Overview
The Anomaly Detection transform identifies statistical outliers in numeric data columns. It supports multiple detection methods and configurable actions — from flagging anomalies for review to automatically removing or capping them. Available on Professional plans and above.Detection Methods
Method Selection Guide
Configuration
Threshold Interpretation
Actions
Examples
Financial Data Quality
Flag transactions with extreme amounts before loading to the warehouse:Sensor Data Cleaning
Cap extreme sensor readings to prevent dashboard spikes:Rolling Window Detection
Detect anomalies based on recent trends rather than all-time statistics:Pipeline Patterns
Review-then-Clean
Multi-Column Analysis
Apply detection to multiple numeric columns in one node:Tips
- Z-Score assumes normally distributed data — verify with Data Profiling first
- IQR is more robust to extreme outliers than Z-Score because it uses quartiles rather than mean
- Lower thresholds flag more anomalies (higher sensitivity, more false positives)
- Window Size enables rolling statistics — useful for time-series data where the distribution shifts over time
- Combine with Validation nodes for comprehensive quality: Anomaly Detection catches statistical outliers while Validation catches rule-based violations
Related
Data Quality Nodes
All data quality transforms including validation and profiling
Data Contracts
Rule-based validation at the contract level
Observability
Monitor anomaly rates across pipeline runs
Column Transforms
Additional column-level operations