> 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/v0.25-branch/getting-started/architecture-and-components/batch-materialization-engine.md).

# Batch Materialization Engine

A batch materialization engine is a component of Feast that's responsible for moving data from the offline store into the online store.

A materialization engine abstracts over specific technologies or frameworks that are used to materialize data. It allows users to use a pure local serialized approach (which is the default LocalMaterializationEngine), or delegates the materialization to seperate components (e.g. AWS Lambda, as implemented by the the LambdaMaterializaionEngine).

If the built-in engines are not sufficient, you can create your own custom materialization engine. Please see [this guide](/v0.25-branch/how-to-guides/customizing-feast/creating-a-custom-materialization-engine.md) for more details.

Please see [feature\_store.yaml](/v0.25-branch/reference/feature-repository/feature-store-yaml.md#overview) for configuring engines.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.feast.dev/v0.25-branch/getting-started/architecture-and-components/batch-materialization-engine.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.