What if our data producers won’t agree to a contract?

Start at the consumer boundary. Define the contract and enforce it at ingest with schema validation, DLQs, and compatibility checks. Publish breaches as metrics tied to business impact. Once producers see their changes page someone, they’ll show up to the table.

We already have Monte Carlo/Soda. Isn’t that enough?

Those are great, but they’re observability. Without explicit contracts and enforcement in CI/orchestration, you’re watching failures, not preventing them. Use them to power SLIs and runbooks.

How do we avoid alert fatigue?

Alert on SLO burn rates (fast and slow), not every failed check. Group alerts by dataset with a single owner/runbook. Add quiet hours for non-critical datasets. Review noise weekly and prune.

Can we do this in streaming?

Yes. Enforce schemas via Schema Registry, route invalid events to DLQs, and run windowed completeness/freshness checks with Kafka Streams or Flink. Expose the same SLIs and wire the same alerting.

What’s the first week look like with GitPlumbers?

Day 1-2: pick top 3 datasets, draft contracts. Day 3: dbt tests + source freshness. Day 4: Great Expectations + Prometheus metrics. Day 5: Airflow circuit breaker + runbooks. You get a working slice plus a backlog to scale.

Data-engineering · Nov 21, 2025 · 10 minute read

The Tuesday Morning Dashboard Fire We Never Fought Again: Data Quality Guardrails That Block Bad Data Upstream

Data reliability isn’t a dashboard; it’s a contract, a circuit breaker, and a runbook. Here’s how we stop bad data before it burns your quarter.

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The Tuesday Morning Dashboard Fire We Never Fought Again: Data Quality Guardrails That Block Bad Data Upstream

Key takeaways

Implementation checklist