
                
                performance_data = {}
                for metric_name, query in performance_queries.items():
                    perf_query = oci.monitoring.models.SummarizeMetricsDataDetails(
                        namespace="oci_computeagent",
                        query=query,
                        compartment_id=compartment_id,
                        start_time=start_time,
                        end_time=end_time
                    )
                    
                    try:
                        perf_response = self.monitoring_client.summarize_metrics_data(perf_query)
                        performance_data[metric_name] = perf_response.data
                    except Exception:
                        performance_data[metric_name] = None
                
                # Calculate efficiency metrics
                if cost_response.data and performance_data['cpu']:
                    cost_per_cpu_hour = self._calculate_cost_efficiency(
                        cost_response.data, performance_data['cpu']
                    )
                    
                    correlations[resource_id] = {
                        'cost_data': cost_response.data,
                        'performance_data': performance_data,
                        'efficiency_metrics': {
                            'cost_per_cpu_hour': cost_per_cpu_hour,
                            'utilization_trend': self._analyze_utilization_trend(performance_data['cpu']),
                            'efficiency_score': self._calculate_efficiency_score(cost_response.data, performance_data)
                        }
                    }
                
            except Exception as e:
                print(f"Error analyzing resource {resource_id}: {e}")
        
        return correlations
    
    def _calculate_cost_efficiency(self, cost_data, cpu_data):
        """
        Calculate cost efficiency based on actual utilization
        """
        if not cost_data or not cpu_data:
            return 0
        
        total_cost = sum([point.value for series in cost_data for point in series.aggregated_datapoints])
        avg_cpu = sum([point.value for series in cpu_data for point in series.aggregated_datapoints]) / len([point.value for series in cpu_data for point in series.aggregated_datapoints])
        
        # Cost per utilized CPU hour
        if avg_cpu > 0:
            return total_cost / (avg_cpu / 100)
        return float('inf')
    
    def _analyze_utilization_trend(self, cpu_data):
        """
        Analyze utilization trends to identify optimization opportunities
        """
        if not cpu_data:
            return "UNKNOWN"
        
        values = [point.value for series in cpu_data for point in series.aggregated_datapoints]
        
        if not values:
            return "NO_DATA"
        
        avg_utilization = sum(values) / len(values)
        
        if avg_utilization < 20:
            return "UNDERUTILIZED"
        elif avg_utilization > 80:
            return "OVERUTILIZED"
        else:
            return "OPTIMAL"
    
    def _calculate_efficiency_score(self, cost_data, performance_data):
        """
        Calculate overall efficiency score (0-100)
        """
        try:
            # Simple efficiency calculation based on cost vs utilization
            total_cost = sum([point.value for series in cost_data for point in series.aggregated_datapoints])
            
            cpu_values = [point.value for series in performance_data.get('cpu', []) for point in series.aggregated_datapoints] if performance_data.get('cpu') else [0]
            avg_cpu = sum(cpu_values) / len(cpu_values) if cpu_values else 0
            
            # Efficiency score: higher utilization with reasonable cost = higher score
            if total_cost > 0 and avg_cpu > 0:
                efficiency = (avg_cpu / 100) * (100 / (total_cost + 1))  # Normalize cost impact
                return min(100, efficiency * 100)
            
            return 0
        except Exception:
            return 0

async def implement_comprehensive_finops(tenancy_id, compartment_id):
    """
    Complete implementation of advanced FinOps on OCI
    """
    print("🚀 Initializing Advanced OCI FinOps Implementation")
    print("="*60)
    
    # Initialize all components
    finops_analytics = OCIFinOpsAnalytics()
    finops_governance = OCIFinOpsGovernance()
    finops_observability = OCIFinOpsObservability()
    
    # Step 1: Comprehensive cost analysis
    print("\n📊 Step 1: Advanced Cost Analysis")
    dashboard_data = create_advanced_cost_dashboard(finops_analytics, tenancy_id)
    
    if not dashboard_data:
        print("❌ Unable to proceed without cost data")
        return
    
    # Step 2: Implement governance
    print("\n🛡️  Step 2: Implementing Automated Governance")
    budgets = finops_governance.create_intelligent_budgets(
        compartment_id, dashboard_data['forecasts']
    )
    print(f"✅ Created {len(budgets)} intelligent budgets")
    
    policies = finops_governance.implement_cost_policies(
        compartment_id, dashboard_data['efficiency_analysis']
    )
    print(f"✅ Implemented {len(policies)} cost control policies")
    
    # Step 3: Setup observability
    print("\n👁️  Step 3: Advanced Observability Setup")
    services_to_monitor = ['compute', 'database', 'storage', 'networking']
    monitoring_configs = finops_observability.setup_intelligent_monitoring(
        compartment_id, services_to_monitor
    )
    print(f"✅ Configured monitoring for {len(services_to_monitor)} services")
    
    # Step 4: Generate final recommendations
    print("\n🎯 Step 4: Strategic Recommendations")
    print("="*40)
    
    recommendations = dashboard_data['recommendations']
    
    print("💰 IMMEDIATE COST SAVINGS OPPORTUNITIES:")
    total_immediate_savings = 0
    for action in recommendations['immediate_actions']:
        print(f"  • {action['issue']}")
        print(f"    Potential Savings: ${action['potential_savings']:.2f}")
        total_immediate_savings += action['potential_savings']
    
    print(f"\n💡 STRATEGIC INITIATIVES:")
    total_strategic_savings = 0
    for initiative in recommendations['strategic_initiatives']:
        print(f"  • {initiative['service']}: ${initiative['potential_savings']:.2f} monthly savings")
        total_strategic_savings += initiative['potential_savings']
    
    print(f"\n🤖 AUTOMATION OPPORTUNITIES:")
    total_automation_savings = 0
    for automation in recommendations['automation_opportunities']:
        print(f"  • {automation['automation_type']} for {automation['service']}")
        print(f"    Estimated Annual Savings: ${automation['estimated_savings'] * 12:.2f}")
        total_automation_savings += automation['estimated_savings'] * 12
    
    print("\n" + "="*60)
    print("FINOPS IMPLEMENTATION SUMMARY")
    print("="*60)
    print(f"💰 Immediate Savings Potential: ${total_immediate_savings:,.2f}")
    print(f"📈 Strategic Savings (Monthly): ${total_strategic_savings:,.2f}")
    print(f"🤖 Automation Savings (Annual): ${total_automation_savings:,.2f}")
    print(f"🎯 Total Annual Impact: ${(total_immediate_savings + total_strategic_savings * 12 + total_automation_savings):,.2f}")
    
    return {
        'analytics_data': dashboard_data,
        'governance': {'budgets': budgets, 'policies': policies},
        'observability': monitoring_configs,
        'recommendations': recommendations,
        'total_savings_potential': total_immediate_savings + total_strategic_savings * 12 + total_automation_savings
    }