Data observability for marketing pipelines: freshness and volume alerting, schema drift detection, and reconciliation so broken feeds never reach a client dashboard.
Silent failure is the expensive kind
A hard crash is easy: something pages, someone fixes it. The costly failures are quiet. A platform adds an enum value and your CASE statement buckets it as null. A rate limit turns a partial response into a real-looking but incomplete day. A currency field changes type and spend numbers land off by a factor of a hundred. Each of these produces a dashboard that renders perfectly and is completely wrong, and you find out when a client does.
What we instrument
- Freshness monitoring per source and per account, so a single stalled client feed is visible immediately
- Volume and distribution anomaly checks that flag a day arriving 60% light before it is reported
- Schema drift detection on every source: new columns, dropped fields, type changes, unexpected enums
- Reconciliation jobs that compare warehouse totals back to platform UI figures within tolerance
- dbt test coverage on the assumptions your models depend on, wired into the run
- Alert routing into Slack or PagerDuty with severity, plus an on-call runbook per failure mode
How we work
Map every existing pipeline and rank sources by how much reporting depends on them
Instrument freshness and volume first, since those catch the majority of real incidents
Add schema contracts and reconciliation for the highest-stakes sources
Tune thresholds against real history so the alerts stay credible, then hand over runbooks
Typical stack
Frequently asked questions
Yes, and it is a common first engagement. Observability sits alongside your existing pipelines rather than replacing them, so it does not require a rewrite. It also tends to be the fastest way to find out what is actually wrong before deciding what to rebuild.
Thresholds are set from your real historical variance rather than round numbers, seasonality is accounted for, and alerts carry severity so a late non-critical source does not page anyone at 3am. An alert channel people mute is worse than no alerting, so we tune it against replayed history before going live.
Schema drift is an upstream source changing shape without warning: a renamed field, a new type, an added enum value. Ad platforms ship these changes continuously. Because the pipeline keeps running, the damage is silent and can span weeks of reporting before anyone notices.
Yes. Some clients take the tooling and runbooks and run it themselves; others keep us on for on-call coverage and fixes. Both are available, and the tooling is identical either way so you are never locked in.
Go deeper
Find out what is failing quietly
We will review your current pipelines and report where a silent failure could reach a client dashboard today.
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