Insight · Data Engineering
Data Quality in Production: Checks, SLAs & Alerts
Stop discovering data issues when dashboards break — build checks into pipelines.
What this article explains
How production data quality is maintained through checks, SLAs, reconciliation, and practical incident response.
Who it is for
Data teams and business owners who cannot afford stale dashboards, silent failures, or unreliable reporting pipelines.
Main outcome
More trustworthy data operations, earlier issue detection, and clearer ownership when pipeline quality problems appear.
The silent failure problem
Pipeline crashes. Nobody notices for 6 hours. Dashboard shows old data. Finance makes decisions on numbers that are 2 days stale. A metric shows $0 revenue (clearly wrong). How many times has this happened to you? Data quality without checks is faith, not engineering.
The fix: Four types of checks
Schema validation
Enforce expected structure before data hits the warehouse
- ✓ Required columns present
- ✓ Data types match expectations
- ✓ No unexpected nulls in critical fields
Reconciliation checks
Compare source vs destination counts
- ✓ Row count match within threshold (±1%)
- ✓ Key aggregations align (sum, count)
Freshness SLAs
Alert when data is stale
- ✓ Pipeline completed within expected window
- ✓ Last update timestamp < X hours ago
Business rule validation
Check domain logic
- ✓ Revenue values non-negative
- ✓ Dates within reasonable range
- ✓ Critical metrics trend check (flag anomalies)
Real example: Orders pipeline
Schema check: order_id, customer_id, amount, created_at present and correct type
Reconciliation: Source count = destination count (±1%)
Freshness: Pipeline completes before 3am UTC. Alert if stale.
Business rule: Amount must be > 0. created_at within last 90 days.
Implementation roadmap
Pick your critical pipelines
Revenue, customer data, product metrics — start with 1-2
Define checks for each
Schema, reconciliation, freshness, business rules
Implement in orchestration
Airflow, dbt tests, Great Expectations, or custom
Set up alerts
Notify data engineers and stakeholders when checks fail
Build incident playbook
Triage steps, escalation path, recovery actions
What you get
- Issues caught at pipeline completion, not discovered by users
- Clear SLAs: Finance knows data is fresh within 2 hours
- Runbooks ready: Data team knows exactly how to respond
- Trust restored: Dashboards become reliable
Common questions
What is data quality in production?
Data quality in production means monitoring live pipelines with checks, SLAs, reconciliation rules, and alerts so teams can detect issues before users rely on bad data.
Why are freshness SLAs important?
Freshness SLAs help teams know whether reporting data is current enough for operational or financial decisions and trigger action when pipelines become stale.
What checks matter most for critical pipelines?
The most important checks usually cover schema validity, row-count or aggregate reconciliation, data freshness, and business-rule validation.