The question.
Teams often train models on one definition of data and serve decisions from another, while dashboards hide late or partially processed events.
The proposed platform puts data contracts, lineage, replay, and freshness indicators alongside the tables and features that consumers depend on.
The proposed approach.
- Ingest versioned events with explicit schemas
- Build incremental, tested transformations for analytical tables
- Generate point-in-time-correct training features
- Track data freshness and provide repeatable backfills
Target architecture.
EventsStream ingestionLakehouse tablesTested transformationsFeature viewsAnalytics and models
The difficult parts.
- Late data and schema evolution
- Training-serving feature differences
- Backfills that change previously reported numbers
How to evaluate it.
- Freshness and data-quality checks under replay
- Point-in-time correctness of training joins
- Recovery time and cost for backfills
Planned deliverables.
- A reference data-contract collection
- A reproducible ingestion-to-feature workflow
- A lineage and freshness reporting design
Design principles.
Data quality is part of the product interface.
A feature is only reproducible when its time boundary is explicit.