> For the complete documentation index, see [llms.txt](https://docs.feast.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.feast.dev/v0.43-branch/getting-started/architecture/feature-transformation.md).

# Feature Transformation

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.

## Feature Transformation Engines

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

1. The Feast Feature Server
2. An Offline Store (e.g., Snowflake, BigQuery, DuckDB, Spark, etc.)
3. A Stream processor (e.g., Flink or Spark Streaming)

The three transformation engines are coupled with the [communication pattern used for writes](/v0.43-branch/getting-started/architecture/write-patterns.md).

Importantly, this implies that different feature transformation code may be used under different transformation engines, so understanding the tradeoffs of when to use which transformation engine/communication pattern is extremely critical to the success of your implementation.

In general, we recommend transformation engines and network calls to be chosen by aligning it with what is most appropriate for the data producer, feature/model usage, and overall product.


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