1 min lesson
What good detection looks like on a lakehouse
Rebuild the parts of "What good detection looks like on a lakehouse", then say why each one matters.
Step 1 of 2
What good detection looks like on a lakehouse
- Freshness
- Asset hasn't materialized within its SLA - catches stalled ingestion before consumers do
- Volume
- Row count drifts outside expected band - catches a dropped source or a partial load
- Distribution
- A column's stats shift (nulls spike, cardinality drops) - catches silent schema/quality rot
- Lineage
- Knowing which gold tables a bad bronze partition touched - turns containment from guesswork into a query
These are the checks that let you say “we caught it before the customer did.”
Blameless beats heroic
The mature framing is systemic: “The pipeline let a malformed partition through because we had no distribution check at the bronze boundary, so I added one and a fail-closed circuit-breaker.” That reads as someone who builds platforms. “I stayed up all night manually fixing rows” reads as someone who'll be the single point of failure forever.