Lakehouse Monitoring
MonitoringMedium Complexity📚 Medium
Automated data quality monitoring for your lakehouse tables
Decision Guide
✓
When to Use
- →Data quality issues are discovered by end-users (too late)
- →Upstream sources change schema or data patterns without notice
- →Need to validate data after ETL pipeline runs
- →Regulatory requirement for data quality monitoring
- →Managing ML feature tables where drift affects model accuracy
✗
When NOT to Use
- →Tables with very low volume (under 100 rows)
- →Need real-time data validation (row-by-row)
Controls Using This Technology
The following 1 controls use Lakehouse Monitoring for implementation:
Need to Compare Options?
See how Lakehouse Monitoring compares to other access control approaches with our detailed comparison table.
Compare Technologies →