Welcome to Production Operations! You've built monitored, secure, cloud-native pipelines with CI/CD. Now we'll learn to operate them in production when things go wrong.
Even well-designed pipelines face incidents. The difference between good and great data teams is how they respond.
Engagement Message
Briefly describe a data pipeline production incident you've experienced or heard about?
A data pipeline incident is any event that disrupts normal operations: failed jobs, data quality issues, performance degradation, or downstream consumer impacts.
Not every failure is an incident—a single retry that succeeds isn't. But delayed dashboards affecting business decisions? That's an incident.
Engagement Message
What is one way to distinguish between a normal retry and a true incident?
Incident response follows a structured process: detect, assess, respond, resolve, and learn. Your monitoring from Unit 5 handles detection, but humans handle everything else.
Speed matters—every minute of downtime affects users and business decisions.
Engagement Message
What's the first action you should take when your monitoring alerts you to a pipeline failure?
Incident severity determines response urgency. Severity 1: critical business impact, immediate response. Severity 2: significant impact, respond within hours. Severity 3: minor impact, fix in next business day.
A failed daily report is Severity 2. A broken real-time fraud detection pipeline is Severity 1.
Engagement Message
Which severity level (1–3) would you assign to a failure of weekly ML model training?
Root cause analysis prevents recurring incidents. Don't just fix symptoms—understand why the failure happened and what systemic changes prevent it.
Was it a code bug, infrastructure failure, data quality issue, or process gap?
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