PILLAR 02-04
Data Anomaly Detection With ML-Tuned Thresholds
Data anomaly detection that learns what normal looks like for every table and flags only the swings that matter, so your alerts stay high-signal.
Alerted #data-eng 0.8s ago.
Downstream impact · consumers at risk
What is data anomaly detection?
Data anomaly detection automatically identifies unexpected changes in your data, such as a sudden drop in row volume, a spike in null values, a late-arriving table, or a value distribution that has drifted. Dataobservability uses ML-tuned thresholds that learn each tables baseline and group related alerts, so you catch real breaks without alert fatigue.
Last updated July 2026
What you get
Built for data anomaly detection
Learns each tables baseline
Thresholds adapt to seasonality and growth instead of firing on every normal fluctuation.
Freshness and volume anomalies
Late arrivals and row-count swings are caught against a learned forecast band.
Schema and null spikes
Structural changes and data-quality regressions surface the moment they happen.
Low-noise by design
Related anomalies group into one incident so one root cause is not ten pings.
How it works
From connected to caught
Connect and learn
We profile each table to learn its normal volume, freshness, and distribution.
Detect deviations
Monitors flag values that fall outside the learned band.
Group and route
Related anomalies collapse into a single incident routed to the right channel.
Investigate fast
Lineage shows what is downstream of the anomaly so you triage by impact.
Catch broken data before your stakeholders do
Connect your warehouse and get data anomaly detection live in 15 minutes. Transparent pricing, no credit card.