Bigeye · Healthcare
Bigeye cuts incident MTTR 90% on a healthcare lakehouse
90% MTTR reduction · data observability across 6,000 tables
At a glance
Key metrics
90%
reduction in incident MTTR
6,000
tables monitored
0
unnoticed SLA breaches post-GA
Challenge
The situation
Pipeline failures surfaced days later — from downstream complaints, not proactive detection. Analysts lost trust.
Approach
How we delivered
- 01 Deployed Bigeye across the lakehouse with auto-generated monitors.
- 02 Routed alerts to on-call via PagerDuty with severity tiers.
- 03 Published a data-SLA covenant with downstream consumers.
Architecture
Solution architecture
Bigeye monitors 6,000 tables in Databricks. Auto-generated freshness, volume, and schema monitors plus custom business-rule SLAs. Alerting through PagerDuty + Slack.
Architecture diagram placeholder
Data flow across source systems, platform, governance, and activation layers.
Outcomes
Measured results
- 90% MTTR reduction.
- Zero unnoticed SLA breaches in the six months after GA.
- Analyst trust score rose 24 points in a quarterly pulse survey.
Technology
Tech stack
Observability
- Bigeye
Lakehouse
- Databricks
- Delta Lake
- Unity Catalog
Alerting
- PagerDuty
- Slack
“We hear about issues before our downstream does — which is the only way this works.”
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