feast-dev · franciscojavierarceo · Jan 14, 2026 · Jan 14, 2026 · Jan 14, 2026 · Jan 14, 2026
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+---
+title: "Faster On Demand Transformations in Feast 🏎️💨"
+description: "Discover how Feast's new Native Python Mode and transformations on writes reduce feature transformation latency by up to 10x, enabling faster and more efficient feature engineering workflows."
+date: 2025-01-14
+authors: ["Francisco Javier Arceo", "Shuchu Han"]
+---
+
+<div class="hero-image">
+  <img src="/images/blog/latency.png" alt="Pandas vs Native Python Latency Decomposition" loading="lazy">
+</div>
+
+# Faster On Demand Transformations in Feast 🏎️💨
+
+*Thank you to Shuchu Han and Francisco Javier Arceo for their contributions [evaluating the latency](https://github.com/feast-dev/feast/issues/4207) of transformation for On Demand Feature Views and adding [transformations on writes to On Demand Feature Views](https://github.com/feast-dev/feast/issues/4376). Thank you also to Maks Stachowiak, Ross Briden, Ankit Nadig, and the folks at Affirm for inspiring this work and creating an initial proof of concept.*
+
+Feature engineering is at the core of building high-performance machine learning models. The Feast team has introduced two major enhancements to [On Demand Feature Views](https://docs.feast.dev/reference/beta-on-demand-feature-view) (ODFVs), pushing the boundaries of efficiency and flexibility for data scientists and engineers. Here's a closer look at these exciting updates:
+
+## 1. Turbocharging Transformations with Native Python Mode
+
+Traditionally, transformations in ODFVs were limited to Pandas-based operations. While powerful, Pandas transformations can be computationally expensive for certain use cases. Feast now introduces **Native Python Mode**, a feature that allows users to write transformations using pure Python.
+
+Key benefits of Native Python Mode include:
+
+* **Blazing Speed:** Transformations using Native Python are nearly **10x faster** compared to Pandas for many operations.
+* **Intuitive Design:** This mode supports list-based and singleton (row-level) transformations, making it easier for data scientists to think in terms of individual rows rather than entire datasets.
+* **Versatility:** Users can now switch between batch and singleton transformations effortlessly, catering to both historical and online retrieval scenarios.
+
+Here's an example of using Native Python Mode for singleton transformations:
+
+```python
+@on_demand_feature_view(
+    sources=[driver_hourly_stats_view, input_request],
+    schema=[Field(name="conv_rate_plus_acc_singleton", dtype=Float64)],
+    mode="python",
+    singleton=True,
+)
+def transformed_conv_rate_singleton(inputs: Dict[str, Any]) -> Dict[str, Any]:
+    return {"conv_rate_plus_acc_singleton": inputs["conv_rate"] + inputs["acc_rate"]}
+```
+
+This approach aligns with how many data scientists naturally process data, simplifying the implementation of feature engineering workflows.
+
+## 2. Transformations on Writes: A New Dimension of Latency Optimization
+
+Until now, ODFVs operated solely as **transformations on reads**, applying logic during online feature retrieval. While this ensured flexibility, it sometimes came at the cost of increased latency during retrieval. Feast now supports **transformations on writes**, enabling users to apply transformations during data ingestion and store the transformed features in the online store.
+
+Why does this matter?
+
+* **Reduced Online Latency:** With transformations pre-applied at ingestion, online retrieval becomes a straightforward lookup, significantly improving performance for latency-sensitive applications.
+* **Operational Flexibility:** By toggling the `write_to_online_store` parameter, users can choose whether transformations should occur at write time (to optimize reads) or at read time (to preserve data freshness).
+
+Here's an example of applying transformations during ingestion:
+
+```python
+@on_demand_feature_view(
+    sources=[driver_hourly_stats_view],
+    schema=[Field(name="conv_rate_adjusted", dtype=Float64)],
+    mode="pandas",
+    write_to_online_store=True,  # Apply transformation during write time
+)
+def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
+    df = pd.DataFrame()
+    df["conv_rate_adjusted"] = features_df["conv_rate"] * 1.1
+    return df
+```
+
+With this new capability, data engineers can optimize online retrieval performance without sacrificing the flexibility of on-demand transformations.
+
+## The Future of ODFVs and Feature Transformations
+
+These enhancements bring ODFVs closer to the goal of seamless feature engineering at scale. By combining high-speed Python-based transformations with the ability to optimize retrieval latency, Feast empowers teams to build more efficient, responsive, and production-ready feature pipelines.
+
+For more detailed examples and use cases, check out the [documentation for On Demand Feature Views](https://docs.feast.dev/reference/beta-on-demand-feature-view). Whether you're a data scientist prototyping features or an engineer optimizing a production system, the new ODFV capabilities offer the tools you need to succeed.
+
+The future of Feature Transformations in Feast will be to unify feature transformations and feature views to allow for a simpler API. If you have thoughts or interest in giving feedback to the maintainers, feel free to comment directly on [the GitHub Issue](https://github.com/feast-dev/feast/issues/4584) or in [the RFC](https://docs.google.com/document/d/1KXCXcsXq1bUvbSpfhnUjDSsu4HpuUZ5XiZoQyltCkvo/edit?tab=t.0#heading=h.g6j9jyjkf5sm).
+
+Our goal is to make Feast blazing fast for feature transformation and serving.
+
+**Are you ready to unlock the full potential of On Demand Feature Views? Update your Feast repository and start building today!**