Monitoring Data Pipelines: Observability Best Practices
Best practices for monitoring data pipelines with metrics, logs, traces, and alerting. Learn what to measure, set SLAs, and catch failures before they impact users.
You can’t manage what you can’t measure. This guide covers comprehensive monitoring strategies for data pipelines.
The Three Pillars of Observability
- Metrics - Numerical measurements over time
- Logs - Detailed event records
- Traces - Request flow tracking
Key Metrics to Track
Pipeline Health Metrics
| Metric | Description | Alert Threshold |
|---|---|---|
| Success Rate | % of successful runs | < 99% |
| Latency | Processing time | > 2x baseline |
| Throughput | Records/second | < 50% baseline |
| Data Freshness | Age of latest data | > SLA |
Code Example
from prometheus_client import Counter, Histogram, Gauge
pipeline_runs = Counter(
'pipeline_runs_total',
'Total pipeline executions',
['pipeline_name', 'status']
)
processing_time = Histogram(
'pipeline_processing_seconds',
'Time spent processing',
['pipeline_name']
)
records_processed = Gauge(
'pipeline_records_processed',
'Records in last run',
['pipeline_name']
)
Structured Logging
import structlog
logger = structlog.get_logger()
def process_batch(batch_id, records):
logger.info(
"processing_batch",
batch_id=batch_id,
record_count=len(records)
)
# ... processing logic
logger.info(
"batch_complete",
batch_id=batch_id,
duration_ms=duration
)
Alerting Strategy
Define alerts based on severity:
- P1: Pipeline completely down
- P2: Significant degradation
- P3: Minor issues, no immediate action
Integrate monitoring with your Building Data Pipelines with Python: A Complete Guide for complete observability.
Dashboard Design
Build dashboards that answer:
- Is the pipeline running?
- Is data fresh?
- Are there quality issues? (see Data Quality Testing: Ensuring Trust in Your Data)
Conclusion
Effective monitoring catches issues before they become incidents. Invest in observability early.
Monitor everything. Alert wisely.