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docs: Add blog post on unified transformation framework #5858

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Copilot Jan 14, 2026
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Add comprehensive blog post on new transformation framework
Copilot Jan 14, 2026
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Update feature transformation docs with new framework info
Copilot Jan 14, 2026
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Apply suggestion from @franciscojavierarceo
franciscojavierarceo Jan 14, 2026
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18 changes: 18 additions & 0 deletions docs/getting-started/architecture/feature-transformation.md
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Expand Up @@ -3,6 +3,10 @@
A *feature transformation* is a function that takes some set of input data and
returns some set of output data. Feature transformations can happen on either raw data or derived data.

{% hint style="info" %}
**New in Feast**: Check out the [Unified Transformation Framework blog post](../../blog/transformation-framework.md) to learn about the latest enhancements to feature transformations, including support for multiple compute engines and a consistent API across different transformation modes.
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@franciscojavierarceo franciscojavierarceo Jan 14, 2026 •
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@copilot this will live on the feast.dev website, not our docs so you can remove this change.

{% endhint %}

## Feature Transformation Engines
Feature transformations can be executed by three types of "transformation engines":

Expand Down Expand Up @@ -64,6 +68,20 @@ feature_view = FeatureView(
)
```

### Available Transformation Modes

The `TransformationMode` enum provides several options for executing transformations:

- **`PYTHON`**: Simple row-level Python transformations, ideal for lightweight operations
- **`PANDAS`**: DataFrame-based transformations using Pandas, great for local development
- **`SPARK`**: Distributed Spark DataFrame transformations for large-scale processing
- **`SPARK_SQL`**: SQL-based transformations executed in Spark
- **`RAY`**: Distributed transformations using Ray for Python-native distributed computing
- **`SQL`**: Database-native SQL transformations
- **`SUBSTRAIT`**: Cross-platform transformations using the Substrait protocol

Choose the mode that best fits your data scale, infrastructure, and team expertise.

### Aggregation
Aggregation is builtin API for defining batch or streamable aggregations on data. It allows you to specify how to aggregate data over a time window, such as calculating the average or sum of a feature over a specified period. Examples include:
```python
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