Engineering Notes
A Practical Checklist for Data Workflow Reliability
A compact checklist for reviewing workflow dependencies, failure patterns, SLA risk, alerting, logs, permissions, resources, and AI diagnosis readiness.
Workflow dependency review
Map upstream and downstream dependencies for critical workflows. Identify hidden dependencies, manual triggers, and workflows with unclear ownership.
Failure pattern review
Classify recurring failures by error type, component, owner, and remediation path. Repeated failures should become structured knowledge, not repeated manual investigation.
SLA review
Define which workflows have business-critical deadlines. Track delay risk before downstream consumers are affected.
Alerting review
Alerts should include workflow context, owner, severity, recent changes, and links to relevant logs or dashboards.
Log collection review
Centralize task logs and normalize workflow, task, environment, and error fields. AI diagnosis quality depends heavily on this foundation.
Permission and resource review
Review scheduler permissions, worker groups, quotas, and resource contention. Reliability problems are often governance problems.
AI diagnosis readiness
Collect representative logs, historical incidents, internal fixes, and platform metadata. Start with the top recurring failures before scaling to all workflows.