Choosing Detection Strategies: When to Use What

The Core Decision: Selecting between rule based and model based detection depends on your data characteristics, team capabilities, and tolerance for false positives versus false negatives. Use Rule Based When: Your data has stable, well understood bounds. For example, user_id null ratio should always be under 0.5% regardless of volume growth. A percentage value cannot exceed 100%. Geographic data should only contain known country codes. These constraints are business logic, not statistical patterns. Rules are also better when you need perfect explainability for compliance or when your team lacks ML expertise to maintain model based systems. Rule based systems work well for critical invariants where any violation is definitely wrong. A financial pipeline where transaction amounts must be positive, or an inventory system where stock levels cannot be negative. False positives are acceptable because every alert represents a real constraint violation. Use Model Based When: Your data has growth trends or seasonality. Daily active users growing 10% month over month will quickly outgrow static thresholds. Retail traffic that spikes 10x during holidays needs adaptive baselines. Model based detection shines when normal behavior changes over time but anomalies are still deviations from the trend. Models handle high dimensional metrics better. If you monitor 1,000 tables with 10 metrics each, maintaining 10,000 manual rules is operationally impossible. A model trained on historical patterns can cover all metrics with shared infrastructure. Hybrid Approach in Practice: Most mature systems combine both. Run rules for hard constraints (schema validation, null ratios on critical fields) alongside models for volume and distribution metrics (row counts, value ranges). Salesforce uses a rule engine for conditions needing clear explanations, plus an ML model service for complex patterns. They batch model requests and use horizontal scaling to handle load. The Streaming Decision: Choose streaming detection when you need operational response times (under 5 minutes) for high value use cases like fraud detection, SLA monitoring, or systems where bad data causes immediate user impact. The cost is 3x to 5x higher infrastructure spend and operational complexity. Choose batch detection when you can tolerate detection latency of one batch interval (typically 15 minutes to 1 hour). Most analytics pipelines fall here. A corrupt daily report detected in 1 hour is acceptable, and batch detection is simpler and cheaper. Cold Start and Growth: Model based systems need warmup. AWS Glue requires at least 3 runs, but 30 to 90 days of history for reliable detection. During cold start or after major changes, expect higher false positive rates. Plan to tune sensitivity thresholds based on your tolerance. Financial systems might accept 20% false positives to catch every real issue. Analytics teams might tune for 5% false positives to reduce alert fatigue.