agentic-data-stack-community/data-core/tasks/setup-monitoring.md

AI Agentic Data Stack Framework - Community Edition. Open source data engineering framework with 4 core agents, essential templates, and 3-dimensional quality validation.

# Task: Setup Monitoring

## Overview
Establishes comprehensive monitoring and observability for data infrastructure, pipelines, and applications. Implements multi-layered monitoring with real-time alerting, performance tracking, and operational intelligence to ensure system reliability and optimal performance.

## Prerequisites
- Deployed data infrastructure and pipelines
- Monitoring requirements and SLA definitions
- Stakeholder notification preferences and escalation procedures
- Infrastructure access and monitoring tool selection
- Security and compliance requirements for monitoring data

## Dependencies
- Templates: `monitoring-tmpl.yaml`, `alerting-configuration-tmpl.yaml`
- Tasks: `build-pipeline.md`, `implement-quality-checks.md`
- Checklists: `monitoring-setup-checklist.md`

## Steps

### 1. **Monitoring Strategy and Architecture**
   - Define comprehensive monitoring strategy and objectives
   - Design monitoring architecture with centralized and distributed components
   - Select monitoring tools and platforms for different monitoring layers
   - Plan monitoring data collection, storage, and retention policies
   - **Validation**: Monitoring strategy reviewed and approved by stakeholders

### 2. **Infrastructure Monitoring Setup**
   - Implement system-level monitoring for servers, containers, and cloud resources
   - Monitor CPU, memory, disk, network, and other infrastructure metrics
   - Set up service discovery and auto-registration for dynamic environments
   - Configure infrastructure alerting thresholds and escalation procedures
   - **Quality Check**: Infrastructure monitoring covers all critical system components

### 3. **Application and Pipeline Monitoring**
   - Implement application performance monitoring for data pipelines
   - Monitor pipeline execution times, throughput, and resource consumption
   - Track data flow metrics, processing stages, and transformation performance
   - Set up custom metrics for business-specific monitoring requirements
   - **Validation**: Application monitoring provides comprehensive pipeline visibility

### 4. **Data Quality and Business Monitoring**
   - Implement data quality monitoring with real-time quality scoring
   - Monitor business metrics and KPIs relevant to data operations
   - Track data freshness, completeness, and accuracy metrics
   - Set up anomaly detection for unusual patterns and outliers
   - **Quality Check**: Quality monitoring aligns with data contract requirements

### 5. **Log Management and Analysis**
   - Implement centralized logging for all system and application components
   - Set up log aggregation, parsing, and structured logging
   - Configure log-based alerting and anomaly detection
   - Implement log retention and archival policies
   - **Validation**: Log management provides comprehensive audit trail and debugging capability

### 6. **Alerting and Notification Framework**
   - Configure intelligent alerting with context-aware thresholds
   - Implement escalation procedures and on-call rotation management
   - Set up multiple notification channels (email, SMS, Slack, PagerDuty)
   - Design alert fatigue prevention with smart filtering and aggregation
   - **Quality Check**: Alerting system tested with various failure scenarios

### 7. **Dashboards and Visualization**
   - Create operational dashboards for real-time monitoring
   - Build executive dashboards for high-level metrics and KPIs
   - Implement user-specific dashboards for different stakeholder groups
   - Design mobile-friendly and responsive dashboard interfaces
   - **Final Validation**: Dashboards provide actionable insights for all stakeholder groups

## Interactive Features

### Real-Time Monitoring Dashboard
- **Live metrics** with real-time updates and streaming data
- **Interactive visualizations** with drill-down capabilities
- **Custom time ranges** and historical data analysis
- **Alert integration** with context and recommended actions

### Intelligent Alerting
- **Context-aware alerts** with relevant background information
- **Smart routing** based on alert type, severity, and on-call schedules
- **Alert correlation** to reduce noise and identify root causes
- **Automated remediation** for common issues and scenarios

### Multi-Stakeholder Views
- **Operations Dashboard**: Technical metrics and system health
- **Business Dashboard**: KPIs and business impact metrics
- **Executive Dashboard**: High-level trends and summary metrics
- **Quality Dashboard**: Data quality scores and trend analysis

## Outputs

### Primary Deliverable
- **Monitoring System** (`monitoring-implementation/`)
  - Complete monitoring infrastructure with all components
  - Configuration files for monitoring tools and platforms
  - Dashboard definitions and visualization configurations
  - Alerting rules and notification configurations

### Supporting Artifacts
- **Monitoring Documentation** - Architecture, procedures, and troubleshooting guides
- **Runbook Collection** - Operational procedures for common monitoring scenarios
- **Dashboard Gallery** - Screenshots and descriptions of all monitoring dashboards
- **Alert Playbook** - Response procedures for different alert types and scenarios

## Success Criteria

### Coverage and Completeness
- **Infrastructure Coverage**: All critical system components monitored
- **Application Coverage**: Complete pipeline and application monitoring
- **Business Coverage**: Key business metrics and quality indicators tracked
- **Alert Coverage**: Comprehensive alerting for all critical failure modes

### Validation Requirements
- [ ] All infrastructure components have monitoring and alerting
- [ ] Data pipelines have comprehensive performance and quality monitoring
- [ ] Business metrics and KPIs are tracked and visualized
- [ ] Alerting system tested with various failure scenarios
- [ ] Dashboards provide actionable insights for stakeholders
- [ ] Documentation complete with operational procedures

### Evidence Collection
- Monitoring coverage assessment showing complete system visibility
- Alert testing results demonstrating proper escalation and notification
- Dashboard usage analytics showing stakeholder engagement
- Incident response validation using monitoring and alerting systems
- Performance baseline establishment through monitoring data collection

## Monitoring Layers and Components

### Infrastructure Monitoring
- **System Metrics**: CPU, memory, disk, network utilization
- **Container Metrics**: Docker/Kubernetes resource consumption
- **Cloud Metrics**: Cloud platform-specific monitoring and billing
- **Network Monitoring**: Connectivity, latency, bandwidth utilization

### Application Monitoring
- **Pipeline Metrics**: Execution time, throughput, success rates
- **Database Monitoring**: Query performance, connection pools, locks
- **API Monitoring**: Response times, error rates, availability
- **Message Queue Monitoring**: Queue depth, processing rates, lag

### Business and Quality Monitoring
- **Data Quality**: Completeness, accuracy, consistency, timeliness
- **Business KPIs**: Revenue impact, user adoption, efficiency metrics
- **SLA Monitoring**: Service level adherence and breach detection
- **Cost Monitoring**: Infrastructure costs, optimization opportunities

### Security and Compliance Monitoring
- **Access Monitoring**: Authentication, authorization, access patterns
- **Security Events**: Failed logins, privilege escalations, anomalies
- **Compliance Tracking**: Regulatory requirement adherence
- **Audit Trail**: Complete activity logging for compliance purposes

## Technology Stack Integration

### Monitoring Platforms
- **Prometheus + Grafana**: Open-source monitoring and visualization
- **DataDog**: Comprehensive cloud monitoring platform
- **New Relic**: Application performance monitoring
- **CloudWatch**: AWS-native monitoring and alerting

### Log Management
- **ELK Stack**: Elasticsearch, Logstash, Kibana for log analysis
- **Splunk**: Enterprise log management and analysis
- **Fluentd**: Log collection and forwarding
- **Cloud Logging**: Platform-native log management services

### Alerting and Notification
- **PagerDuty**: Incident management and on-call scheduling
- **Slack**: Team notification and collaboration
- **Email**: Traditional notification delivery
- **SMS**: Critical alert notification for immediate attention

### Specialized Tools
- **Monte Carlo**: Data observability and quality monitoring
- **Great Expectations**: Data quality testing and monitoring
- **dbt**: Data transformation monitoring and documentation
- **Apache Airflow**: Workflow monitoring and management

## Alerting Strategy

### Alert Types and Severity Levels
- **Critical**: Service down, data corruption, security breach
- **Warning**: Performance degradation, quality issues, capacity concerns
- **Info**: Normal operations, scheduled maintenance, informational updates

### Alert Routing and Escalation
- **Primary On-Call**: First responder for immediate issues
- **Secondary On-Call**: Escalation for unresolved issues
- **Manager Escalation**: Extended outages or major incidents
- **Executive Notification**: Business-critical impacts

### Alert Fatigue Prevention
- **Smart Grouping**: Correlate related alerts to reduce noise
- **Dynamic Thresholds**: Adjust thresholds based on historical patterns
- **Maintenance Windows**: Suppress alerts during planned maintenance
- **Alert Review**: Regular review and tuning of alert rules

## Dashboard Design Principles

### User Experience
- **Role-Based Views**: Different dashboards for different user types
- **Mobile Responsive**: Accessible on mobile devices and tablets
- **Interactive Elements**: Drill-down and filtering capabilities
- **Real-Time Updates**: Live data with appropriate refresh rates

### Information Architecture
- **Hierarchical Navigation**: From overview to detailed views
- **Contextual Information**: Relevant metadata and explanations
- **Actionable Insights**: Clear indicators of what requires attention
- **Historical Context**: Trends and patterns over time

### Visual Design
- **Consistent Styling**: Uniform appearance across all dashboards
- **Color Psychology**: Appropriate colors for different alert states
- **Information Density**: Optimal balance of information and clarity
- **Accessibility**: Compliance with accessibility standards

## Validation Framework

### Testing and Validation
1. **Monitoring Coverage Testing**: Verify all components are monitored
2. **Alert Testing**: Validate alert triggers and notification delivery
3. **Dashboard Testing**: Ensure dashboards load and display correctly
4. **Performance Testing**: Monitor system performance under load
5. **Failure Simulation**: Test monitoring during various failure scenarios

### Continuous Improvement
- Regular review of monitoring effectiveness and coverage
- Alert tuning based on operational experience
- Dashboard optimization based on user feedback
- Monitoring strategy evolution with system changes

## Best Practices

### Implementation Strategy
- Start with essential monitoring and expand incrementally
- Focus on actionable alerts that require human intervention
- Design dashboards for specific user needs and workflows
- Implement monitoring as code for version control and automation

### Operational Excellence
- Regular review and tuning of monitoring thresholds
- Documentation of all monitoring procedures and runbooks
- Training for all team members on monitoring tools and procedures
- Post-incident reviews to improve monitoring and alerting

## Risk Mitigation

### Common Pitfalls
- **Alert Fatigue**: Too many non-actionable alerts reduce response effectiveness
- **Monitoring Gaps**: Incomplete coverage leaves blind spots in system visibility
- **Tool Sprawl**: Too many monitoring tools create complexity and overhead
- **Data Overload**: Too much information without clear actionability

### Success Factors
- Clear monitoring strategy aligned with business objectives
- Comprehensive coverage of all critical system components
- Intelligent alerting that focuses on actionable issues
- User-friendly dashboards that provide clear insights
- Regular review and improvement of monitoring effectiveness

## Notes
Effective monitoring is essential for maintaining reliable data operations and enabling proactive issue resolution. Invest in comprehensive monitoring from the beginning and continuously refine based on operational experience. Focus on actionable insights rather than just data collection.