Data anomaly detection that knows your data
For platform engineers tired of threshold babysitting. DataHub's ML-powered Smart Assertions learn your data's patterns and surface real anomalies before they hit production.
- Detects volume, freshness, column, and custom SQL anomalies
- Learns seasonality, including Mondays, weekends, and quarter-end spikes
- Scales across Snowflake, BigQuery, Redshift, and Databricks
See it live with your stack
A DataHub engineer will walk through your environment, not a generic script.
Why do data anomalies keep slipping through?
Static thresholds miss seasonal patterns. Manual rules don't scale. By the time your team finds out, the dashboard is already wrong.
Threshold fatigue at scale
Thousands of tables. Hundreds of thresholds. Every one of them wrong the moment the data changes cadence or volume.
Seasonality blindness
A Monday spike isn't an anomaly. A quiet weekend isn't a failure. Static rules can't tell the difference, so your on-call queue fills with noise.
Scale without coverage
Writing manual rules for every new table isn't a strategy. Coverage gaps grow faster than your team can close them.
Delayed detection
By the time a broken dashboard surfaces a data issue, downstream consumers have already acted on bad numbers.
A better way to detect data anomalies
Smart Assertions replace brittle thresholds with ML-powered monitoring that learns your data's normal patterns across every dimension.
Volume anomaly detection
Smart Assertions track historical load patterns per table and flag deviations that fall outside learned norms, without requiring a manually set threshold.
- Learns expected row counts per table and time window
- Flags drops and spikes relative to historical baselines
- Covers batch, streaming, and incremental load patterns
Freshness anomaly detection
DataHub monitors update cadence per asset and alerts when a table hasn't refreshed within its expected window, accounting for known schedule variations.
- Tracks last-updated timestamps across all monitored assets
- Adapts expected cadence to day-of-week patterns
- Routes alerts to the owning team via Slack or PagerDuty
Column metric anomaly detection
Monitor null rates, distinct counts, and numeric distributions at the column level. DataHub learns what normal looks like and alerts when values drift outside that range.
- Tracks null rate, uniqueness, and value distribution
- Detects schema drift and unexpected type changes
- Configurable sensitivity per column or dataset
Custom SQL anomaly detection
When built-in monitors don't cover your business rules, Custom SQL Assertions let you define exactly what to measure and what counts as an anomaly for your data.
- Define assertions using any SQL your warehouse supports
- Set pass/fail conditions on returned numeric values
- Schedule runs independently of pipeline cadence
How it works
Three steps from connection to continuous anomaly detection across your data platform.
Connect your data warehouse
Contextualize assets with metadata
Activate Smart Assertions at scale
Built for enterprise-grade scale and security
Flexible deployment, role-based access control, and broad platform support for detecting anomalies across your entire data estate.
Flexible deployment
Role-based access control
Supported platforms
Trusted by modern data teams
Gartner Peer Insights
Verified reviewer
Outcome
Reduced time investigating data issues
"DataHub gives our team a single place to understand data lineage, ownership, and quality. The observability features have reduced the time we spend investigating data issues."
Frequently asked questions about data anomaly detection
Catch data anomalies before they reach production
DataHub's Smart Assertions monitor your data continuously, so your team spends less time investigating incidents and more time building. No long implementation required.
We'll walk through your environment in the demo. No commitment required.