If you’ve worked with data pipelines, you’ve probably dealt with that dreaded flood of alerts — most of which turn out to be false alarms. At some point, it stops being “observability” and starts being “background noise.”
Our team used to get hit with dozens of alerts a week. The problem wasn’t that checks were failing — it was that they weren’t telling us anything useful. So we took a step back and rebuilt our approach.
What We Started With
Like most teams, we had:
- Freshness checks
- Volume anomaly detection
- Schema change alerts
- A bit of lineage to trace problems
This is a good starting point, but we still found ourselves missing critical issues while drowning in minor ones.
What Changed
We made two big moves:
- Focused only on critical datasets at first — no need to monitor every table in the warehouse.
- Added context to every alert — if an alert doesn’t tell you where and why something failed, it’s not helpful.
- We also brought in Sifflet to help automate the boring parts and surface only the alerts that actually matter. The difference was huge.
Tips That Actually Helped Us
- Start wide with thresholds, then tighten them over time.
- Group related alerts so you don’t get 10 separate pings for the same incident.
- Keep a short “incident playbook” so whoever is on call knows exactly where to start.
- Involve data consumers — they often spot real-world issues before your monitoring does.
The Result
We’ve cut alert noise by almost half, and when something does ping, it’s usually worth dropping what we’re doing.
If you’re building or refining your observability setup, remember: more alerts ≠ better monitoring. Make them count.
Top comments (0)