dataops-sla-monitoring

Data freshness SLA monitoring tracks recency by comparing last update timestamps against defined thresholds using Prometheus-derived metrics or SQL checks. This prevents consumers from accessing stale data by alerting on breach or at-risk states.

Detect data completeness SLA breaches using Soda Core checks and row-count variance logic versus a reference period. This identifies missing or anomalous data volume, notifying consumer channels when completeness thresholds are violated.

Track error budget consumption by linking SLA breaches to root causes and logging violations over time. This produces dashboards summarizing weekly SLA performance and error budget health for data teams.

Yes, you can schedule automated SLA checks for gold datasets using Airflow. The system defines freshness and completeness SLAs, runs scheduled verifications, and triggers Prometheus alerting when thresholds are breached.

Soda Core handles completeness checks while Prometheus handles derived metrics for latency and freshness alerting. Together they monitor pipeline latency, alert on at-risk states, and notify consumers of SLA misses.

Create a version-controlled SLA catalog by defining freshness, completeness, and latency thresholds for data pipelines. This catalog schedules automated checks, triggers alerts on breaches, and generates SLA performance reports.