Welcome to Pipeline Monitoring & Alerting! Remember how we deployed containerized pipelines to the cloud? Now we'll learn to keep those pipelines healthy with proper observability.
Production data pipelines fail—it's not if, but when. The key is detecting problems before they impact your users.
Engagement Message
What's one serious business impact if a critical data pipeline fails silently for hours?
Observability is your pipeline's health monitoring system. It's built on three pillars: logs (what happened), metrics (how much/fast), and traces (where time was spent).
Think of it like monitoring a patient—logs are symptoms, metrics are vital signs, traces show blood flow.
Engagement Message
Which pillar would best help you find why a pipeline suddenly got 50% slower?
Logs capture discrete events: "Started processing batch 1234", "Failed to connect to database", "Completed transformation step". They're your detective's notebook for troubleshooting.
But logs alone aren't enough—you need metrics for the bigger picture.
Engagement Message
What's one limitation of only having logs when monitoring a data pipeline?
Metrics provide quantitative measurements over time: records processed per minute, memory usage, error rates. They power your monitoring dashboards and alert thresholds.
Good metrics answer questions like "Is the pipeline keeping up with incoming data?"
Engagement Message
Can you name one metric that would help you detect if your pipeline is falling behind?
Traces show the complete journey of a single data record through your pipeline. They reveal where bottlenecks occur and how components interact.
This is crucial for complex pipelines with multiple steps and dependencies.
Engagement Message
How could traces reveal the bottleneck in an 8-step data transformation?
