Data Quality and Anomaly Detection
Your pipelines pass. Your dashboards break anyway. DataHub learns your data's normal behavior and alerts you when something actually goes wrong.
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Detect volume, freshness, schema, and field anomalies automatically
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Auto-adjusting thresholds trained on 60 days of historical data
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Lineage-aware alerts show which downstream systems are at risk
See anomaly detection live on your data
A DataHub engineer walks through your specific environment, not a generic script.
Why do data anomalies keep slipping through?
Manual thresholds miss edge cases. Alert fatigue buries real failures. By the time your team finds the issue, it is already in a dashboard.
Alert fatigue is real
Too many low-signal alerts train teams to ignore notifications, so genuine failures go unnoticed until a stakeholder reports a broken dashboard.
Thresholds break at scale
Static rules written for last quarter's data volumes fail silently when pipelines grow, leaving unmonitored gaps no one catches.
Incidents surface too late
Without lineage context, teams spend hours tracing which reports or APIs consumed bad data before a fix can begin.
Root cause takes hours
Disconnected tools mean engineers manually correlate logs, schemas, and pipeline runs to find one upstream issue.
Anomaly detection that learns your data
DataHub's ML-powered assertions establish baselines from your historical data and flag real deviations before they reach downstream consumers.
Volume and freshness monitoring
DataHub tracks row counts, byte volumes, and update cadences across every monitored dataset, flagging deviations before downstream consumers notice a problem.
- Row-count and byte-volume checks on every run
- Freshness assertions with configurable SLA windows
- Alerts fire before consumers detect a problem
Field-level and schema validation
Assertions run at the column level, catching null rates, value distributions, and unexpected schema changes that aggregate checks miss entirely.
- Null-rate and uniqueness checks per column
- Distribution shift detection on numeric fields
- Schema change alerts with field-level diff view
Custom SQL rules with anomaly detection
Write assertions in SQL against any metric your team defines. DataHub's ML layer learns the expected range and flags anomalies without manual threshold tuning.
- SQL-based assertions on any custom metric
- ML-learned thresholds replace manual tuning
- Version-controlled rules stored alongside metadata
Incident management and lineage impact
When an assertion fails, DataHub opens an incident, maps affected downstream assets via lineage, and routes alerts to the right team through your existing channels.
- Automatic incident creation on assertion failure
- Lineage graph shows all affected downstream assets
- Routes to Slack, PagerDuty, or your existing tools
From connection to continuous monitoring
Three steps. Works with your existing stack. No rebuilding pipelines.
Connect your data sources
- 50+ native connectors for warehouses, lakes, and transformation tools
- Snowflake, BigQuery, Databricks, dbt, Looker, and more
- No custom engineering work required to get started
Baselines established automatically
- ML engine analyzes up to 60 days of historical pipeline runs
- Expected ranges set for volume, freshness, and field metrics
- Thresholds adapt to seasonality and growth trends over time
Smart assertions monitor continuously
- Assertions run on every pipeline execution automatically
- Failures open incidents and map lineage impact immediately
- Alerts route to your team through Slack, PagerDuty, or webhooks
Built for enterprise data quality at scale
DataHub fits into your existing infrastructure and governance model. Deployment is flexible, access is controlled, and every assertion is programmable.
Deployment and access control
- Deploy on-premises, in your VPC, or as a managed cloud service
- Role-based access control governs who can create or modify assertions
Integrations and extensibility
- API-first design lets engineering teams automate assertion management
- 50+ connectors cover warehouses, lakes, BI tools, and orchestrators
Trusted by modern data teams
"DataHub gives our data engineering team a single place to define, monitor, and act on data quality across every pipeline. The lineage integration is what sets it apart."