lancedb/docs/web/feature-engineering/index.mdx

---
title: "Feature engineering"
sidebarTitle: "Overview"
description: "Derive columns and tables from data already in the lakehouse, without moving it somewhere else to compute."
icon: layer-group
keywords: ["feature engineering", "computed columns", "materialized views", "lakehouse", "jobs"]
---

Use feature engineering to turn raw data into features for search, analysis
and model training. LanceDB stores derived columns and materialized views
alongside the source data, so you can reuse the results across those workflows.

[Functions](/management/function) and
[materialized views](/feature-engineering/materialized-views) are ordinary
[catalog objects](/basics/catalog). Create them through the SDK or SQL,
then refresh the derived data when its inputs change.

<Note>
In LanceDB Enterprise, every operation here also has a SQL form. See [DDL](/sql/ddl/table#alter) to declare a column and [REFRESH COLUMN](/sql/dml/refresh-column) to fill it.
</Note>

## Computed columns and materialized views {#the-shape}

Computed columns and materialized views both keep derived data in the
lakehouse, but they fill it differently, and the difference decides what a
refresh costs:

| | Computed column | Materialized view |
| --- | --- | --- |
| Declaring or creating it | records the expression; no values are written | runs the query and stores its rows, unless created empty |
| A refresh | fills rows with no value and recomputes rows whose inputs changed | rebuilds the view completely from its source query |
| A refresh when nothing changed | fills nothing | still rebuilds the view and commits a new version |
| In LanceDB Enterprise | runs as a [job](/basics/jobs) | runs as a server-side materialization job |

### Computed columns fill incrementally {#incremental}

A [computed column](/feature-engineering/add-computed-column) separates
**declaring** from **filling**:

- Declaring is cheap and immediate. It records the expression in the schema
  and commits a new table version, with no data written. It does not evaluate the expression over existing rows.
- Fill the column with a separate refresh call that you can retry. In LanceDB
  Enterprise, submit the refresh as a [job](/basics/jobs) and track its progress.

**Refresh new and changed rows.** A
[refresh](/feature-engineering/refresh-column) gives values to rows that lack
them and recomputes rows whose inputs changed since they were computed. It
leaves the rest alone, so calling it twice is not twice the work, and appending
rows means the next refresh handles only the new ones.

**Computed values update when you refresh.** If a column derives from `price` and a row's
`price` is later updated, the derived value keeps its old value until the next
refresh recomputes it. Schedule refreshes after changing the source data when consumers need
up-to-date features.

### Materialized views store query results {#views}

A [materialized view](/feature-engineering/materialized-views) has no separate
declaration step: creating one runs its query and stores the result, so the view
is ready to read. A refresh rebuilds the stored result completely from the
source query; see [Refresh a materialized view](/feature-engineering/refresh-materialized-view)
for the full behavior, including automatic refresh.

Enterprise runs materialization on remote compute, as a server-side job.

## Choose a feature engineering workflow {#which-one}

| | What it produces | Filled by | Available |
| --- | --- | --- | --- |
| [View](/feature-engineering/view) | a saved query, no stored rows of its own | nothing -- it runs on every read | Enterprise |
| [Computed column](/feature-engineering/add-computed-column) | a new column on an existing table | a SQL expression | Enterprise |
| [Materialized view](/feature-engineering/materialized-views) | a new table derived by a query | a refresh | Enterprise |
| [Function](/management/function) | a computed column filled by Python | registered code | <Badge color="red">Enterprise</Badge> |
| [Geneva](/geneva) | columns filled by Python, for existing pipelines | the `geneva` package | <Badge color="red">Enterprise</Badge> |

**A view** has no refresh and no staleness window, because it stores the
query rather than its result. Reach for one first, before a computed column
or a materialized view, when the query is cheap enough to simply re-run on
every read.

**Computed columns** are the common case: a column declared with a SQL
expression, filled by [a refresh](/feature-engineering/refresh-column).

**Materialized views** derive a whole table (a filtered, projected, or
aggregated view of a source) and bring it up to date on each refresh. The
result is an ordinary table: queryable, indexable, searchable.

**Functions** run Python where the data is, for work SQL cannot express:
embedding a column, calling a model, decoding an image. Registered once,
versioned, then applied as a column. They are the way to do that on a
deployment; [Geneva](/geneva) remains available for existing pipelines.

A materialized view can select computed columns. A computed column can be
filled by a registered function.

## Where the work runs {#where}

<Badge color="red">Enterprise</Badge>

Locally, a refresh runs in your process. With LanceDB Enterprise it runs across the
cluster, and the API reflects that: `refresh_column` raises, and
`refresh_column_async` returns a [job](/basics/jobs) handle instead. Computed
columns are off by default in LanceDB Enterprise, and its administrator has to enable
them before a column can be declared there.

<CodeGroup>
```python Python icon="python"
job = table.refresh_column_async("power_score")
result = job.wait()
result.rows_assigned
```
</CodeGroup>

A backfill over a large table can therefore be started, left, and checked on
from a different process: the connection does not have to stay open.

Enterprise manages the compute for the refresh. Declaring a computed column
doesn't refresh it on a schedule unless your deployment enables automatic
refresh; see [Auto Refresh](/enterprise/auto-refresh).
That is also why a function must declare the environment it needs.

## Recovering from a failure {#failures}

A function that raises fails the job. There is no per-row retry or skip policy
in the function definition, so a function calling a flaky external service
should handle that itself, inside the function body.

When a refresh fails, fix the cause and run the refresh
again. The new refresh fills what is still empty or stale. How much of the
failed run was kept depends on where it ran (see
[Refresh a computed column](/feature-engineering/refresh-column#failures)), and a retry
can recompute values that were already written, so do not count on it costing
only the rows that did not complete.

## Related guides {#next}

<CardGroup cols={2}>
  <Card title="Create a view" icon="eye" href="/feature-engineering/view">
    A saved query, with no storage or refresh of its own.
  </Card>
  <Card title="Create a computed column" icon="table-columns" href="/feature-engineering/add-computed-column">
    Declare a column from a SQL expression.
  </Card>
  <Card title="Refresh a computed column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
    Fill the rows that lack values, locally or as a job.
  </Card>
  <Card title="Create a materialized view" icon="table" href="/feature-engineering/materialized-views">
    Store a query's result as a table.
  </Card>
  <Card title="Refresh a materialized view" icon="arrows-rotate" href="/feature-engineering/refresh-materialized-view">
    Bring a derived table up to date with its source.
  </Card>
  <Card title="Functions" icon="code" href="/management/function">
    Register Python to fill a column with.
  </Card>
</CardGroup>