PILLAR 01-04
Data Quality Monitoring That Catches Broken Data First
Automated data quality monitoring across every table in your warehouse, so a stale sync or a bad load never reaches a dashboard unnoticed.
Alerted #data-eng 0.8s ago.
Downstream impact · consumers at risk
What is data quality monitoring?
Data quality monitoring is the continuous, automated checking of your data for freshness, volume, schema, and anomaly issues. Dataobservability watches every table you connect, learns its normal behavior, and alerts your team in Slack or PagerDuty the moment something breaks, with the downstream impact already mapped.
Last updated July 2026
What you get
Built for data quality monitoring
Automated checks, not hand-written rules
ML-tuned monitors learn each tables normal range for volume, nulls, and distribution, so you catch the breakages you never wrote a test for.
Freshness and volume SLAs
Set freshness SLAs per table and get alerted when a load is late or a row count swings beyond its forecast band.
Schema change detection
A renamed or dropped column is caught instantly and traced to everything downstream that depends on it.
Low compute, low noise
Metadata-first checks keep warehouse cost tiny, and alert grouping keeps your channels clean.
How it works
From connected to caught
Connect your warehouse
Read-only metadata access to Snowflake, BigQuery, Databricks, or Redshift in a few minutes.
Auto-generate monitors
We create freshness, volume, schema, and anomaly monitors on every table, dbt-native if you use dbt.
Tune and route
Set SLAs and severities, route alerts to Slack or PagerDuty, and group related alerts into one incident.
Catch and resolve
When data breaks you get an alert with root-cause hints and the downstream blast radius, then track it to resolution.
Catch broken data before your stakeholders do
Connect your warehouse and get data quality monitoring live in 15 minutes. Transparent pricing, no credit card.