Data Anomaly Detection for Modern Data Platforms
Your pipelines pass. Your dashboards break anyway. DataHub learns your data patterns and alerts your team before downstream consumers notice.
- Catch volume spikes, stale tables, and schema drift automatically
- ML models learn seasonality and trends, no threshold tuning required
- Runs inside your VPC, your data never leaves your network
See data anomaly detection live
A self-guided tour of DataHub's observability layer. No sales call required.
Why data anomalies are so hard to catch
By the time a broken dashboard surfaces in standup, the anomaly is hours old. The cost is rarely the fix, it is the time spent finding it.
Volume spikes go unnoticed
A table doubles in size overnight. No alert fires. A downstream model trains on corrupted data before anyone checks.
Stale data breaks trust
A pipeline silently stops updating. Stakeholders make decisions on data that is 18 hours old and do not know it.
Schema changes break pipelines
A column gets renamed upstream. Three dashboards break. You spend the morning tracing the source.
Column drift hides in plain sight
Null rates creep up. Cardinality shifts. No single row looks wrong, but the aggregate tells a different story.
A better way to detect data anomalies
DataHub monitors your data landscape continuously, surfacing issues before they reach stakeholders.
ML models learn what normal looks like
DataHub's Smart Assertions use time-series forecasting to learn seasonality, trends, and normal variation in your data. No manual thresholds. No rules to maintain.
- Prophet-based forecasting adapts to weekly and monthly patterns
- Sensitivity controls let you tune signal-to-noise per dataset
- Mark false positives to retrain models and improve accuracy
Catch stale and bloated tables early
Volume assertions detect unexpected row count changes, including spikes, drops, and growth rate shifts. Freshness assertions monitor update patterns so stale tables surface before stakeholders do.
- Track total volume and change rates between scheduled checks
- Monitor freshness via audit logs, information schema, or last-modified columns
- Alert when tables miss expected refresh windows
Surface column drift automatically
Column-level assertions track null counts, cardinality, distributions, and value ranges. Schema assertions catch unexpected column additions, type changes, and deletions before they propagate downstream.
- Detect outliers in null rates, min/max values, and cardinality
- Validate column values against regex patterns, ranges, or allowed sets
- Alert on schema changes before downstream consumers break
Scale detection across thousands of tables
Monitoring Rules apply anomaly monitors automatically across your data landscape using search predicates. New datasets matching your criteria get monitors. Removed datasets lose them.
- Filter by DataHub domain, platform, schema, tag, or custom attribute
- Auto-apply monitors as new datasets are ingested
- Manage coverage at scale without per-table configuration
From connection to alert in three steps
DataHub works with your existing infrastructure. No pipeline rewrites. No new data stores.
Connect your data sources
Contextualize with assertions
Activate alerts with lineage context
Connects to the platforms your team already uses
DataHub supports data anomaly detection across the most common cloud warehouses and lakes. A remote executor option keeps query traffic inside your network.
Supported warehouses and lakes
Alerting integrations
Trusted by modern data teams
Financial Services
Data Engineering Lead
Outcome
Proactive anomaly detection before stakeholder impact
"DataHub gives our data engineering team visibility into quality issues we previously had no way to detect until a stakeholder reported them."
Quote sourced from Gartner Peer Insights. Gartner does not endorse any vendor, product, or service depicted in its research publications.
Questions teams ask before getting started
See anomaly detection working on your data
The product tour walks through Smart Assertions, Monitoring Rules, and alerting using realistic examples. No installation needed to get started.