monitoring-observability

Set up monitoring and observability for data pipelines by enabling Prometheus scraping and Grafana dashboards, then use the provided assets/config.yaml to tailor log levels and alert rules for your ETL jobs.

The best way to visualize ETL job latency and error rates is by using Grafana dashboards, which surface real-time Prometheus metrics to help you proactively identify pipeline bottlenecks and trigger remediation.

Yes, you can use Prometheus metrics with structured logging for ETL jobs; this combination provides consistent machine-readable logs for correlation and debugging alongside real-time performance data collection.

Alerting rules for data pipeline incidents work by defining thresholds in your configuration that route alerts directly to on-call channels, enabling fast remediation when ingestion jobs experience latency spikes or failures.

Yes, you need YAML to configure pipeline alerting and log levels; the Skill uses a configurable integration through assets/config.yaml to define thresholds, routing, and structured logging formats.

Use structured logging instead of plain text for service backends because it generates consistent, machine-readable logs that enable faster correlation, debugging, and detection of issues across production-grade data pipelines.