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MongoDB Data Pipeline Optimization and Stream Processing: Advanced Real-Time Analytics for High-Performance Data Workflows

Modern applications require sophisticated data processing capabilities that can handle high-velocity data streams, complex analytical workloads, and real-time insights while maintaining optimal performance under varying load conditions. Traditional data pipeline approaches often struggle with complex transformation logic, performance bottlenecks in aggregation operations, and the operational complexity of maintaining separate systems for batch and stream processing, leading to increased latency, resource inefficiency, and difficulty in maintaining data consistency across processing workflows.

MongoDB provides comprehensive data pipeline capabilities through the Aggregation Framework, Change Streams, and advanced stream processing features that enable real-time analytics, complex data transformations, and high-performance data processing within a single unified platform. Unlike traditional approaches that require multiple specialized systems and complex integration logic, MongoDB's integrated data pipeline capabilities deliver superior performance through native optimization, intelligent query planning, and seamless integration with storage and indexing systems.

The Traditional Data Pipeline Challenge

Conventional data processing architectures face significant limitations when handling complex analytical workloads:

-- Traditional PostgreSQL data pipeline - complex ETL processes with performance limitations

-- Basic data transformation pipeline with limited optimization capabilities
CREATE TABLE raw_events (
    event_id BIGSERIAL PRIMARY KEY,
    event_timestamp TIMESTAMP NOT NULL,
    user_id BIGINT NOT NULL,
    session_id VARCHAR(100),
    event_type VARCHAR(100) NOT NULL,
    event_category VARCHAR(100),

    -- Basic event data (limited nested structure support)
    event_data JSONB,
    device_info JSONB,
    location_data JSONB,

    -- Processing metadata
    ingested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    processed_at TIMESTAMP,
    processing_status VARCHAR(50) DEFAULT 'pending',

    -- Partitioning key
    partition_date DATE GENERATED ALWAYS AS (event_timestamp::date) STORED

) PARTITION BY RANGE (partition_date);

-- Create monthly partitions (manual maintenance required)
CREATE TABLE raw_events_2024_11 PARTITION OF raw_events
    FOR VALUES FROM ('2024-11-01') TO ('2024-12-01');
CREATE TABLE raw_events_2024_12 PARTITION OF raw_events  
    FOR VALUES FROM ('2024-12-01') TO ('2025-01-01');

-- Complex data transformation pipeline with performance issues
CREATE OR REPLACE FUNCTION process_event_batch(
    batch_size INTEGER DEFAULT 1000
) RETURNS TABLE (
    processed_events INTEGER,
    failed_events INTEGER,
    processing_time_ms INTEGER,
    transformation_errors TEXT[]
) AS $$
DECLARE
    batch_start_time TIMESTAMP;
    processing_errors TEXT[] := '{}';
    events_processed INTEGER := 0;
    events_failed INTEGER := 0;
    event_record RECORD;
BEGIN
    batch_start_time := clock_timestamp();

    -- Process events in batches (inefficient row-by-row processing)
    FOR event_record IN 
        SELECT * FROM raw_events 
        WHERE processing_status = 'pending'
        ORDER BY ingested_at
        LIMIT batch_size
    LOOP
        BEGIN
            -- Complex transformation logic (limited JSON processing capabilities)
            WITH transformed_event AS (
                SELECT 
                    event_record.event_id,
                    event_record.event_timestamp,
                    event_record.user_id,
                    event_record.session_id,
                    event_record.event_type,
                    event_record.event_category,

                    -- Basic data extraction and transformation
                    COALESCE(event_record.event_data->>'revenue', '0')::DECIMAL(10,2) as revenue,
                    COALESCE(event_record.event_data->>'quantity', '1')::INTEGER as quantity,
                    event_record.event_data->>'product_id' as product_id,
                    event_record.event_data->>'product_name' as product_name,

                    -- Device information extraction (limited nested processing)
                    event_record.device_info->>'device_type' as device_type,
                    event_record.device_info->>'browser' as browser,
                    event_record.device_info->>'os' as operating_system,

                    -- Location processing (basic only)
                    event_record.location_data->>'country' as country,
                    event_record.location_data->>'region' as region,
                    event_record.location_data->>'city' as city,

                    -- Time-based calculations
                    EXTRACT(HOUR FROM event_record.event_timestamp) as event_hour,
                    EXTRACT(DOW FROM event_record.event_timestamp) as day_of_week,
                    TO_CHAR(event_record.event_timestamp, 'YYYY-MM') as year_month,

                    -- User segmentation (basic logic only)
                    CASE 
                        WHEN user_segments.segment_type IS NOT NULL THEN user_segments.segment_type
                        ELSE 'unknown'
                    END as user_segment,

                    -- Processing metadata
                    CURRENT_TIMESTAMP as processed_at

                FROM raw_events re
                LEFT JOIN user_segments ON re.user_id = user_segments.user_id
                WHERE re.event_id = event_record.event_id
            )

            -- Insert into processed events table (separate table required)
            INSERT INTO processed_events (
                event_id, event_timestamp, user_id, session_id, event_type, event_category,
                revenue, quantity, product_id, product_name,
                device_type, browser, operating_system,
                country, region, city,
                event_hour, day_of_week, year_month, user_segment,
                processed_at
            )
            SELECT * FROM transformed_event;

            -- Update processing status
            UPDATE raw_events 
            SET 
                processed_at = CURRENT_TIMESTAMP,
                processing_status = 'completed'
            WHERE event_id = event_record.event_id;

            events_processed := events_processed + 1;

        EXCEPTION WHEN OTHERS THEN
            events_failed := events_failed + 1;
            processing_errors := array_append(processing_errors, 
                'Event ID ' || event_record.event_id || ': ' || SQLERRM);

            -- Mark event as failed
            UPDATE raw_events 
            SET 
                processed_at = CURRENT_TIMESTAMP,
                processing_status = 'failed'
            WHERE event_id = event_record.event_id;
        END;
    END LOOP;

    -- Return processing results
    RETURN QUERY SELECT 
        events_processed,
        events_failed,
        EXTRACT(MILLISECONDS FROM clock_timestamp() - batch_start_time)::INTEGER,
        processing_errors;

END;
$$ LANGUAGE plpgsql;

-- Execute batch processing (requires manual scheduling)
SELECT * FROM process_event_batch(1000);

-- Complex analytical query with performance limitations
WITH hourly_metrics AS (
    -- Time-based aggregation with limited optimization
    SELECT 
        DATE_TRUNC('hour', event_timestamp) as hour_bucket,
        event_type,
        event_category,
        user_segment,
        device_type,
        country,

        -- Basic aggregations (limited analytical functions)
        COUNT(*) as event_count,
        COUNT(DISTINCT user_id) as unique_users,
        COUNT(DISTINCT session_id) as unique_sessions,
        SUM(revenue) as total_revenue,
        AVG(revenue) FILTER (WHERE revenue > 0) as avg_revenue_per_transaction,

        -- Limited statistical functions
        PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY revenue) as median_revenue,
        STDDEV_POP(revenue) as revenue_stddev,

        -- Time-based calculations
        MIN(event_timestamp) as first_event_time,
        MAX(event_timestamp) as last_event_time

    FROM processed_events
    WHERE event_timestamp >= CURRENT_TIMESTAMP - INTERVAL '24 hours'
    GROUP BY 
        DATE_TRUNC('hour', event_timestamp),
        event_type, event_category, user_segment, device_type, country
),

user_behavior_analysis AS (
    -- User journey analysis (complex and slow)
    SELECT 
        user_id,
        session_id,

        -- Session-level aggregations
        COUNT(*) as events_per_session,
        SUM(revenue) as session_revenue,
        EXTRACT(SECONDS FROM (MAX(event_timestamp) - MIN(event_timestamp))) as session_duration_seconds,

        -- Event sequence analysis (limited capabilities)
        string_agg(event_type, ' -> ' ORDER BY event_timestamp) as event_sequence,
        array_agg(event_timestamp ORDER BY event_timestamp) as event_timestamps,

        -- Conversion analysis
        CASE 
            WHEN 'purchase' = ANY(array_agg(event_type)) THEN 'converted'
            WHEN 'add_to_cart' = ANY(array_agg(event_type)) THEN 'engaged'
            ELSE 'browsing'
        END as conversion_status,

        -- Time-based metrics
        first_value(event_timestamp) OVER (
            PARTITION BY user_id 
            ORDER BY event_timestamp 
            ROWS UNBOUNDED PRECEDING
        ) as first_session_event,

        last_value(event_timestamp) OVER (
            PARTITION BY user_id 
            ORDER BY event_timestamp 
            ROWS UNBOUNDED FOLLOWING
        ) as last_session_event

    FROM processed_events
    WHERE event_timestamp >= CURRENT_TIMESTAMP - INTERVAL '7 days'
    GROUP BY user_id, session_id
),

funnel_analysis AS (
    -- Conversion funnel analysis (very limited and slow)
    SELECT 
        event_category,
        user_segment,

        -- Funnel step counts
        COUNT(*) FILTER (WHERE event_type = 'view') as step_1_views,
        COUNT(*) FILTER (WHERE event_type = 'click') as step_2_clicks,
        COUNT(*) FILTER (WHERE event_type = 'add_to_cart') as step_3_cart_adds,
        COUNT(*) FILTER (WHERE event_type = 'purchase') as step_4_purchases,

        -- Conversion rates (basic calculations)
        CASE 
            WHEN COUNT(*) FILTER (WHERE event_type = 'view') > 0 THEN
                (COUNT(*) FILTER (WHERE event_type = 'click') * 100.0) / 
                COUNT(*) FILTER (WHERE event_type = 'view')
            ELSE 0
        END as click_through_rate,

        CASE 
            WHEN COUNT(*) FILTER (WHERE event_type = 'add_to_cart') > 0 THEN
                (COUNT(*) FILTER (WHERE event_type = 'purchase') * 100.0) / 
                COUNT(*) FILTER (WHERE event_type = 'add_to_cart')
            ELSE 0
        END as cart_to_purchase_rate

    FROM processed_events
    WHERE event_timestamp >= CURRENT_TIMESTAMP - INTERVAL '30 days'
    GROUP BY event_category, user_segment
)

-- Final analytical output (limited insights)
SELECT 
    hm.hour_bucket,
    hm.event_type,
    hm.event_category,
    hm.user_segment,
    hm.device_type,
    hm.country,

    -- Volume metrics
    hm.event_count,
    hm.unique_users,
    hm.unique_sessions,
    ROUND(hm.event_count::DECIMAL / hm.unique_users, 2) as events_per_user,

    -- Revenue metrics  
    ROUND(hm.total_revenue, 2) as total_revenue,
    ROUND(hm.avg_revenue_per_transaction, 2) as avg_revenue_per_transaction,
    ROUND(hm.median_revenue, 2) as median_revenue,

    -- User behavior insights (very limited)
    (SELECT AVG(events_per_session) 
     FROM user_behavior_analysis uba 
     WHERE uba.session_revenue > 0) as avg_events_per_converting_session,

    -- Conversion insights
    fa.click_through_rate,
    fa.cart_to_purchase_rate,

    -- Performance indicators
    EXTRACT(MINUTES FROM (hm.last_event_time - hm.first_event_time)) as processing_window_minutes,

    -- Trend indicators (very basic)
    LAG(hm.event_count, 1) OVER (
        PARTITION BY hm.event_type, hm.user_segment 
        ORDER BY hm.hour_bucket
    ) as prev_hour_event_count

FROM hourly_metrics hm
LEFT JOIN funnel_analysis fa ON (
    hm.event_category = fa.event_category AND 
    hm.user_segment = fa.user_segment
)
WHERE hm.event_count > 10  -- Filter low-volume segments
ORDER BY hm.hour_bucket DESC, hm.total_revenue DESC
LIMIT 1000;

-- Problems with traditional data pipeline approaches:
-- 1. Complex ETL processes requiring separate batch processing jobs
-- 2. Limited support for nested and complex data structures  
-- 3. Poor performance with large-scale analytical workloads
-- 4. Manual partitioning and maintenance overhead
-- 5. No real-time stream processing capabilities
-- 6. Limited statistical and analytical functions
-- 7. Complex joins and data movement between processing stages
-- 8. No native support for time-series and event stream processing
-- 9. Difficulty in maintaining data consistency across pipeline stages
-- 10. Limited optimization for analytical query patterns

MongoDB provides comprehensive data pipeline capabilities with advanced stream processing and analytics:

// MongoDB Advanced Data Pipeline and Stream Processing - real-time analytics with optimized performance
const { MongoClient, GridFSBucket } = require('mongodb');
const { EventEmitter } = require('events');

// Comprehensive MongoDB Data Pipeline Manager
class AdvancedDataPipelineManager extends EventEmitter {
  constructor(connectionString, pipelineConfig = {}) {
    super();
    this.connectionString = connectionString;
    this.client = null;
    this.db = null;

    // Advanced pipeline configuration
    this.config = {
      // Pipeline processing configuration
      enableStreamProcessing: pipelineConfig.enableStreamProcessing !== false,
      enableRealTimeAnalytics: pipelineConfig.enableRealTimeAnalytics !== false,
      enableBatchProcessing: pipelineConfig.enableBatchProcessing !== false,

      // Performance optimization settings
      aggregationOptimization: pipelineConfig.aggregationOptimization !== false,
      indexOptimization: pipelineConfig.indexOptimization !== false,
      memoryOptimization: pipelineConfig.memoryOptimization !== false,
      parallelProcessing: pipelineConfig.parallelProcessing !== false,

      // Stream processing configuration
      changeStreamOptions: {
        fullDocument: 'updateLookup',
        maxAwaitTimeMS: 1000,
        batchSize: 1000,
        ...pipelineConfig.changeStreamOptions
      },

      // Batch processing configuration
      batchSize: pipelineConfig.batchSize || 5000,
      maxBatchProcessingTime: pipelineConfig.maxBatchProcessingTime || 300000, // 5 minutes

      // Analytics configuration
      analyticsWindowSize: pipelineConfig.analyticsWindowSize || 3600000, // 1 hour
      retentionPeriod: pipelineConfig.retentionPeriod || 90 * 24 * 60 * 60 * 1000, // 90 days

      // Performance monitoring
      enablePerformanceMetrics: pipelineConfig.enablePerformanceMetrics !== false,
      enablePipelineOptimization: pipelineConfig.enablePipelineOptimization !== false
    };

    // Pipeline state management
    this.activePipelines = new Map();
    this.streamProcessors = new Map();
    this.batchProcessors = new Map();
    this.performanceMetrics = {
      pipelinesExecuted: 0,
      totalProcessingTime: 0,
      documentsProcessed: 0,
      averageThroughput: 0
    };

    this.initializeDataPipeline();
  }

  async initializeDataPipeline() {
    console.log('Initializing advanced data pipeline system...');

    try {
      // Connect to MongoDB
      this.client = new MongoClient(this.connectionString);
      await this.client.connect();
      this.db = this.client.db();

      // Setup collections and indexes
      await this.setupPipelineInfrastructure();

      // Initialize stream processing
      if (this.config.enableStreamProcessing) {
        await this.initializeStreamProcessing();
      }

      // Initialize batch processing
      if (this.config.enableBatchProcessing) {
        await this.initializeBatchProcessing();
      }

      // Setup real-time analytics
      if (this.config.enableRealTimeAnalytics) {
        await this.setupRealTimeAnalytics();
      }

      console.log('Advanced data pipeline system initialized successfully');

    } catch (error) {
      console.error('Error initializing data pipeline:', error);
      throw error;
    }
  }

  async setupPipelineInfrastructure() {
    console.log('Setting up data pipeline infrastructure...');

    try {
      // Create collections with optimized configuration
      const collections = {
        rawEvents: this.db.collection('raw_events'),
        processedEvents: this.db.collection('processed_events'),
        analyticsResults: this.db.collection('analytics_results'),
        pipelineMetrics: this.db.collection('pipeline_metrics'),
        userSessions: this.db.collection('user_sessions'),
        conversionFunnels: this.db.collection('conversion_funnels')
      };

      // Create optimized indexes for high-performance data processing
      await collections.rawEvents.createIndexes([
        { key: { eventTimestamp: -1 }, background: true },
        { key: { userId: 1, sessionId: 1, eventTimestamp: -1 }, background: true },
        { key: { eventType: 1, eventCategory: 1, eventTimestamp: -1 }, background: true },
        { key: { 'processingStatus': 1, 'ingestedAt': 1 }, background: true },
        { key: { 'locationData.country': 1, 'deviceInfo.deviceType': 1 }, background: true, sparse: true }
      ]);

      await collections.processedEvents.createIndexes([
        { key: { eventTimestamp: -1 }, background: true },
        { key: { userId: 1, sessionId: 1, eventTimestamp: -1 }, background: true },
        { key: { eventType: 1, userSegment: 1, eventTimestamp: -1 }, background: true },
        { key: { 'metrics.revenue': -1, eventTimestamp: -1 }, background: true, sparse: true }
      ]);

      await collections.analyticsResults.createIndexes([
        { key: { analysisType: 1, timeWindow: -1 }, background: true },
        { key: { 'dimensions.eventType': 1, 'dimensions.userSegment': 1, timeWindow: -1 }, background: true },
        { key: { createdAt: -1 }, background: true }
      ]);

      this.collections = collections;

      console.log('Pipeline infrastructure setup completed');

    } catch (error) {
      console.error('Error setting up pipeline infrastructure:', error);
      throw error;
    }
  }

  async createAdvancedAnalyticsPipeline(pipelineConfig) {
    console.log('Creating advanced analytics pipeline...');

    const pipelineId = this.generatePipelineId();
    const startTime = Date.now();

    try {
      // Build comprehensive aggregation pipeline
      const analyticsStages = [
        // Stage 1: Data filtering and initial processing
        {
          $match: {
            eventTimestamp: {
              $gte: new Date(Date.now() - this.config.analyticsWindowSize),
              $lte: new Date()
            },
            processingStatus: 'completed',
            ...pipelineConfig.matchCriteria
          }
        },

        // Stage 2: Advanced data transformation and enrichment
        {
          $addFields: {
            // Time-based dimensions
            hourBucket: {
              $dateFromParts: {
                year: { $year: '$eventTimestamp' },
                month: { $month: '$eventTimestamp' },
                day: { $dayOfMonth: '$eventTimestamp' },
                hour: { $hour: '$eventTimestamp' }
              }
            },
            dayOfWeek: { $dayOfWeek: '$eventTimestamp' },
            yearMonth: {
              $dateToString: {
                format: '%Y-%m',
                date: '$eventTimestamp'
              }
            },

            // User segmentation and classification
            userSegment: {
              $switch: {
                branches: [
                  {
                    case: { $gte: ['$userMetrics.totalRevenue', 1000] },
                    then: 'high_value'
                  },
                  {
                    case: { $gte: ['$userMetrics.totalRevenue', 100] },
                    then: 'medium_value'
                  },
                  {
                    case: { $gt: ['$userMetrics.totalRevenue', 0] },
                    then: 'low_value'
                  }
                ],
                default: 'non_revenue'
              }
            },

            // Device and technology classification
            deviceCategory: {
              $switch: {
                branches: [
                  {
                    case: { $in: ['$deviceInfo.deviceType', ['smartphone', 'tablet']] },
                    then: 'mobile'
                  },
                  {
                    case: { $eq: ['$deviceInfo.deviceType', 'desktop'] },
                    then: 'desktop'
                  }
                ],
                default: 'other'
              }
            },

            // Geographic clustering
            geoRegion: {
              $switch: {
                branches: [
                  {
                    case: { $in: ['$locationData.country', ['US', 'CA', 'MX']] },
                    then: 'North America'
                  },
                  {
                    case: { $in: ['$locationData.country', ['GB', 'DE', 'FR', 'IT', 'ES']] },
                    then: 'Europe'
                  },
                  {
                    case: { $in: ['$locationData.country', ['JP', 'KR', 'CN', 'IN']] },
                    then: 'Asia'
                  }
                ],
                default: 'Other'
              }
            },

            // Revenue and value metrics
            revenueMetrics: {
              revenue: { $toDouble: '$eventData.revenue' },
              quantity: { $toInt: '$eventData.quantity' },
              averageOrderValue: {
                $cond: [
                  { $gt: [{ $toInt: '$eventData.quantity' }, 0] },
                  { $divide: [{ $toDouble: '$eventData.revenue' }, { $toInt: '$eventData.quantity' }] },
                  0
                ]
              }
            }
          }
        },

        // Stage 3: Multi-dimensional aggregation and analytics
        {
          $group: {
            _id: {
              hourBucket: '$hourBucket',
              eventType: '$eventType',
              eventCategory: '$eventCategory',
              userSegment: '$userSegment',
              deviceCategory: '$deviceCategory',
              geoRegion: '$geoRegion'
            },

            // Volume metrics
            eventCount: { $sum: 1 },
            uniqueUsers: { $addToSet: '$userId' },
            uniqueSessions: { $addToSet: '$sessionId' },

            // Revenue analytics
            totalRevenue: { $sum: '$revenueMetrics.revenue' },
            totalQuantity: { $sum: '$revenueMetrics.quantity' },
            revenueTransactions: {
              $sum: {
                $cond: [{ $gt: ['$revenueMetrics.revenue', 0] }, 1, 0]
              }
            },

            // Statistical aggregations
            revenueValues: { $push: '$revenueMetrics.revenue' },
            quantityValues: { $push: '$revenueMetrics.quantity' },
            avgOrderValues: { $push: '$revenueMetrics.averageOrderValue' },

            // Time-based analytics
            firstEventTime: { $min: '$eventTimestamp' },
            lastEventTime: { $max: '$eventTimestamp' },
            eventTimestamps: { $push: '$eventTimestamp' },

            // User behavior patterns
            userSessions: {
              $push: {
                userId: '$userId',
                sessionId: '$sessionId',
                eventTimestamp: '$eventTimestamp',
                revenue: '$revenueMetrics.revenue'
              }
            }
          }
        },

        // Stage 4: Advanced statistical calculations
        {
          $addFields: {
            // User metrics
            uniqueUserCount: { $size: '$uniqueUsers' },
            uniqueSessionCount: { $size: '$uniqueSessions' },
            eventsPerUser: {
              $divide: ['$eventCount', { $size: '$uniqueUsers' }]
            },
            eventsPerSession: {
              $divide: ['$eventCount', { $size: '$uniqueSessions' }]
            },

            // Revenue analytics
            averageRevenue: {
              $cond: [
                { $gt: ['$revenueTransactions', 0] },
                { $divide: ['$totalRevenue', '$revenueTransactions'] },
                0
              ]
            },
            revenuePerUser: {
              $divide: ['$totalRevenue', { $size: '$uniqueUsers' }]
            },
            conversionRate: {
              $divide: ['$revenueTransactions', '$eventCount']
            },

            // Statistical measures
            revenueStats: {
              $let: {
                vars: {
                  sortedRevenues: {
                    $sortArray: {
                      input: '$revenueValues',
                      sortBy: 1
                    }
                  }
                },
                in: {
                  median: {
                    $arrayElemAt: [
                      '$$sortedRevenues',
                      { $floor: { $multiply: [{ $size: '$$sortedRevenues' }, 0.5] } }
                    ]
                  },
                  percentile75: {
                    $arrayElemAt: [
                      '$$sortedRevenues',
                      { $floor: { $multiply: [{ $size: '$$sortedRevenues' }, 0.75] } }
                    ]
                  },
                  percentile95: {
                    $arrayElemAt: [
                      '$$sortedRevenues',
                      { $floor: { $multiply: [{ $size: '$$sortedRevenues' }, 0.95] } }
                    ]
                  }
                }
              }
            },

            // Temporal analysis
            processingWindowMinutes: {
              $divide: [
                { $subtract: ['$lastEventTime', '$firstEventTime'] },
                60000 // Convert to minutes
              ]
            },

            // Session analysis
            sessionMetrics: {
              $reduce: {
                input: '$userSessions',
                initialValue: {
                  totalSessions: 0,
                  convertingSessions: 0,
                  totalSessionRevenue: 0
                },
                in: {
                  totalSessions: { $add: ['$$value.totalSessions', 1] },
                  convertingSessions: {
                    $cond: [
                      { $gt: ['$$this.revenue', 0] },
                      { $add: ['$$value.convertingSessions', 1] },
                      '$$value.convertingSessions'
                    ]
                  },
                  totalSessionRevenue: {
                    $add: ['$$value.totalSessionRevenue', '$$this.revenue']
                  }
                }
              }
            }
          }
        },

        // Stage 5: Performance optimization and data enrichment
        {
          $addFields: {
            // Performance indicators
            performanceMetrics: {
              throughputEventsPerMinute: {
                $divide: ['$eventCount', '$processingWindowMinutes']
              },
              revenueVelocity: {
                $divide: ['$totalRevenue', '$processingWindowMinutes']
              },
              userEngagementRate: {
                $divide: [{ $size: '$uniqueUsers' }, '$eventCount']
              }
            },

            // Business metrics
            businessMetrics: {
              customerLifetimeValue: {
                $multiply: [
                  '$revenuePerUser',
                  { $literal: 12 } // Assuming 12-month projection
                ]
              },
              sessionConversionRate: {
                $divide: [
                  '$sessionMetrics.convertingSessions',
                  '$sessionMetrics.totalSessions'
                ]
              },
              averageSessionValue: {
                $divide: [
                  '$sessionMetrics.totalSessionRevenue',
                  '$sessionMetrics.totalSessions'
                ]
              }
            },

            // Data quality metrics
            dataQuality: {
              completenessScore: {
                $divide: [
                  { $add: [
                    { $cond: [{ $gt: [{ $size: '$uniqueUsers' }, 0] }, 1, 0] },
                    { $cond: [{ $gt: ['$eventCount', 0] }, 1, 0] },
                    { $cond: [{ $ne: ['$_id.eventType', null] }, 1, 0] },
                    { $cond: [{ $ne: ['$_id.eventCategory', null] }, 1, 0] }
                  ] },
                  4
                ]
              },
              consistencyScore: {
                $cond: [
                  { $eq: ['$eventsPerSession', { $divide: ['$eventCount', { $size: '$uniqueSessions' }] }] },
                  1.0,
                  0.8
                ]
              }
            }
          }
        },

        // Stage 6: Final result formatting and metadata
        {
          $project: {
            // Dimension information
            dimensions: '$_id',
            timeWindow: '$_id.hourBucket',
            analysisType: { $literal: pipelineConfig.analysisType || 'comprehensive_analytics' },

            // Core metrics
            metrics: {
              volume: {
                eventCount: '$eventCount',
                uniqueUserCount: '$uniqueUserCount',
                uniqueSessionCount: '$uniqueSessionCount',
                eventsPerUser: { $round: ['$eventsPerUser', 2] },
                eventsPerSession: { $round: ['$eventsPerSession', 2] }
              },

              revenue: {
                totalRevenue: { $round: ['$totalRevenue', 2] },
                totalQuantity: '$totalQuantity',
                revenueTransactions: '$revenueTransactions',
                averageRevenue: { $round: ['$averageRevenue', 2] },
                revenuePerUser: { $round: ['$revenuePerUser', 2] },
                conversionRate: { $round: ['$conversionRate', 4] }
              },

              statistical: {
                medianRevenue: { $round: ['$revenueStats.median', 2] },
                percentile75Revenue: { $round: ['$revenueStats.percentile75', 2] },
                percentile95Revenue: { $round: ['$revenueStats.percentile95', 2] }
              },

              performance: '$performanceMetrics',
              business: '$businessMetrics',
              dataQuality: '$dataQuality'
            },

            // Temporal information
            temporal: {
              firstEventTime: '$firstEventTime',
              lastEventTime: '$lastEventTime',
              processingWindowMinutes: { $round: ['$processingWindowMinutes', 1] }
            },

            // Pipeline metadata
            pipelineMetadata: {
              pipelineId: { $literal: pipelineId },
              executionTime: { $literal: new Date() },
              configurationUsed: { $literal: pipelineConfig }
            }
          }
        },

        // Stage 7: Results persistence and optimization
        {
          $merge: {
            into: 'analytics_results',
            whenMatched: 'replace',
            whenNotMatched: 'insert'
          }
        }
      ];

      // Execute the comprehensive analytics pipeline
      console.log('Executing comprehensive analytics pipeline...');
      const pipelineResult = await this.collections.processedEvents.aggregate(
        analyticsStages,
        {
          allowDiskUse: true,
          maxTimeMS: this.config.maxBatchProcessingTime,
          hint: { eventTimestamp: -1 }, // Optimize with time-based index
          comment: `Advanced analytics pipeline: ${pipelineId}`
        }
      ).toArray();

      const executionTime = Date.now() - startTime;

      // Update performance metrics
      this.updatePipelineMetrics(pipelineId, {
        executionTime: executionTime,
        documentsProcessed: pipelineResult.length,
        pipelineType: 'analytics',
        success: true
      });

      this.emit('pipelineCompleted', {
        pipelineId: pipelineId,
        pipelineType: 'analytics',
        executionTime: executionTime,
        documentsProcessed: pipelineResult.length,
        resultsGenerated: pipelineResult.length
      });

      console.log(`Analytics pipeline completed: ${pipelineId} (${executionTime}ms, ${pipelineResult.length} results)`);

      return {
        success: true,
        pipelineId: pipelineId,
        executionTime: executionTime,
        resultsGenerated: pipelineResult.length,
        analyticsData: pipelineResult
      };

    } catch (error) {
      console.error(`Analytics pipeline failed: ${pipelineId}`, error);

      this.updatePipelineMetrics(pipelineId, {
        executionTime: Date.now() - startTime,
        pipelineType: 'analytics',
        success: false,
        error: error.message
      });

      return {
        success: false,
        pipelineId: pipelineId,
        error: error.message
      };
    }
  }

  async initializeStreamProcessing() {
    console.log('Initializing real-time stream processing...');

    try {
      // Setup change streams for real-time processing
      const changeStream = this.collections.rawEvents.watch(
        [
          {
            $match: {
              'operationType': { $in: ['insert', 'update'] },
              'fullDocument.processingStatus': { $ne: 'processed' }
            }
          }
        ],
        this.config.changeStreamOptions
      );

      // Process streaming data in real-time
      changeStream.on('change', async (change) => {
        try {
          await this.processStreamingEvent(change);
        } catch (error) {
          console.error('Error processing streaming event:', error);
          this.emit('streamProcessingError', { change, error: error.message });
        }
      });

      changeStream.on('error', (error) => {
        console.error('Change stream error:', error);
        this.emit('changeStreamError', { error: error.message });
      });

      this.streamProcessors.set('main', changeStream);

      console.log('Stream processing initialized successfully');

    } catch (error) {
      console.error('Error initializing stream processing:', error);
      throw error;
    }
  }

  async processStreamingEvent(change) {
    console.log('Processing streaming event:', change.documentKey);

    const document = change.fullDocument;
    const processingStartTime = Date.now();

    try {
      // Real-time event transformation and enrichment
      const transformedEvent = await this.transformEventData(document);

      // Apply real-time analytics calculations
      const analyticsData = await this.calculateRealTimeMetrics(transformedEvent);

      // Update processed events collection
      await this.collections.processedEvents.replaceOne(
        { _id: transformedEvent._id },
        {
          ...transformedEvent,
          ...analyticsData,
          processedAt: new Date(),
          processingLatency: Date.now() - processingStartTime
        },
        { upsert: true }
      );

      // Update real-time analytics aggregations
      if (this.config.enableRealTimeAnalytics) {
        await this.updateRealTimeAnalytics(transformedEvent);
      }

      this.emit('eventProcessed', {
        eventId: document._id,
        processingLatency: Date.now() - processingStartTime,
        analyticsGenerated: Object.keys(analyticsData).length
      });

    } catch (error) {
      console.error('Error processing streaming event:', error);

      // Mark event as failed for retry processing
      await this.collections.rawEvents.updateOne(
        { _id: document._id },
        {
          $set: {
            processingStatus: 'failed',
            processingError: error.message,
            lastProcessingAttempt: new Date()
          }
        }
      );

      throw error;
    }
  }

  async transformEventData(rawEvent) {
    // Advanced event data transformation with MongoDB-specific optimizations
    const transformed = {
      _id: rawEvent._id,
      eventId: rawEvent.eventId || rawEvent._id,
      eventTimestamp: rawEvent.eventTimestamp,
      userId: rawEvent.userId,
      sessionId: rawEvent.sessionId,
      eventType: rawEvent.eventType,
      eventCategory: rawEvent.eventCategory,

      // Enhanced data extraction using MongoDB operators
      eventData: {
        ...rawEvent.eventData,
        revenue: parseFloat(rawEvent.eventData?.revenue || 0),
        quantity: parseInt(rawEvent.eventData?.quantity || 1),
        productId: rawEvent.eventData?.productId,
        productName: rawEvent.eventData?.productName
      },

      // Device and technology information
      deviceInfo: {
        deviceType: rawEvent.deviceInfo?.deviceType || 'unknown',
        browser: rawEvent.deviceInfo?.browser || 'unknown',
        operatingSystem: rawEvent.deviceInfo?.os || 'unknown',
        screenResolution: rawEvent.deviceInfo?.screenResolution,
        userAgent: rawEvent.deviceInfo?.userAgent
      },

      // Geographic information
      locationData: {
        country: rawEvent.locationData?.country || 'unknown',
        region: rawEvent.locationData?.region || 'unknown',
        city: rawEvent.locationData?.city || 'unknown',
        coordinates: rawEvent.locationData?.coordinates
      },

      // Time-based dimensions for efficient aggregation
      timeDimensions: {
        hour: rawEvent.eventTimestamp.getHours(),
        dayOfWeek: rawEvent.eventTimestamp.getDay(),
        yearMonth: `${rawEvent.eventTimestamp.getFullYear()}-${String(rawEvent.eventTimestamp.getMonth() + 1).padStart(2, '0')}`,
        quarterYear: `Q${Math.floor(rawEvent.eventTimestamp.getMonth() / 3) + 1}-${rawEvent.eventTimestamp.getFullYear()}`
      },

      // Processing metadata
      processingMetadata: {
        transformedAt: new Date(),
        version: '2.0',
        source: 'stream_processor'
      }
    };

    return transformed;
  }

  async calculateRealTimeMetrics(event) {
    // Real-time metrics calculation using MongoDB aggregation
    const metricsCalculation = [
      {
        $match: {
          userId: event.userId,
          eventTimestamp: {
            $gte: new Date(Date.now() - 24 * 60 * 60 * 1000) // Last 24 hours
          }
        }
      },
      {
        $group: {
          _id: null,
          totalEvents: { $sum: 1 },
          totalRevenue: { $sum: '$eventData.revenue' },
          uniqueSessions: { $addToSet: '$sessionId' },
          eventTypes: { $addToSet: '$eventType' },
          averageOrderValue: { $avg: '$eventData.revenue' }
        }
      }
    ];

    const userMetrics = await this.collections.processedEvents
      .aggregate(metricsCalculation)
      .toArray();

    return {
      userMetrics: userMetrics[0] || {
        totalEvents: 1,
        totalRevenue: event.eventData.revenue,
        uniqueSessions: [event.sessionId],
        eventTypes: [event.eventType],
        averageOrderValue: event.eventData.revenue
      }
    };
  }

  updatePipelineMetrics(pipelineId, metrics) {
    // Update system-wide pipeline performance metrics
    this.performanceMetrics.pipelinesExecuted++;
    this.performanceMetrics.totalProcessingTime += metrics.executionTime;
    this.performanceMetrics.documentsProcessed += metrics.documentsProcessed || 0;

    if (this.performanceMetrics.pipelinesExecuted > 0) {
      this.performanceMetrics.averageThroughput = 
        this.performanceMetrics.documentsProcessed / 
        (this.performanceMetrics.totalProcessingTime / 1000);
    }

    // Store detailed pipeline metrics
    this.collections.pipelineMetrics.insertOne({
      pipelineId: pipelineId,
      metrics: metrics,
      timestamp: new Date(),
      systemMetrics: {
        memoryUsage: process.memoryUsage(),
        systemPerformance: this.performanceMetrics
      }
    }).catch(error => {
      console.error('Error storing pipeline metrics:', error);
    });
  }

  generatePipelineId() {
    const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
    return `pipeline_${timestamp}_${Math.random().toString(36).substr(2, 9)}`;
  }

  async shutdown() {
    console.log('Shutting down data pipeline manager...');

    try {
      // Close all active stream processors
      for (const [processorId, stream] of this.streamProcessors.entries()) {
        await stream.close();
        console.log(`Closed stream processor: ${processorId}`);
      }

      // Close MongoDB connection
      if (this.client) {
        await this.client.close();
      }

      console.log('Data pipeline manager shutdown complete');

    } catch (error) {
      console.error('Error during shutdown:', error);
    }
  }
}

// Benefits of MongoDB Advanced Data Pipeline:
// - Real-time stream processing with Change Streams for immediate insights
// - Comprehensive aggregation framework for complex analytical workloads
// - Native support for nested and complex data structures without ETL overhead
// - Optimized indexing and query planning for high-performance analytics
// - Integrated batch and stream processing within a single platform
// - Advanced statistical and mathematical functions for sophisticated analytics
// - Automatic scaling and optimization for large-scale data processing
// - SQL-compatible pipeline management through QueryLeaf integration
// - Built-in performance monitoring and optimization capabilities
// - Production-ready stream processing with minimal configuration overhead

module.exports = {
  AdvancedDataPipelineManager
};

Understanding MongoDB Data Pipeline Architecture

Advanced Stream Processing and Real-Time Analytics Patterns

Implement sophisticated data pipeline workflows for production MongoDB deployments:

// Enterprise-grade MongoDB data pipeline with advanced stream processing and analytics optimization
class EnterpriseDataPipelineProcessor extends AdvancedDataPipelineManager {
  constructor(connectionString, enterpriseConfig) {
    super(connectionString, enterpriseConfig);

    this.enterpriseConfig = {
      ...enterpriseConfig,
      enableAdvancedAnalytics: true,
      enableMachineLearningPipelines: true,
      enablePredictiveAnalytics: true,
      enableDataGovernance: true,
      enableComplianceReporting: true
    };

    this.setupEnterpriseFeatures();
    this.initializePredictiveAnalytics();
    this.setupComplianceFramework();
  }

  async implementAdvancedDataPipeline() {
    console.log('Implementing enterprise data pipeline with advanced capabilities...');

    const pipelineStrategy = {
      // Multi-tier processing architecture
      processingTiers: {
        realTimeProcessing: {
          latencyTarget: 100, // milliseconds
          throughputTarget: 100000, // events per second
          consistencyLevel: 'eventual'
        },
        nearRealTimeProcessing: {
          latencyTarget: 5000, // 5 seconds
          throughputTarget: 50000,
          consistencyLevel: 'strong'
        },
        batchProcessing: {
          latencyTarget: 300000, // 5 minutes
          throughputTarget: 1000000,
          consistencyLevel: 'strong'
        }
      },

      // Advanced analytics capabilities
      analyticsCapabilities: {
        descriptiveAnalytics: true,
        diagnosticAnalytics: true,
        predictiveAnalytics: true,
        prescriptiveAnalytics: true
      },

      // Data governance and compliance
      dataGovernance: {
        dataLineageTracking: true,
        dataQualityMonitoring: true,
        privacyCompliance: true,
        auditTrailMaintenance: true
      }
    };

    return await this.deployEnterpriseStrategy(pipelineStrategy);
  }

  async setupPredictiveAnalytics() {
    console.log('Setting up predictive analytics capabilities...');

    const predictiveConfig = {
      // Machine learning models
      models: {
        churnPrediction: true,
        revenueForecasting: true,
        behaviorPrediction: true,
        anomalyDetection: true
      },

      // Feature engineering
      featureEngineering: {
        temporalFeatures: true,
        behavioralFeatures: true,
        demographicFeatures: true,
        interactionFeatures: true
      },

      // Model deployment
      modelDeployment: {
        realTimeScoring: true,
        batchScoring: true,
        modelVersioning: true,
        performanceMonitoring: true
      }
    };

    return await this.deployPredictiveAnalytics(predictiveConfig);
  }
}

SQL-Style Data Pipeline Operations with QueryLeaf

QueryLeaf provides familiar SQL syntax for MongoDB data pipeline operations and stream processing:

-- QueryLeaf advanced data pipeline operations with SQL-familiar syntax for MongoDB

-- Configure comprehensive data pipeline strategy
CONFIGURE DATA_PIPELINE 
SET pipeline_name = 'enterprise_analytics_pipeline',
    processing_modes = ['real_time', 'batch', 'stream'],

    -- Real-time processing configuration
    stream_processing_enabled = true,
    stream_latency_target_ms = 100,
    stream_throughput_target = 100000,
    change_stream_batch_size = 1000,

    -- Batch processing configuration
    batch_processing_enabled = true,
    batch_size = 10000,
    batch_processing_interval_minutes = 5,
    max_batch_processing_time_minutes = 30,

    -- Analytics configuration
    enable_real_time_analytics = true,
    analytics_window_size_hours = 24,
    enable_predictive_analytics = true,
    enable_statistical_functions = true,

    -- Performance optimization
    enable_aggregation_optimization = true,
    enable_index_optimization = true,
    enable_parallel_processing = true,
    max_memory_usage_gb = 8,

    -- Data governance
    enable_data_lineage_tracking = true,
    enable_data_quality_monitoring = true,
    enable_audit_trail = true,
    data_retention_days = 90;

-- Advanced multi-dimensional analytics pipeline with comprehensive transformations
WITH event_enrichment AS (
  SELECT 
    event_id,
    event_timestamp,
    user_id,
    session_id,
    event_type,
    event_category,

    -- Advanced data extraction and type conversion
    CAST(event_data->>'revenue' AS DECIMAL(10,2)) as revenue,
    CAST(event_data->>'quantity' AS INTEGER) as quantity,
    event_data->>'product_id' as product_id,
    event_data->>'product_name' as product_name,
    event_data->>'campaign_id' as campaign_id,

    -- Device and technology classification
    CASE 
      WHEN device_info->>'device_type' IN ('smartphone', 'tablet') THEN 'mobile'
      WHEN device_info->>'device_type' = 'desktop' THEN 'desktop'
      ELSE 'other'
    END as device_category,

    device_info->>'browser' as browser,
    device_info->>'operating_system' as operating_system,

    -- Geographic dimensions
    location_data->>'country' as country,
    location_data->>'region' as region,
    location_data->>'city' as city,

    -- Advanced geographic clustering
    CASE 
      WHEN location_data->>'country' IN ('US', 'CA', 'MX') THEN 'North America'
      WHEN location_data->>'country' IN ('GB', 'DE', 'FR', 'IT', 'ES', 'NL') THEN 'Europe'
      WHEN location_data->>'country' IN ('JP', 'KR', 'CN', 'IN', 'SG') THEN 'Asia Pacific'
      WHEN location_data->>'country' IN ('BR', 'AR', 'CL', 'CO') THEN 'Latin America'
      ELSE 'Other'
    END as geo_region,

    -- Time-based dimensions for efficient aggregation
    DATE_TRUNC('hour', event_timestamp) as hour_bucket,
    EXTRACT(HOUR FROM event_timestamp) as event_hour,
    EXTRACT(DOW FROM event_timestamp) as day_of_week,
    EXTRACT(WEEK FROM event_timestamp) as week_of_year,
    EXTRACT(MONTH FROM event_timestamp) as month_of_year,
    TO_CHAR(event_timestamp, 'YYYY-MM') as year_month,
    TO_CHAR(event_timestamp, 'YYYY-"Q"Q') as year_quarter,

    -- Advanced user segmentation
    CASE 
      WHEN user_metrics.total_revenue >= 1000 THEN 'high_value'
      WHEN user_metrics.total_revenue >= 100 THEN 'medium_value'  
      WHEN user_metrics.total_revenue > 0 THEN 'low_value'
      ELSE 'non_revenue'
    END as user_segment,

    -- Customer lifecycle classification
    CASE 
      WHEN user_metrics.days_since_first_event <= 30 THEN 'new'
      WHEN user_metrics.days_since_last_event <= 30 THEN 'active'
      WHEN user_metrics.days_since_last_event <= 90 THEN 'dormant'
      ELSE 'inactive'  
    END as customer_lifecycle_stage,

    -- Behavioral indicators
    user_metrics.total_events as user_total_events,
    user_metrics.total_revenue as user_total_revenue,
    user_metrics.avg_session_duration as user_avg_session_duration,
    user_metrics.days_since_first_event,
    user_metrics.days_since_last_event,

    -- Revenue and value calculations
    CASE 
      WHEN CAST(event_data->>'quantity' AS INTEGER) > 0 THEN
        CAST(event_data->>'revenue' AS DECIMAL(10,2)) / CAST(event_data->>'quantity' AS INTEGER)
      ELSE 0
    END as average_order_value,

    -- Processing metadata
    CURRENT_TIMESTAMP as processed_at,
    'advanced_pipeline_v2' as processing_version

  FROM raw_events re
  LEFT JOIN user_behavioral_metrics user_metrics ON re.user_id = user_metrics.user_id
  WHERE 
    re.event_timestamp >= CURRENT_TIMESTAMP - INTERVAL '24 hours'
    AND re.processing_status = 'pending'
),

comprehensive_aggregation AS (
  SELECT 
    hour_bucket,
    event_type,
    event_category,
    user_segment,
    customer_lifecycle_stage,
    device_category,
    geo_region,
    browser,
    operating_system,

    -- Volume metrics with advanced calculations
    COUNT(*) as event_count,
    COUNT(DISTINCT user_id) as unique_users,
    COUNT(DISTINCT session_id) as unique_sessions,
    COUNT(DISTINCT product_id) as unique_products,
    COUNT(DISTINCT campaign_id) as unique_campaigns,

    -- User engagement metrics
    ROUND(COUNT(*)::DECIMAL / COUNT(DISTINCT user_id), 2) as events_per_user,
    ROUND(COUNT(*)::DECIMAL / COUNT(DISTINCT session_id), 2) as events_per_session,
    ROUND(COUNT(DISTINCT session_id)::DECIMAL / COUNT(DISTINCT user_id), 2) as sessions_per_user,

    -- Revenue analytics with statistical functions
    SUM(revenue) as total_revenue,
    SUM(quantity) as total_quantity,
    COUNT(*) FILTER (WHERE revenue > 0) as revenue_transactions,

    -- Advanced revenue statistics
    AVG(revenue) as avg_revenue,
    AVG(revenue) FILTER (WHERE revenue > 0) as avg_revenue_per_transaction,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY revenue) as median_revenue,
    PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY revenue) as percentile_75_revenue,
    PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY revenue) as percentile_95_revenue,
    STDDEV_POP(revenue) as revenue_standard_deviation,

    -- Advanced order value analytics
    AVG(average_order_value) as avg_order_value,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY average_order_value) as median_order_value,
    MAX(average_order_value) as max_order_value,
    MIN(average_order_value) FILTER (WHERE average_order_value > 0) as min_order_value,

    -- Conversion and engagement metrics
    ROUND((COUNT(*) FILTER (WHERE revenue > 0)::DECIMAL / COUNT(*)) * 100, 2) as conversion_rate_percent,
    SUM(revenue) / NULLIF(COUNT(DISTINCT user_id), 0) as revenue_per_user,
    SUM(revenue) / NULLIF(COUNT(DISTINCT session_id), 0) as revenue_per_session,

    -- Time-based analytics
    MIN(event_timestamp) as window_start_time,
    MAX(event_timestamp) as window_end_time,
    EXTRACT(MINUTES FROM (MAX(event_timestamp) - MIN(event_timestamp))) as processing_window_minutes,

    -- User behavior pattern analysis
    AVG(user_total_events) as avg_user_lifetime_events,
    AVG(user_total_revenue) as avg_user_lifetime_revenue,
    AVG(user_avg_session_duration) as avg_session_duration_seconds,
    AVG(days_since_first_event) as avg_days_since_first_event,

    -- Product performance metrics
    MODE() WITHIN GROUP (ORDER BY product_id) as top_product_id,
    MODE() WITHIN GROUP (ORDER BY product_name) as top_product_name,
    COUNT(DISTINCT product_id) FILTER (WHERE revenue > 0) as converting_products,

    -- Campaign effectiveness
    MODE() WITHIN GROUP (ORDER BY campaign_id) as top_campaign_id,
    COUNT(DISTINCT campaign_id) FILTER (WHERE revenue > 0) as converting_campaigns,

    -- Seasonal and temporal patterns
    MODE() WITHIN GROUP (ORDER BY day_of_week) as most_active_day_of_week,
    MODE() WITHIN GROUP (ORDER BY event_hour) as most_active_hour,

    -- Data quality indicators
    COUNT(*) FILTER (WHERE revenue IS NOT NULL) / COUNT(*)::DECIMAL as revenue_data_completeness,
    COUNT(*) FILTER (WHERE product_id IS NOT NULL) / COUNT(*)::DECIMAL as product_data_completeness,
    COUNT(*) FILTER (WHERE geo_region != 'Other') / COUNT(*)::DECIMAL as location_data_completeness

  FROM event_enrichment
  GROUP BY 
    hour_bucket, event_type, event_category, user_segment, customer_lifecycle_stage,
    device_category, geo_region, browser, operating_system
),

performance_analysis AS (
  SELECT 
    ca.*,

    -- Performance indicators and rankings
    ROW_NUMBER() OVER (ORDER BY total_revenue DESC) as revenue_rank,
    ROW_NUMBER() OVER (ORDER BY unique_users DESC) as user_engagement_rank,
    ROW_NUMBER() OVER (ORDER BY conversion_rate_percent DESC) as conversion_rank,
    ROW_NUMBER() OVER (ORDER BY avg_order_value DESC) as aov_rank,

    -- Efficiency metrics
    ROUND(total_revenue / processing_window_minutes, 2) as revenue_velocity_per_minute,
    ROUND(event_count::DECIMAL / processing_window_minutes, 1) as event_velocity_per_minute,
    ROUND(unique_users::DECIMAL / processing_window_minutes, 1) as user_acquisition_rate_per_minute,

    -- Business health indicators
    CASE 
      WHEN conversion_rate_percent >= 5.0 THEN 'excellent'
      WHEN conversion_rate_percent >= 2.0 THEN 'good'
      WHEN conversion_rate_percent >= 1.0 THEN 'fair'
      ELSE 'poor'
    END as conversion_performance_rating,

    CASE 
      WHEN revenue_per_user >= 100 THEN 'high_value'
      WHEN revenue_per_user >= 25 THEN 'medium_value'
      WHEN revenue_per_user >= 5 THEN 'low_value'
      ELSE 'minimal_value'
    END as user_value_rating,

    CASE 
      WHEN events_per_user >= 10 THEN 'highly_engaged'
      WHEN events_per_user >= 5 THEN 'moderately_engaged'
      WHEN events_per_user >= 2 THEN 'lightly_engaged'
      ELSE 'minimally_engaged'
    END as user_engagement_rating,

    -- Trend and growth indicators
    LAG(total_revenue) OVER (
      PARTITION BY event_type, user_segment, device_category, geo_region 
      ORDER BY hour_bucket
    ) as prev_hour_revenue,

    LAG(unique_users) OVER (
      PARTITION BY event_type, user_segment, device_category, geo_region 
      ORDER BY hour_bucket
    ) as prev_hour_users,

    LAG(conversion_rate_percent) OVER (
      PARTITION BY event_type, user_segment, device_category, geo_region 
      ORDER BY hour_bucket
    ) as prev_hour_conversion_rate

  FROM comprehensive_aggregation ca
),

trend_analysis AS (
  SELECT 
    pa.*,

    -- Revenue trends
    CASE 
      WHEN prev_hour_revenue IS NOT NULL AND prev_hour_revenue > 0 THEN
        ROUND(((total_revenue - prev_hour_revenue) / prev_hour_revenue * 100), 1)
      ELSE NULL
    END as revenue_change_percent,

    -- User acquisition trends
    CASE 
      WHEN prev_hour_users IS NOT NULL AND prev_hour_users > 0 THEN
        ROUND(((unique_users - prev_hour_users)::DECIMAL / prev_hour_users * 100), 1)
      ELSE NULL
    END as user_growth_percent,

    -- Conversion optimization trends
    CASE 
      WHEN prev_hour_conversion_rate IS NOT NULL THEN
        ROUND((conversion_rate_percent - prev_hour_conversion_rate), 2)
      ELSE NULL
    END as conversion_rate_change,

    -- Growth classification
    CASE 
      WHEN prev_hour_revenue IS NOT NULL AND total_revenue > prev_hour_revenue * 1.1 THEN 'high_growth'
      WHEN prev_hour_revenue IS NOT NULL AND total_revenue > prev_hour_revenue * 1.05 THEN 'moderate_growth'
      WHEN prev_hour_revenue IS NOT NULL AND total_revenue >= prev_hour_revenue * 0.95 THEN 'stable'
      WHEN prev_hour_revenue IS NOT NULL AND total_revenue >= prev_hour_revenue * 0.9 THEN 'moderate_decline'
      WHEN prev_hour_revenue IS NOT NULL THEN 'significant_decline'
      ELSE 'insufficient_data'
    END as revenue_trend_classification,

    -- Anomaly detection indicators
    CASE 
      WHEN conversion_rate_percent > (AVG(conversion_rate_percent) OVER () + 2 * STDDEV_POP(conversion_rate_percent) OVER ()) THEN 'conversion_anomaly_high'
      WHEN conversion_rate_percent < (AVG(conversion_rate_percent) OVER () - 2 * STDDEV_POP(conversion_rate_percent) OVER ()) THEN 'conversion_anomaly_low'
      WHEN revenue_per_user > (AVG(revenue_per_user) OVER () + 2 * STDDEV_POP(revenue_per_user) OVER ()) THEN 'revenue_anomaly_high'
      WHEN revenue_per_user < (AVG(revenue_per_user) OVER () - 2 * STDDEV_POP(revenue_per_user) OVER ()) THEN 'revenue_anomaly_low'
      ELSE 'normal'
    END as anomaly_detection_status

  FROM performance_analysis pa
),

insights_and_recommendations AS (
  SELECT 
    ta.*,

    -- Strategic insights
    ARRAY[
      CASE WHEN conversion_performance_rating = 'excellent' THEN 'Maintain current conversion optimization strategies' END,
      CASE WHEN conversion_performance_rating = 'poor' THEN 'Implement conversion rate optimization initiatives' END,
      CASE WHEN user_value_rating = 'high_value' THEN 'Focus on retention and upselling strategies' END,
      CASE WHEN user_value_rating = 'minimal_value' THEN 'Develop user value enhancement programs' END,
      CASE WHEN revenue_trend_classification = 'high_growth' THEN 'Scale successful channels and campaigns' END,
      CASE WHEN revenue_trend_classification = 'significant_decline' THEN 'Investigate and address performance issues urgently' END,
      CASE WHEN anomaly_detection_status LIKE '%anomaly%' THEN 'Investigate anomalous behavior for opportunities or issues' END
    ]::TEXT[] as strategic_recommendations,

    -- Operational recommendations
    ARRAY[
      CASE WHEN event_velocity_per_minute > 1000 THEN 'Consider increasing processing capacity' END,
      CASE WHEN revenue_data_completeness < 0.9 THEN 'Improve data collection completeness' END,
      CASE WHEN location_data_completeness < 0.8 THEN 'Enhance geographic data capture' END,
      CASE WHEN processing_window_minutes > 60 THEN 'Optimize data pipeline performance' END
    ]::TEXT[] as operational_recommendations,

    -- Priority scoring for resource allocation
    CASE 
      WHEN total_revenue >= 10000 AND conversion_rate_percent >= 3.0 THEN 10  -- Highest priority
      WHEN total_revenue >= 5000 AND conversion_rate_percent >= 2.0 THEN 8
      WHEN total_revenue >= 1000 AND conversion_rate_percent >= 1.0 THEN 6
      WHEN unique_users >= 1000 THEN 4
      ELSE 2
    END as business_priority_score,

    -- Investment recommendations
    CASE 
      WHEN business_priority_score >= 8 THEN 'High investment recommended'
      WHEN business_priority_score >= 6 THEN 'Moderate investment recommended'
      WHEN business_priority_score >= 4 THEN 'Selective investment recommended'
      ELSE 'Monitor performance'
    END as investment_recommendation

  FROM trend_analysis ta
)

-- Final comprehensive analytics output with actionable insights
SELECT 
  -- Core dimensions
  hour_bucket,
  event_type,
  event_category,
  user_segment,
  customer_lifecycle_stage,
  device_category,
  geo_region,

  -- Volume and engagement metrics
  event_count,
  unique_users,
  unique_sessions,
  events_per_user,
  events_per_session,
  sessions_per_user,

  -- Revenue analytics
  ROUND(total_revenue, 2) as total_revenue,
  revenue_transactions,
  ROUND(avg_revenue_per_transaction, 2) as avg_revenue_per_transaction,
  ROUND(median_revenue, 2) as median_revenue,
  ROUND(percentile_95_revenue, 2) as percentile_95_revenue,
  ROUND(revenue_per_user, 2) as revenue_per_user,
  ROUND(revenue_per_session, 2) as revenue_per_session,

  -- Performance indicators
  conversion_rate_percent,
  ROUND(avg_order_value, 2) as avg_order_value,
  conversion_performance_rating,
  user_value_rating,
  user_engagement_rating,

  -- Trend analysis
  revenue_change_percent,
  user_growth_percent,
  conversion_rate_change,
  revenue_trend_classification,
  anomaly_detection_status,

  -- Business metrics
  business_priority_score,
  investment_recommendation,

  -- Performance rankings
  revenue_rank,
  user_engagement_rank,
  conversion_rank,

  -- Operational metrics
  ROUND(revenue_velocity_per_minute, 2) as revenue_velocity_per_minute,
  ROUND(event_velocity_per_minute, 1) as event_velocity_per_minute,
  processing_window_minutes,

  -- Data quality
  ROUND(revenue_data_completeness * 100, 1) as revenue_data_completeness_percent,
  ROUND(product_data_completeness * 100, 1) as product_data_completeness_percent,
  ROUND(location_data_completeness * 100, 1) as location_data_completeness_percent,

  -- Top performing entities
  top_product_name,
  top_campaign_id,
  most_active_hour,

  -- Strategic insights
  strategic_recommendations,
  operational_recommendations,

  -- Metadata
  window_start_time,
  window_end_time,
  CURRENT_TIMESTAMP as analysis_generated_at

FROM insights_and_recommendations
WHERE 
  -- Filter for significant segments to focus analysis
  (event_count >= 10 OR total_revenue >= 100 OR unique_users >= 5)
  AND business_priority_score >= 2
ORDER BY 
  business_priority_score DESC,
  total_revenue DESC,
  hour_bucket DESC;

-- Real-time streaming analytics with change stream processing
CREATE STREAMING_ANALYTICS_VIEW real_time_conversion_funnel AS
WITH funnel_events AS (
  SELECT 
    user_id,
    session_id,
    event_type,
    event_timestamp,
    revenue,

    -- Create event sequence within sessions
    ROW_NUMBER() OVER (
      PARTITION BY user_id, session_id 
      ORDER BY event_timestamp
    ) as event_sequence,

    -- Identify funnel steps
    CASE event_type
      WHEN 'page_view' THEN 1
      WHEN 'product_view' THEN 2  
      WHEN 'add_to_cart' THEN 3
      WHEN 'checkout_start' THEN 4
      WHEN 'purchase' THEN 5
      ELSE 0
    END as funnel_step,

    -- Calculate time between events
    LAG(event_timestamp) OVER (
      PARTITION BY user_id, session_id 
      ORDER BY event_timestamp
    ) as prev_event_timestamp

  FROM CHANGE_STREAM('raw_events')
  WHERE event_timestamp >= CURRENT_TIMESTAMP - INTERVAL '1 hour'
  AND event_type IN ('page_view', 'product_view', 'add_to_cart', 'checkout_start', 'purchase')
),

real_time_funnel_analysis AS (
  SELECT 
    DATE_TRUNC('minute', event_timestamp) as minute_bucket,

    -- Funnel step counts
    COUNT(*) FILTER (WHERE funnel_step = 1) as step_1_page_views,
    COUNT(*) FILTER (WHERE funnel_step = 2) as step_2_product_views,  
    COUNT(*) FILTER (WHERE funnel_step = 3) as step_3_add_to_cart,
    COUNT(*) FILTER (WHERE funnel_step = 4) as step_4_checkout_start,
    COUNT(*) FILTER (WHERE funnel_step = 5) as step_5_purchase,

    -- Unique user counts at each step
    COUNT(DISTINCT user_id) FILTER (WHERE funnel_step = 1) as unique_users_step_1,
    COUNT(DISTINCT user_id) FILTER (WHERE funnel_step = 2) as unique_users_step_2,
    COUNT(DISTINCT user_id) FILTER (WHERE funnel_step = 3) as unique_users_step_3,
    COUNT(DISTINCT user_id) FILTER (WHERE funnel_step = 4) as unique_users_step_4,
    COUNT(DISTINCT user_id) FILTER (WHERE funnel_step = 5) as unique_users_step_5,

    -- Revenue metrics
    SUM(revenue) FILTER (WHERE funnel_step = 5) as total_revenue,
    AVG(revenue) FILTER (WHERE funnel_step = 5 AND revenue > 0) as avg_purchase_value,

    -- Timing analysis
    AVG(EXTRACT(SECONDS FROM (event_timestamp - prev_event_timestamp))) FILTER (
      WHERE prev_event_timestamp IS NOT NULL 
      AND EXTRACT(SECONDS FROM (event_timestamp - prev_event_timestamp)) <= 3600
    ) as avg_time_between_steps_seconds

  FROM funnel_events
  WHERE funnel_step > 0
  GROUP BY DATE_TRUNC('minute', event_timestamp)
)

SELECT 
  minute_bucket,

  -- Funnel volumes
  step_1_page_views,
  step_2_product_views,
  step_3_add_to_cart, 
  step_4_checkout_start,
  step_5_purchase,

  -- Conversion rates between steps
  ROUND((step_2_product_views::DECIMAL / NULLIF(step_1_page_views, 0)) * 100, 2) as page_to_product_rate,
  ROUND((step_3_add_to_cart::DECIMAL / NULLIF(step_2_product_views, 0)) * 100, 2) as product_to_cart_rate,
  ROUND((step_4_checkout_start::DECIMAL / NULLIF(step_3_add_to_cart, 0)) * 100, 2) as cart_to_checkout_rate,
  ROUND((step_5_purchase::DECIMAL / NULLIF(step_4_checkout_start, 0)) * 100, 2) as checkout_to_purchase_rate,

  -- Overall funnel performance
  ROUND((step_5_purchase::DECIMAL / NULLIF(step_1_page_views, 0)) * 100, 2) as overall_conversion_rate,

  -- User journey efficiency
  ROUND((unique_users_step_5::DECIMAL / NULLIF(unique_users_step_1, 0)) * 100, 2) as user_conversion_rate,

  -- Revenue performance
  ROUND(total_revenue, 2) as total_revenue,
  ROUND(avg_purchase_value, 2) as avg_purchase_value,
  ROUND(total_revenue / NULLIF(unique_users_step_5, 0), 2) as revenue_per_converting_user,

  -- Efficiency metrics
  ROUND(avg_time_between_steps_seconds / 60.0, 1) as avg_minutes_between_steps,

  -- Performance indicators
  CASE 
    WHEN overall_conversion_rate >= 5.0 THEN 'excellent'
    WHEN overall_conversion_rate >= 2.0 THEN 'good'
    WHEN overall_conversion_rate >= 1.0 THEN 'fair'
    ELSE 'needs_improvement'
  END as funnel_performance_rating,

  -- Real-time alerts
  CASE 
    WHEN overall_conversion_rate < 0.5 THEN 'LOW_CONVERSION_ALERT'
    WHEN avg_time_between_steps_seconds > 300 THEN 'SLOW_FUNNEL_ALERT'  
    WHEN step_5_purchase = 0 AND step_4_checkout_start > 5 THEN 'CHECKOUT_ISSUE_ALERT'
    ELSE 'normal'
  END as real_time_alert_status,

  CURRENT_TIMESTAMP as analysis_timestamp

FROM real_time_funnel_analysis
WHERE minute_bucket >= CURRENT_TIMESTAMP - INTERVAL '30 minutes'
ORDER BY minute_bucket DESC;

-- QueryLeaf provides comprehensive data pipeline capabilities:
-- 1. SQL-familiar syntax for MongoDB aggregation pipeline construction
-- 2. Advanced real-time stream processing with Change Streams integration
-- 3. Comprehensive multi-dimensional analytics with statistical functions
-- 4. Built-in performance optimization and index utilization
-- 5. Real-time anomaly detection and business intelligence
-- 6. Advanced funnel analysis and conversion optimization
-- 7. Sophisticated trend analysis and predictive indicators
-- 8. Enterprise-ready data governance and compliance features
-- 9. Automated insights generation and recommendation systems
-- 10. Production-ready stream processing with minimal configuration

Best Practices for Production Data Pipeline Implementation

Pipeline Architecture Design Principles

Essential principles for effective MongoDB data pipeline deployment:

  1. Multi-Tier Processing Strategy: Implement real-time, near-real-time, and batch processing tiers based on latency and consistency requirements
  2. Performance Optimization: Design aggregation pipelines with proper indexing, stage ordering, and memory optimization for maximum throughput
  3. Stream Processing Integration: Leverage Change Streams for real-time processing while maintaining batch processing for historical analysis
  4. Data Quality Management: Implement comprehensive data validation, cleansing, and quality monitoring throughout the pipeline
  5. Scalability Planning: Design pipelines that can scale horizontally and handle increasing data volumes and processing complexity
  6. Monitoring and Alerting: Establish comprehensive pipeline monitoring with performance metrics and business-critical alerting

Enterprise Data Pipeline Architecture

Design pipeline systems for enterprise-scale requirements:

  1. Advanced Analytics Integration: Implement sophisticated analytical capabilities including predictive analytics and machine learning integration
  2. Data Governance Framework: Establish data lineage tracking, compliance monitoring, and audit trail maintenance
  3. Performance Monitoring: Implement comprehensive performance tracking with optimization recommendations and capacity planning
  4. Security and Compliance: Design secure pipelines with encryption, access controls, and regulatory compliance features
  5. Operational Excellence: Integrate with existing monitoring systems and establish operational procedures for pipeline management
  6. Disaster Recovery: Implement pipeline resilience with failover capabilities and data recovery procedures

Conclusion

MongoDB data pipeline optimization and stream processing provide comprehensive real-time analytics capabilities that enable sophisticated data transformations, high-performance analytical workloads, and intelligent business insights through native aggregation framework optimization, integrated change stream processing, and advanced statistical functions. The unified platform approach eliminates the complexity of managing separate batch and stream processing systems while delivering superior performance and operational simplicity.

Key MongoDB Data Pipeline benefits include:

  • Real-Time Processing: Advanced Change Streams integration for immediate data processing and real-time analytics generation
  • Comprehensive Analytics: Sophisticated aggregation framework with advanced statistical functions and multi-dimensional analysis capabilities
  • Performance Optimization: Native query optimization, intelligent indexing, and memory management for maximum throughput
  • Stream and Batch Integration: Unified platform supporting both real-time stream processing and comprehensive batch analytics
  • Business Intelligence: Advanced analytics with anomaly detection, trend analysis, and automated insights generation
  • SQL Accessibility: Familiar SQL-style data pipeline operations through QueryLeaf for accessible advanced analytics

Whether you're building real-time dashboards, implementing complex analytical workloads, processing high-velocity data streams, or developing sophisticated business intelligence systems, MongoDB data pipeline optimization with QueryLeaf's familiar SQL interface provides the foundation for scalable, high-performance data processing workflows.

QueryLeaf Integration: QueryLeaf automatically optimizes MongoDB aggregation pipelines while providing SQL-familiar syntax for complex analytics, stream processing, and data transformation operations. Advanced pipeline construction, performance optimization, and business intelligence features are seamlessly handled through familiar SQL constructs, making sophisticated data processing accessible to SQL-oriented analytics teams.

The combination of MongoDB's powerful aggregation framework with SQL-style data pipeline operations makes it an ideal platform for applications requiring both advanced analytical capabilities and familiar database interaction patterns, ensuring your data processing workflows can scale efficiently while delivering actionable business insights in real-time.

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