Freshness vs Latency: Streaming Materialization Trade-offs
Feature Freshness: The time between when underlying data changes and when the feature reflects that change. Batch materialization achieves freshness measured in hours; streaming materialization achieves minutes or seconds. The trade-off involves infrastructure complexity, cost, and operational burden.
When Freshness Matters
Not all features need real-time updates. User demographics (age, location) change rarely—daily batch is sufficient. Session activity (pages viewed, items clicked) changes constantly—stale values hurt predictions. Rule of thumb: if the feature measures recent behavior (last hour, last session), freshness matters. If it measures historical patterns (lifetime value, purchase history), batch is fine. Audit feature importance: if a feature contributes less than 1% to model performance, batch materialization is acceptable regardless of freshness requirements.
Streaming Materialization Architecture
Events flow through a stream processor (Flink, Spark Streaming) that computes feature values and writes to the online store. Challenges: handling late data (events arriving after the feature was computed), maintaining state across restarts (checkpointing), and managing schema evolution (adding new features without downtime). Operational complexity is 3-5x higher than batch pipelines. Reserve streaming for features that demonstrably improve model performance when fresh.
Hybrid Materialization
Most production systems use both. Batch pipeline runs nightly, computing all features and loading both stores. Streaming pipeline runs continuously, updating only time-sensitive features. The online store contains batch features (refreshed daily) plus streaming features (refreshed continuously). This limits streaming complexity to the subset of features that truly need it while maintaining consistency for the rest.
Cost Warning: Streaming infrastructure typically costs 3-10x more than batch for the same feature. Run A/B tests measuring whether real-time freshness actually improves business metrics before committing to streaming materialization.