# Bridged Schema

> Forward-fits old-format data into new schemas to preserve historical training data

- **Category**: Machine Learning
- **Subcategory**: Training Process and Optimization
- **Canonical URL**: https://designpattern.fyi/patterns/bridged-schema/

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## Description
**Intent**: Handle data schema evolution without discarding useful historical data that predates schema changes, which would otherwise waste training examples.

**Context**: Data schemas evolve—new fields get added, categorical fields gain new values—but historical data collected before the change doesn't have the new field. Discarding all older data wastes useful training examples.

**Solution**: Forward-fit old-format data into the new schema by filling in reasonable placeholders for missing fields (constant defaults, statistically informed values sampled from new field's distribution, or model-based imputation). Combine bridged historical data with genuine new-schema data, weighting newer data more heavily during transition.



## Use Cases
- Schema migrations in production ML systems
- Adding new features to existing models
- Evolving data pipelines without losing history
- Any situation where schema changes would otherwise force data loss






## Trade-offs


### Advantages

- Preserves useful historical training data through schema changes

- Enables smooth transitions during schema evolution

- Avoids wasting data collected before schema changes

- Allows continuous training through migrations




### Considerations & Drawbacks

- Imputed values aren't real data and can introduce bias

- Requires careful bridging strategy selection

- Needs weighting to prevent imputed values from dominating

- Adds complexity to data pipeline







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**Reference**: [Original Source](https://github.com/GoogleCloudPlatform/ml-design-patterns)