Data Platform Observability: Best Practices for Production Systems

Data platform observability is crucial for maintaining reliable, performant data systems at scale. This guide covers essential monitoring strategies, alerting practices, and debugging techniques for production data infrastructure.

Why data observability matters

Traditional application monitoring falls short for data systems because:

• Data quality issues can propagate downstream silently
• Pipeline failures may not surface until business reports break
• Performance degradation in batch jobs affects SLAs
• Cost overruns in cloud data warehouses can escalate quickly

The three pillars of data observability

1. Metrics

Quantitative measurements of system behavior:

• Pipeline execution times
• Data volume processed
• Error rates and success rates
• Resource utilization (CPU, memory, storage)
• Cost metrics

2. Logs

Detailed records of events and operations:

• Pipeline execution logs
• Data quality check results
• Error messages and stack traces
• Audit trails for data access

3. Traces

Request flows through distributed systems:

• End-to-end pipeline execution paths
• Cross-system data lineage
• Performance bottleneck identification
• Dependency mapping

Key metrics to monitor

Pipeline health metrics

Execution metrics:

# Example metric collection in Python
pipeline_duration = time.time() - start_time
pipeline_success_rate = successful_runs / total_runs
data_freshness = current_time - latest_data_timestamp
# Send metrics to monitoring system
metrics.gauge('pipeline.duration', pipeline_duration, tags=['pipeline:user_analytics'])
metrics.gauge('pipeline.success_rate', pipeline_success_rate)
metrics.gauge('data.freshness_hours', data_freshness / 3600)

Data quality metrics:

• Row count changes (% deviation from expected)
• Null value percentages
• Unique constraint violations
• Schema drift detection

• Data distribution shifts

Infrastructure metrics

Compute resources:

• Warehouse query queue depth
• Cluster utilization rates
• Auto-scaling events
• Job scheduling delays

Storage metrics:

• Table growth rates
• Partition performance
• Compression ratios
• Storage costs per GB

Alerting strategies

Critical alerts (page immediately)

• Pipeline failures blocking business-critical reports
• Data freshness SLA violations
• Security incidents (unauthorized access)
• Cost anomalies exceeding budget thresholds

Warning alerts (next business day)

• Data quality degradation trends
• Performance regression (>20% slowdown)
• Unusual data volume changes
• Dependency failures with fallbacks

Informational alerts (weekly digest)

• Capacity planning recommendations
• Optimization opportunities
• Usage pattern changes
• Cost optimization suggestions

Implementing data quality monitoring

Schema monitoring

-- Monitor schema changes
WITH schema_changes AS (
  SELECT 
    table_name,
    column_name,
    data_type,
    is_nullable,
    column_default,
    ordinal_position
  FROM information_schema.columns
  WHERE table_schema = 'production'
)
SELECT * FROM schema_changes
WHERE last_updated > CURRENT_DATE - INTERVAL '1 DAY';

Automated data profiling

def profile_table(table_name):
    """Generate comprehensive data profile"""
    profile = {
        'row_count': get_row_count(table_name),
        'null_percentages': get_null_rates(table_name),
        'unique_counts': get_unique_counts(table_name),
        'value_distributions': get_distributions(table_name),
        'data_types': get_column_types(table_name)
    }
    
    # Compare against baseline
    anomalies = detect_anomalies(profile, baseline_profile)
    if anomalies:
        send_alert(f"Data quality anomalies detected in {table_name}", anomalies)
    
    return profile

Monitoring data lineage

Impact analysis

Track data dependencies to understand failure impact:

# Example lineage tracking
lineage_graph = {
    'raw.users': ['staging.users'],
    'staging.users': ['marts.user_metrics', 'marts.user_segments'],
    'marts.user_metrics': ['dashboard.daily_kpis']
}
def get_downstream_impact(failed_table):
    """Find all tables affected by upstream failure"""
    affected = []
    queue = [failed_table]
    
    while queue:
        current = queue.pop(0)
        if current in lineage_graph:
            downstream = lineage_graph[current]
            affected.extend(downstream)
            queue.extend(downstream)
    
    return list(set(affected))

Cost monitoring

Warehouse query monitoring

-- Monitor expensive queries in Snowflake
SELECT 
    query_id,
    query_text,
    warehouse_name,
    execution_time,
    bytes_scanned,
    credits_used,
    user_name
FROM snowflake.account_usage.query_history
WHERE start_time > CURRENT_TIMESTAMP - INTERVAL '1 HOUR'
  AND credits_used > 1.0
ORDER BY credits_used DESC;

Cost anomaly detection

• Daily spend comparisons
• Query cost per byte processed
• Warehouse utilization efficiency
• Storage growth rates

Debugging production issues

Incident response playbook

1. Immediate assessment (< 5 minutes)

   • Check system status dashboards
   • Identify scope of impact
   • Determine if manual intervention needed

2. Root cause analysis (< 30 minutes)

   • Review recent deployments/changes
   • Check upstream data source status
   • Analyze error logs and traces
   • Validate data quality metrics

3. Resolution (variable)

   • Apply immediate fixes/workarounds
   • Implement long-term solutions
   • Update monitoring to prevent recurrence

Common debugging patterns

Pipeline failures:

# Check recent job executions
kubectl logs -l app=data-pipeline --since=1h
# Review data quality results  
SELECT * FROM data_quality_results 
WHERE check_timestamp > NOW() - INTERVAL '2 HOURS'
  AND status = 'FAILED';
# Analyze resource utilization
SELECT warehouse_name, avg_running_queries, queued_queries
FROM warehouse_load_history 
WHERE start_time > CURRENT_TIMESTAMP - INTERVAL '4 HOURS';

Tool recommendations

Open source solutions

• Prometheus + Grafana: Metrics and dashboards
• ELK Stack: Centralized logging
• Jaeger: Distributed tracing
• Great Expectations: Data quality testing

Commercial solutions

• Datadog: Comprehensive observability platform
• New Relic: Application and infrastructure monitoring
• Monte Carlo: Data observability specialist
• Observe: Modern observability platform

Building a monitoring culture

Team practices

• Runbooks: Document common issues and solutions
• On-call rotation: Shared responsibility for system health
• Post-mortems: Learn from incidents without blame
• Regular reviews: Weekly system health discussions

Continuous improvement

• Monitor your monitoring (alert fatigue metrics)
• Regular dashboard reviews and cleanup
• Performance baseline updates
• SLA refinements based on business needs

Related reading:

• Medallion Architecture Pitfalls - Build observable data layers from the start
• Hidden Costs of Data Platform Technical Debt - Why monitoring prevents expensive technical debt

Building robust data platform observability? Our team at Black Dog Labs has extensive experience implementing monitoring solutions for enterprise data infrastructure. Contact us to discuss your observability strategy.