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Monitoring and Observability

This document describes the monitoring and observability strategy for the Iceberg Data Engineering Platform, including metrics collection, logging, alerting, and performance monitoring.

Overview

The monitoring strategy covers all aspects of the platform, from infrastructure health to data quality, ensuring reliable operation and quick issue resolution.

Monitoring Architecture

graph TB
    subgraph "Data Sources"
        A[Application Logs]
        B[System Metrics]
        C[Data Quality Metrics]
        D[Business Metrics]
    end

    subgraph "Collection Layer"
        E[Log Aggregation]
        F[Metrics Collection]
        G[Health Checks]
    end

    subgraph "Processing Layer"
        H[Log Processing]
        I[Metrics Processing]
        J[Alert Processing]
    end

    subgraph "Storage Layer"
        K[Log Storage]
        L[Metrics Storage]
        M[Alert Storage]
    end

    subgraph "Visualization Layer"
        N[Dashboards]
        O[Alerts]
        P[Reports]
    end

    A --> E
    B --> F
    C --> F
    D --> F

    E --> H
    F --> I
    G --> J

    H --> K
    I --> L
    J --> M

    K --> N
    L --> N
    M --> O
    N --> P

Key Metrics

Infrastructure Metrics

System Health

  • CPU Usage: Per service and overall
  • Memory Usage: RAM utilization
  • Disk Usage: Storage capacity
  • Network I/O: Bandwidth utilization

Service Health

  • Service Status: Up/down status
  • Response Time: API response times
  • Error Rates: HTTP error rates
  • Throughput: Requests per second

Data Pipeline Metrics

Data Volume

  • Records Processed: Per pipeline and domain
  • Data Size: Storage utilization
  • Processing Rate: Records per minute
  • Backlog Size: Pending records

Data Quality

  • Quality Score: Overall data quality
  • Validation Failures: Failed validations
  • Completeness: Missing data percentage
  • Accuracy: Data accuracy metrics

Processing Performance

  • Pipeline Duration: Execution time
  • Resource Utilization: CPU/memory usage
  • Queue Depth: Processing queue size
  • Throughput: Processing rate

Business Metrics

Data Domains

  • Asset Property: Property sales volume
  • Flight Radar: Flight tracking volume
  • E-commerce: Order processing volume

Analytics Performance

  • Query Response Time: Average query time
  • Query Success Rate: Successful queries
  • Concurrent Users: Active users
  • Data Freshness: Time since last update

Logging Strategy

Log Levels

Application Logs

  • ERROR: Critical errors requiring immediate attention
  • WARN: Warning conditions that may need attention
  • INFO: General information about application flow
  • DEBUG: Detailed information for debugging

System Logs

  • Service Logs: Individual service logs
  • Container Logs: Docker container logs
  • Infrastructure Logs: System and network logs

Log Aggregation

```yaml

Log aggregation configuration

logging: level: INFO format: json output: stdout retention: 30d

services: - name: trino level: INFO retention: 7d - name: dagster level: DEBUG retention: 14d - name: spark level: WARN retention: 3d ```

Log Processing

```python

Log processing pipeline

@asset def process_logs(): """Process and analyze application logs"""

# Log parsing
# Error detection
# Performance analysis
# Alert generation
pass

```

Alerting Strategy

Alert Categories

Critical Alerts

  • Service Down: Service unavailable
  • Data Pipeline Failure: Pipeline execution failure
  • Data Quality Failure: Quality score below threshold
  • Storage Full: Disk space critical

Warning Alerts

  • High CPU Usage: CPU usage above 80%
  • High Memory Usage: Memory usage above 85%
  • Slow Queries: Query response time above threshold
  • Data Volume Anomaly: Unusual data volume changes

Info Alerts

  • Pipeline Success: Successful pipeline execution
  • Data Quality Good: Quality score above threshold
  • Performance Improvement: Performance metrics improvement

Alert Configuration

```yaml

Alert configuration

alerts: critical: - name: service_down condition: service_status == "down" action: notify_team escalation: immediate

- name: pipeline_failure
  condition: pipeline_status == "failed"
  action: notify_team
  escalation: immediate

warning: - name: high_cpu condition: cpu_usage > 80 action: notify_team escalation: 15min

- name: slow_queries
  condition: avg_query_time > 30s
  action: notify_team
  escalation: 30min

```

Performance Monitoring

Query Performance

Metrics

  • Query Response Time: Average and P95 response times
  • Query Success Rate: Percentage of successful queries
  • Concurrent Queries: Number of simultaneous queries
  • Resource Usage: CPU and memory per query

Optimization

  • Query Analysis: Identify slow queries
  • Index Usage: Monitor index effectiveness
  • Cache Hit Rate: Cache performance metrics
  • Partition Pruning: Partition effectiveness

Data Pipeline Performance

Metrics

  • Processing Time: Pipeline execution duration
  • Throughput: Records processed per minute
  • Resource Utilization: CPU and memory usage
  • Queue Depth: Processing queue size

Optimization

  • Parallel Processing: Increase parallelism
  • Resource Allocation: Optimize resource usage
  • Data Partitioning: Improve partition strategy
  • Caching: Implement caching strategies

Health Checks

Service Health Checks

```python

Health check implementation

def health_check(): """Comprehensive health check"""

checks = {
    'database': check_database_connection(),
    'storage': check_storage_access(),
    'services': check_service_status(),
    'data_quality': check_data_quality()
}

return {
    'status': 'healthy' if all(checks.values()) else 'unhealthy',
    'checks': checks,
    'timestamp': datetime.now()
}

```

Data Quality Health Checks

```python

Data quality health check

def data_quality_check(): """Check data quality metrics"""

metrics = {
    'completeness': check_data_completeness(),
    'accuracy': check_data_accuracy(),
    'consistency': check_data_consistency(),
    'timeliness': check_data_freshness()
}

return {
    'quality_score': calculate_quality_score(metrics),
    'metrics': metrics,
    'status': 'good' if calculate_quality_score(metrics) > 0.8 else 'poor'
}

```

Dashboard Configuration

Infrastructure Dashboard

```yaml

Infrastructure dashboard

dashboard: name: Infrastructure Health panels: - title: System Resources type: graph metrics: - cpu_usage - memory_usage - disk_usage - network_io

- title: Service Status
  type: table
  metrics:
    - service_status
    - response_time
    - error_rate
    - throughput

```

Data Pipeline Dashboard

```yaml

Data pipeline dashboard

dashboard: name: Data Pipeline Health panels: - title: Pipeline Performance type: graph metrics: - pipeline_duration - processing_rate - data_volume - quality_score

- title: Data Quality
  type: gauge
  metrics:
    - overall_quality_score
    - completeness_score
    - accuracy_score
    - timeliness_score

```

Incident Response

Incident Classification

Severity Levels

  • P1 - Critical: Service down, data loss
  • P2 - High: Performance degradation, data quality issues
  • P3 - Medium: Minor issues, optimization opportunities
  • P4 - Low: Informational, maintenance tasks

Response Procedures

P1 - Critical Incidents

  1. Immediate Response: Acknowledge within 15 minutes
  2. Escalation: Notify on-call team immediately
  3. Communication: Update stakeholders every 30 minutes
  4. Resolution: Target resolution within 4 hours

P2 - High Priority Incidents

  1. Response: Acknowledge within 1 hour
  2. Escalation: Notify team lead
  3. Communication: Update stakeholders every 2 hours
  4. Resolution: Target resolution within 24 hours

Post-Incident Review

```yaml

Post-incident review template

incident_review: incident_id: INC-001 title: Service Outage severity: P1 duration: 2h 30m root_cause: Database connection pool exhaustion impact: Service unavailable for 2.5 hours actions_taken: - Restarted database service - Increased connection pool size - Implemented connection monitoring prevention_measures: - Add connection pool monitoring - Implement circuit breaker pattern - Add automated scaling ```

Capacity Planning

Resource Planning

Current Usage

  • CPU: Average 60%, Peak 85%
  • Memory: Average 70%, Peak 90%
  • Storage: 40% utilized, 60% available
  • Network: Average 50%, Peak 80%

Growth Projections

  • Data Volume: 20% monthly growth
  • User Growth: 15% monthly growth
  • Query Volume: 25% monthly growth
  • Storage Growth: 30% monthly growth

Scaling Recommendations

  • Horizontal Scaling: Add worker nodes
  • Vertical Scaling: Increase resource allocation
  • Storage Scaling: Implement tiered storage
  • Network Scaling: Optimize bandwidth allocation

Best Practices

Monitoring Best Practices

  1. Comprehensive Coverage: Monitor all critical components
  2. Appropriate Alerting: Set meaningful thresholds
  3. Regular Review: Review and update monitoring rules
  4. Documentation: Document monitoring procedures

Performance Best Practices

  1. Baseline Establishment: Establish performance baselines
  2. Trend Analysis: Monitor performance trends
  3. Proactive Optimization: Optimize before issues occur
  4. Continuous Improvement: Regular performance reviews

Incident Management Best Practices

  1. Clear Procedures: Document incident response procedures
  2. Regular Drills: Practice incident response
  3. Post-Incident Reviews: Learn from incidents
  4. Continuous Improvement: Improve based on lessons learned
Last update: October 3, 2025
Created: October 3, 2025