class WebAnalyticsEngine:
    def __init__(self):
        self.daily_unique_visitors = {}
        self.page_view_analytics = {}
        self.conversion_funnels = {}
    
    def track_daily_visitors(self, date, visitor_data):
        """Track daily unique visitors"""
        
        if date not in self.daily_unique_visitors:
            self.daily_unique_visitors[date] = {
                'total_visitors': HyperLogLog(12),
                'mobile_visitors': HyperLogLog(12),
                'desktop_visitors': HyperLogLog(12),
                'new_visitors': HyperLogLog(12),
                'returning_visitors': HyperLogLog(12)
            }
        
        hll = self.daily_unique_visitors[date]
        
        # Total visitors
        hll['total_visitors'].add(visitor_data['user_id'])
        
        # Device-based classification
        if visitor_data['device_type'] == 'mobile':
            hll['mobile_visitors'].add(visitor_data['user_id'])
        else:
            hll['desktop_visitors'].add(visitor_data['user_id'])
        
        # New/returning visitor classification
        if visitor_data['is_new_visitor']:
            hll['new_visitors'].add(visitor_data['user_id'])
        else:
            hll['returning_visitors'].add(visitor_data['user_id'])
        
        return {
            'date': date,
            'total_unique_visitors': hll['total_visitors'].count(),
            'mobile_unique_visitors': hll['mobile_visitors'].count(),
            'desktop_unique_visitors': hll['desktop_visitors'].count(),
            'new_unique_visitors': hll['new_visitors'].count(),
            'returning_unique_visitors': hll['returning_visitors'].count()
        }
    
    def analyze_user_journey(self, user_events):
        """User journey analysis"""
        
        journey_analysis = {
            'funnel_steps': {
                'landing': HyperLogLog(10),
                'product_view': HyperLogLog(10),
                'add_to_cart': HyperLogLog(10),
                'checkout': HyperLogLog(10),
                'purchase': HyperLogLog(10)
            },
            'conversion_rates': {},
            'drop_off_analysis': {}
        }
        
        # Collect unique users for each step
        for event in user_events:
            user_id = event['user_id']
            event_type = event['event_type']
            
            if event_type in journey_analysis['funnel_steps']:
                journey_analysis['funnel_steps'][event_type].add(user_id)
        
        # Calculate conversion rates
        steps = ['landing', 'product_view', 'add_to_cart', 'checkout', 'purchase']
        previous_count = None
        
        for step in steps:
            current_count = journey_analysis['funnel_steps'][step].count()
            
            if previous_count is not None:
                conversion_rate = (current_count / previous_count) * 100
                journey_analysis['conversion_rates'][f'{previous_step}_to_{step}'] = {
                    'rate': conversion_rate,
                    'users': current_count,
                    'previous_users': previous_count
                }
            
            previous_count = current_count
            previous_step = step
        
        return journey_analysis

class RealTimeDashboard:
    def __init__(self):
        self.metrics_store = {}
        self.update_interval = 60  # Update every 60 seconds
    
    def generate_realtime_metrics(self):
        """Generate real-time metrics"""
        
        current_time = datetime.now()
        metrics = {
            'timestamp': current_time,
            'unique_visitors_1h': self._calculate_unique_visitors('1h'),
            'unique_visitors_24h': self._calculate_unique_visitors('24h'),
            'page_views_1h': self._calculate_page_views('1h'),
            'conversion_rate_24h': self._calculate_conversion_rate('24h'),
            'top_pages': self._get_top_pages('1h'),
            'device_breakdown': self._get_device_breakdown('1h')
        }
        
        return metrics
    
    def _calculate_unique_visitors(self, time_window):
        """Calculate unique visitors by time window"""
        
        if time_window == '1h':
            # Last 1 hour data
            cutoff_time = datetime.now() - timedelta(hours=1)
            hll = HyperLogLog(12)
            
            # Collect visitor data within 1 hour
            for timestamp, visitor_data in self._get_visitor_data_since(cutoff_time):
                hll.add(visitor_data['user_id'])
            
            return hll.count()
        
        elif time_window == '24h':
            # Last 24 hours data
            cutoff_time = datetime.now() - timedelta(hours=24)
            hll = HyperLogLog(14)  # Higher precision
            
            for timestamp, visitor_data in self._get_visitor_data_since(cutoff_time):
                hll.add(visitor_data['user_id'])
            
            return hll.count()

class MarketingCampaignAnalyzer:
    def __init__(self):
        self.campaign_metrics = {}
        self.attribution_models = {}
    
    def track_campaign_performance(self, campaign_data):
        """Track campaign performance"""
        
        campaign_id = campaign_data['campaign_id']
        
        if campaign_id not in self.campaign_metrics:
            self.campaign_metrics[campaign_id] = {
                'impressions': HyperLogLog(10),
                'clicks': HyperLogLog(10),
                'conversions': HyperLogLog(10),
                'revenue': 0,
                'cost': 0
            }
        
        metrics = self.campaign_metrics[campaign_id]
        
        # Impression count (deduplicated)
        if 'impression' in campaign_data['event_type']:
            metrics['impressions'].add(campaign_data['user_id'])
        
        # Click count
        if 'click' in campaign_data['event_type']:
            metrics['clicks'].add(campaign_data['user_id'])
        
        # Conversion count
        if 'conversion' in campaign_data['event_type']:
            metrics['conversions'].add(campaign_data['user_id'])
            metrics['revenue'] += campaign_data.get('revenue', 0)
        
        # Calculate performance metrics
        performance = self._calculate_campaign_performance(campaign_id)
        return performance
    
    def _calculate_campaign_performance(self, campaign_id):
        """Calculate campaign performance metrics"""
        
        metrics = self.campaign_metrics[campaign_id]
        
        unique_impressions = metrics['impressions'].count()
        unique_clicks = metrics['clicks'].count()
        unique_conversions = metrics['conversions'].count()
        
        performance = {
            'campaign_id': campaign_id,
            'unique_impressions': unique_impressions,
            'unique_clicks': unique_clicks,
            'unique_conversions': unique_conversions,
            'ctr': (unique_clicks / unique_impressions * 100) if unique_impressions > 0 else 0,
            'conversion_rate': (unique_conversions / unique_clicks * 100) if unique_clicks > 0 else 0,
            'cpc': metrics['cost'] / unique_clicks if unique_clicks > 0 else 0,
            'cpa': metrics['cost'] / unique_conversions if unique_conversions > 0 else 0,
            'roi': (metrics['revenue'] - metrics['cost']) / metrics['cost'] * 100 if metrics['cost'] > 0 else 0
        }
        
        return performance

class AdaptivePrecisionHLL:
    def __init__(self, initial_precision=10):
        self.precision = initial_precision
        self.hll = HyperLogLog(initial_precision)
        self.cardinality_threshold = 2 ** (initial_precision + 2)
        self.max_precision = 16
    
    def add(self, value):
        """Automatic precision adjustment when adding values"""
        
        current_count = self.hll.count()
        
        # Increase precision if cardinality exceeds threshold
        if current_count > self.cardinality_threshold and self.precision < self.max_precision:
            self._increase_precision()
        
        self.hll.add(value)
    
    def _increase_precision(self):
        """Increase precision and migrate data"""
        
        old_precision = self.precision
        new_precision = min(self.precision + 2, self.max_precision)
        
        # Create new HLL
        new_hll = HyperLogLog(new_precision)
        
        # Migrate existing register values to new HLL
        for register_value in self.hll.registers:
            if register_value > 0:
                # Convert register value to new precision
                new_hll.registers.append(register_value)
        
        self.hll = new_hll
        self.precision = new_precision
        self.cardinality_threshold = 2 ** (new_precision + 2)
        
        print(f"Precision increased from {old_precision} to {new_precision}")

class CompressedHLLStorage:
    def __init__(self):
        self.compression_algorithms = {
            'gzip': gzip,
            'lz4': lz4,
            'zstd': zstd
        }
    
    def compress_hll_data(self, hll_data, algorithm='zstd'):
        """Compress HLL data"""
        
        # Serialize HLL register data
        serialized_data = pickle.dumps(hll_data)
        
        # Apply compression
        if algorithm == 'gzip':
            compressed_data = gzip.compress(serialized_data)
        elif algorithm == 'lz4':
            compressed_data = lz4.compress(serialized_data)
        elif algorithm == 'zstd':
            compressed_data = zstd.compress(serialized_data)
        
        compression_ratio = len(compressed_data) / len(serialized_data)
        
        return {
            'compressed_data': compressed_data,
            'algorithm': algorithm,
            'compression_ratio': compression_ratio,
            'original_size': len(serialized_data),
            'compressed_size': len(compressed_data)
        }
    
    def decompress_hll_data(self, compressed_info):
        """Decompress HLL data"""
        
        compressed_data = compressed_info['compressed_data']
        algorithm = compressed_info['algorithm']
        
        # Decompress
        if algorithm == 'gzip':
            serialized_data = gzip.decompress(compressed_data)
        elif algorithm == 'lz4':
            serialized_data = lz4.decompress(compressed_data)
        elif algorithm == 'zstd':
            serialized_data = zstd.decompress(compressed_data)
        
        # Restore HLL object
        hll_data = pickle.loads(serialized_data)
        return hll_data

class DistributedHLLProcessor:
    def __init__(self, num_workers=4):
        self.num_workers = num_workers
        self.worker_pools = {}
    
    def process_large_dataset(self, data_stream, chunk_size=10000):
        """Parallel processing of large datasets"""
        
        # Split data into chunks
        chunks = self._split_data_into_chunks(data_stream, chunk_size)
        
        # Parallel processing
        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
            futures = []
            
            for i, chunk in enumerate(chunks):
                future = executor.submit(self._process_chunk, chunk, i)
                futures.append(future)
            
            # Collect results
            chunk_results = []
            for future in as_completed(futures):
                result = future.result()
                chunk_results.append(result)
        
        # Merge HLL results
        final_hll = self._merge_hll_results(chunk_results)
        return final_hll
    
    def _process_chunk(self, chunk, chunk_id):
        """Process individual chunk"""
        
        hll = HyperLogLog(12)
        
        for item in chunk:
            hll.add(item['user_id'])
        
        return {
            'chunk_id': chunk_id,
            'hll': hll,
            'processed_count': len(chunk)
        }
    
    def _merge_hll_results(self, chunk_results):
        """Merge HLL results"""
        
        if not chunk_results:
            return HyperLogLog(12)
        
        # Use first HLL as base for merging
        merged_hll = chunk_results[0]['hll']
        
        for result in chunk_results[1:]:
            merged_hll = self._union_hll(merged_hll, result['hll'])
        
        return merged_hll
    
    def _union_hll(self, hll1, hll2):
        """Calculate union of two HLLs"""
        
        if hll1.precision != hll2.precision:
            raise ValueError("HLL precision mismatch")
        
        union_hll = HyperLogLog(hll1.precision)
        
        # Select maximum value for each register
        for i in range(len(hll1.registers)):
            union_hll.registers[i] = max(hll1.registers[i], hll2.registers[i])
        
        return union_hll

class HLLAccuracyMonitor:
    def __init__(self):
        self.accuracy_metrics = {}
        self.baseline_counts = {}
    
    def track_accuracy(self, hll_result, exact_count, context):
        """Track HLL accuracy"""
        
        if exact_count == 0:
            return
        
        error_rate = abs(hll_result - exact_count) / exact_count
        relative_error = (hll_result - exact_count) / exact_count
        
        accuracy_record = {
            'timestamp': datetime.now(),
            'hll_count': hll_result,
            'exact_count': exact_count,
            'error_rate': error_rate,
            'relative_error': relative_error,
            'context': context
        }
        
        # Track accuracy by context
        if context not in self.accuracy_metrics:
            self.accuracy_metrics[context] = []
        
        self.accuracy_metrics[context].append(accuracy_record)
        
        # Check alert conditions
        self._check_accuracy_alerts(accuracy_record, context)
        
        return accuracy_record
    
    def _check_accuracy_alerts(self, record, context):
        """Check accuracy alert conditions"""
        
        error_rate = record['error_rate']
        
        # Alert if error rate exceeds threshold
        if error_rate > 0.05:  # 5% error
            alert = {
                'level': 'WARNING',
                'message': f"HLL accuracy degraded in {context}",
                'error_rate': error_rate,
                'timestamp': record['timestamp']
            }
            self._send_alert(alert)
        
        elif error_rate > 0.1:  # 10% error
            alert = {
                'level': 'CRITICAL',
                'message': f"HLL accuracy critically low in {context}",
                'error_rate': error_rate,
                'timestamp': record['timestamp']
            }
            self._send_alert(alert)
    
    def generate_accuracy_report(self, time_window='24h'):
        """Generate accuracy report"""
        
        cutoff_time = datetime.now() - timedelta(hours=24) if time_window == '24h' else datetime.now() - timedelta(days=7)
        
        report = {
            'time_window': time_window,
            'overall_accuracy': {},
            'context_breakdown': {},
            'recommendations': []
        }
        
        for context, records in self.accuracy_metrics.items():
            # Filter records within time window
            recent_records = [r for r in records if r['timestamp'] > cutoff_time]
            
            if not recent_records:
                continue
            
            # Calculate average error rate
            avg_error_rate = sum(r['error_rate'] for r in recent_records) / len(recent_records)
            max_error_rate = max(r['error_rate'] for r in recent_records)
            
            report['context_breakdown'][context] = {
                'avg_error_rate': avg_error_rate,
                'max_error_rate': max_error_rate,
                'sample_count': len(recent_records),
                'accuracy_score': max(0, 100 - (avg_error_rate * 100))
            }
        
        # Calculate overall accuracy
        all_records = []
        for records in self.accuracy_metrics.values():
            all_records.extend([r for r in records if r['timestamp'] > cutoff_time])
        
        if all_records:
            overall_avg_error = sum(r['error_rate'] for r in all_records) / len(all_records)
            report['overall_accuracy'] = {
                'avg_error_rate': overall_avg_error,
                'accuracy_score': max(0, 100 - (overall_avg_error * 100)),
                'total_samples': len(all_records)
            }
        
        # Generate recommendations
        report['recommendations'] = self._generate_recommendations(report)
        
        return report
    
    def _generate_recommendations(self, report):
        """Generate accuracy improvement recommendations"""
        
        recommendations = []
        
        for context, metrics in report['context_breakdown'].items():
            if metrics['avg_error_rate'] > 0.05:
                recommendations.append({
                    'context': context,
                    'issue': 'High error rate detected',
                    'recommendation': f'Consider increasing HLL precision for {context}',
                    'priority': 'HIGH' if metrics['avg_error_rate'] > 0.1 else 'MEDIUM'
                })
        
        return recommendations

class HLLPerformanceMonitor:
    def __init__(self):
        self.performance_metrics = {
            'throughput': [],
            'latency': [],
            'memory_usage': [],
            'cpu_usage': []
        }
    
    def track_operation_performance(self, operation_type, start_time, end_time, memory_usage, cpu_usage):
        """Track HLL operation performance"""
        
        duration = (end_time - start_time).total_seconds()
        
        performance_record = {
            'timestamp': datetime.now(),
            'operation_type': operation_type,
            'duration': duration,
            'memory_usage': memory_usage,
            'cpu_usage': cpu_usage
        }
        
        self.performance_metrics['latency'].append(performance_record)
        
        # Calculate throughput (items processed per second)
        if duration > 0:
            throughput = 1 / duration  # Single operation basis
            self.performance_metrics['throughput'].append({
                'timestamp': performance_record['timestamp'],
                'operation_type': operation_type,
                'throughput': throughput
            })
    
    def analyze_performance_trends(self, time_window='1h'):
        """Analyze performance trends"""
        
        cutoff_time = datetime.now() - timedelta(hours=1) if time_window == '1h' else datetime.now() - timedelta(hours=24)
        
        # Analyze latency
        recent_latency = [r for r in self.performance_metrics['latency'] if r['timestamp'] > cutoff_time]
        
        if not recent_latency:
            return None
        
        latency_by_operation = {}
        for record in recent_latency:
            op_type = record['operation_type']
            if op_type not in latency_by_operation:
                latency_by_operation[op_type] = []
            latency_by_operation[op_type].append(record['duration'])
        
        # Calculate statistics
        performance_analysis = {
            'time_window': time_window,
            'operation_breakdown': {},
            'overall_metrics': {
                'avg_latency': sum(r['duration'] for r in recent_latency) / len(recent_latency),
                'max_latency': max(r['duration'] for r in recent_latency),
                'min_latency': min(r['duration'] for r in recent_latency)
            }
        }
        
        for op_type, latencies in latency_by_operation.items():
            performance_analysis['operation_breakdown'][op_type] = {
                'avg_latency': sum(latencies) / len(latencies),
                'max_latency': max(latencies),
                'min_latency': min(latencies),
                'operation_count': len(latencies)
            }
        
        return performance_analysis

class HLLMicroservice:
    def __init__(self, service_name, redis_client, kafka_producer):
        self.service_name = service_name
        self.redis_client = redis_client
        self.kafka_producer = kafka_producer
        self.hll_cache = {}
        self.metrics_collector = HLLPerformanceMonitor()
    
    def process_cardinality_request(self, request):
        """Process cardinality calculation request"""
        
        start_time = datetime.now()
        
        try:
            # Parse request
            dataset_id = request['dataset_id']
            time_range = request['time_range']
            filters = request.get('filters', {})
            
            # Get or create HLL
            hll_key = self._generate_hll_key(dataset_id, time_range, filters)
            hll = self._get_or_create_hll(hll_key, dataset_id, time_range, filters)
            
            # Calculate cardinality
            cardinality = hll.count()
            
            # Return result
            result = {
                'dataset_id': dataset_id,
                'time_range': time_range,
                'filters': filters,
                'cardinality': cardinality,
                'confidence_interval': self._calculate_confidence_interval(cardinality, hll.precision),
                'timestamp': datetime.now()
            }
            
            # Record performance metrics
            end_time = datetime.now()
            self.metrics_collector.track_operation_performance(
                'cardinality_calculation', start_time, end_time, 
                self._get_memory_usage(), self._get_cpu_usage()
            )
            
            return result
            
        except Exception as e:
            # Error logging and alerting
            self._handle_error(e, request)
            raise
    
    def _get_or_create_hll(self, hll_key, dataset_id, time_range, filters):
        """Get or create HLL"""
        
        # Check cache first
        if hll_key in self.hll_cache:
            return self.hll_cache[hll_key]
        
        # Check Redis
        cached_hll = self._load_hll_from_redis(hll_key)
        if cached_hll:
            self.hll_cache[hll_key] = cached_hll
            return cached_hll
        
        # Create new HLL
        hll = self._create_new_hll(dataset_id, time_range, filters)
        
        # Store in cache and Redis
        self.hll_cache[hll_key] = hll
        self._save_hll_to_redis(hll_key, hll)
        
        return hll
    
    def _create_new_hll(self, dataset_id, time_range, filters):
        """Create new HLL"""
        
        # Fetch data from data source
        raw_data = self._fetch_data_from_source(dataset_id, time_range, filters)
        
        # Create HLL and add data
        hll = HyperLogLog(12)  # Set appropriate precision
        
        for record in raw_data:
            hll.add(record['user_id'])
        
        return hll
    
    def _calculate_confidence_interval(self, cardinality, precision):
        """Calculate confidence interval"""
        
        # Calculate HyperLogLog standard error
        standard_error = 1.04 / math.sqrt(2 ** precision)
        margin_of_error = cardinality * standard_error
        
        return {
            'lower_bound': max(0, cardinality - margin_of_error),
            'upper_bound': cardinality + margin_of_error,
            'margin_of_error': margin_of_error,
            'confidence_level': 0.95
        }

class DistributedHLLCache:
    def __init__(self, redis_cluster, cache_ttl=3600):
        self.redis_cluster = redis_cluster
        self.cache_ttl = cache_ttl
        self.local_cache = {}
        self.cache_stats = {
            'hits': 0,
            'misses': 0,
            'evictions': 0
        }
    
    def get_hll(self, cache_key):
        """Get HLL from cache"""
        
        # Check local cache first
        if cache_key in self.local_cache:
            self.cache_stats['hits'] += 1
            return self.local_cache[cache_key]
        
        # Check Redis
        try:
            redis_key = f"hll:{cache_key}"
            cached_data = self.redis_cluster.get(redis_key)
            
            if cached_data:
                hll = pickle.loads(cached_data)
                # Store in local cache
                self.local_cache[cache_key] = hll
                self.cache_stats['hits'] += 1
                return hll
            else:
                self.cache_stats['misses'] += 1
                return None
                
        except Exception as e:
            print(f"Redis cache error: {e}")
            self.cache_stats['misses'] += 1
            return None
    
    def set_hll(self, cache_key, hll):
        """Store HLL in cache"""
        
        try:
            # Store in local cache
            self.local_cache[cache_key] = hll
            
            # Store in Redis
            redis_key = f"hll:{cache_key}"
            serialized_hll = pickle.dumps(hll)
            self.redis_cluster.setex(redis_key, self.cache_ttl, serialized_hll)
            
        except Exception as e:
            print(f"Cache storage error: {e}")
    
    def evict_cache(self, cache_key):
        """Evict cache"""
        
        # Remove from local cache
        if cache_key in self.local_cache:
            del self.local_cache[cache_key]
        
        # Remove from Redis
        try:
            redis_key = f"hll:{cache_key}"
            self.redis_cluster.delete(redis_key)
            self.cache_stats['evictions'] += 1
        except Exception as e:
            print(f"Cache eviction error: {e}")
    
    def get_cache_stats(self):
        """Return cache statistics"""
        
        total_requests = self.cache_stats['hits'] + self.cache_stats['misses']
        hit_rate = (self.cache_stats['hits'] / total_requests * 100) if total_requests > 0 else 0
        
        return {
            'hit_rate': hit_rate,
            'total_requests': total_requests,
            'local_cache_size': len(self.local_cache),
            'stats': self.cache_stats
        }

class RealTimeAnalyticsPlatform:
    def __init__(self):
        self.data_pipeline = DataPipeline()
        self.hll_processor = DistributedHLLProcessor()
        self.cache_system = DistributedHLLCache()
        self.monitoring_system = HLLAccuracyMonitor()
        self.alert_system = AlertSystem()
    
    def setup_analytics_pipeline(self):
        """Setup analytics pipeline"""
        
        pipeline_config = {
            'data_sources': {
                'user_events': {
                    'source': 'Kafka',
                    'topics': ['user-page-views', 'user-clicks', 'user-purchases'],
                    'processing_mode': 'streaming'
                },
                'user_profiles': {
                    'source': 'Database',
                    'table': 'user_profiles',
                    'processing_mode': 'batch'
                }
            },
            'hll_processing': {
                'precision_levels': {
                    'hourly': 10,
                    'daily': 12,
                    'weekly': 14,
                    'monthly': 16
                },
                'aggregation_windows': ['1h', '24h', '7d', '30d'],
                'cache_strategy': 'distributed'
            },
            'output_targets': {
                'real_time_dashboard': {
                    'update_interval': '1m',
                    'metrics': ['unique_visitors', 'page_views', 'conversion_rate']
                },
                'data_warehouse': {
                    'storage_format': 'Parquet',
                    'partition_strategy': 'daily'
                }
            }
        }
        
        return pipeline_config
    
    def implement_real_time_processing(self):
        """Implement real-time processing"""
        
        processing_engine = {
            'stream_processing': {
                'framework': 'Apache Flink',
                'configuration': {
                    'parallelism': 4,
                    'checkpoint_interval': '60s',
                    'state_backend': 'RocksDB'
                },
                'operators': {
                    'event_parser': 'JSONParser',
                    'user_id_extractor': 'UserIDExtractor',
                    'hll_aggregator': 'HLLAggregator',
                    'result_sink': 'KafkaSink'
                }
            },
            'batch_processing': {
                'framework': 'Apache Spark',
                'configuration': {
                    'executor_instances': 8,
                    'executor_memory': '4g',
                    'driver_memory': '2g'
                },
                'jobs': {
                    'daily_aggregation': 'DailyHLLAggregation',
                    'weekly_rollup': 'WeeklyRollup',
                    'monthly_archive': 'MonthlyArchive'
                }
            }
        }
        
        return processing_engine
    
    def setup_monitoring_and_alerts(self):
        """Setup monitoring and alerts"""
        
        monitoring_config = {
            'accuracy_monitoring': {
                'check_interval': '5m',
                'error_threshold': 0.05,
                'critical_threshold': 0.1,
                'baseline_comparison': True
            },
            'performance_monitoring': {
                'latency_threshold': '100ms',
                'throughput_threshold': 1000,
                'memory_threshold': '80%',
                'cpu_threshold': '85%'
            },
            'alert_channels': {
                'email': ['admin@company.com'],
                'slack': ['#data-alerts'],
                'pagerduty': ['data-team']
            }
        }
        
        return monitoring_config
    
    def generate_business_insights(self):
        """Generate business insights"""
        
        insights_engine = {
            'user_behavior_analysis': {
                'retention_cohorts': 'CohortAnalysis',
                'user_segmentation': 'UserSegmentation',
                'conversion_funnels': 'ConversionFunnelAnalysis'
            },
            'marketing_effectiveness': {
                'campaign_attribution': 'CampaignAttribution',
                'channel_performance': 'ChannelPerformance',
                'roi_analysis': 'ROIAnalysis'
            },
            'product_analytics': {
                'feature_adoption': 'FeatureAdoption',
                'user_engagement': 'EngagementMetrics',
                'churn_prediction': 'ChurnPrediction'
            }
        }
        
        return insights_engine

class AutomatedOperationsSystem:
    def __init__(self):
        self.scheduler = APScheduler()
        self.health_checker = HealthChecker()
        self.auto_scaler = AutoScaler()
        self.backup_manager = BackupManager()
    
    def setup_automated_operations(self):
        """Setup automated operations"""
        
        operations = {
            'scheduled_tasks': {
                'daily_hll_refresh': {
                    'schedule': '0 2 * * *',  # Every day at 2 AM
                    'task': 'refresh_daily_hll_metrics',
                    'retry_count': 3
                },
                'weekly_accuracy_check': {
                    'schedule': '0 3 * * 1',  # Every Monday at 3 AM
                    'task': 'validate_hll_accuracy',
                    'alert_on_failure': True
                },
                'monthly_archive': {
                    'schedule': '0 4 1 * *',  # First day of month at 4 AM
                    'task': 'archive_old_hll_data',
                    'retention_days': 365
                }
            },
            'health_checks': {
                'hll_service_health': {
                    'check_interval': '30s',
                    'endpoints': ['/health', '/metrics'],
                    'failure_threshold': 3
                },
                'cache_health': {
                    'check_interval': '1m',
                    'redis_cluster_check': True,
                    'cache_hit_rate_threshold': 0.8
                }
            },
            'auto_scaling': {
                'hll_processing_nodes': {
                    'min_instances': 2,
                    'max_instances': 10,
                    'scale_up_threshold': 'cpu > 70%',
                    'scale_down_threshold': 'cpu < 30%'
                }
            }
        }
        
        return operations
    
    def implement_disaster_recovery(self):
        """Implement disaster recovery"""
        
        dr_config = {
            'backup_strategy': {
                'hll_data_backup': {
                    'frequency': 'hourly',
                    'retention': '7 days',
                    'storage': 'S3'
                },
                'configuration_backup': {
                    'frequency': 'daily',
                    'retention': '30 days',
                    'storage': 'Git repository'
                }
            },
            'failover_procedures': {
                'primary_failure': {
                    'detection_time': '30s',
                    'failover_time': '2m',
                    'secondary_region': 'us-west-2'
                },
                'data_corruption': {
                    'detection_method': 'checksum_validation',
                    'recovery_time': '5m',
                    'backup_source': 'latest_known_good'
                }
            },
            'recovery_testing': {
                'frequency': 'monthly',
                'test_scenarios': [
                    'primary_region_failure',
                    'database_corruption',
                    'cache_failure'
                ],
                'success_criteria': {
                    'rto': '5 minutes',  # Recovery Time Objective
                    'rpo': '1 minute'    # Recovery Point Objective
                }
            }
        }
        
        return dr_config