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"]
---

Feature engineering happens where the data already is. Rather than exporting a
table, computing somewhere else and writing the result back, you declare what a
column or a table should contain and let LanceDB fill it.

{/* if functions launched */}
[Functions](/management/function) and
[materialized views](/feature-engineering/materialized-views) are ordinary
{/* end if */}
{/* if functions not launched */}
[Materialized views](/feature-engineering/materialized-views) are ordinary
{/* end if */}
[catalog objects](/basics/catalog), alongside tables and indexes -- created,
versioned, and refreshed like anything else, not a separate system bolted
alongside.

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

## The shape everything shares {#the-shape}

Every mechanism here separates **declaring** from **filling**, and that
separation is the point:

- Declaring is cheap and immediate. It records intent in the schema and commits
  a new table version, with no data written. Declaring a column on a
  billion-row table costs what declaring one on an empty table costs.
- Filling is a separate call you control, can retry, and -- against a deployment
  -- can hand to the server as a [job](/basics/jobs) rather than waiting on.

**Filling does not redo current work.** A refresh 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.

The corollary is worth stating plainly: **a filled value catches up with its
inputs only 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. That is a deliberate trade -- it is what makes refreshes
idempotent and cheap -- but it will surprise you if you expect a spreadsheet.

## Which one to reach for {#which-one}

| | What it produces | Filled by | Available |
| --- | --- | --- | --- |
| [Computed column](/feature-engineering/add-computed-column) | a new column on an existing table | a SQL expression | embedded, and deployments that enable it |
| [Materialized view](/feature-engineering/materialized-views) | a new table derived by a query | a refresh | embedded, and deployments that enable it |
| [Function](/management/function) {/* if functions launched */} | a computed column filled by Python | registered code | <Badge color="red">Enterprise</Badge> deployments that enable it |

**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.

{/* if functions launched */}
**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.

{/* end if */}
They compose. A materialized view can select computed columns.
A computed column can be filled by a registered function. {/* if functions launched */}

## Where the work runs {#where}

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

Locally, a refresh runs in your process. Against a deployment it runs across the
cluster, and the API reflects that: `refresh_column` raises, and
`refresh_column_async` returns a [job](/basics/jobs) handle instead.

```python
job = table.refresh_column_async("embedding")
result = job.wait()
result.rows_assigned
```

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.

That relocation is the main thing to understand about Enterprise feature
engineering. The API is the same; what changes is that the code runs somewhere
else.
That is also why a function must declare the environment it needs. {/* if functions launched */}

## Recovering from a failure {#failures}

{/* if functions launched */}
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.

{/* end if */}
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 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.

## Where to go next {#next}

<CardGroup cols={2}>
  <Card title="Add a computed column" icon="table-columns" href="/feature-engineering/add-computed-column">
    Declare a column from a SQL expression.
  </Card>
  <Card title="Refresh a column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
    Fill the rows that lack values, locally or as a job.
  </Card>
  <Card title="Refresh a materialized view" icon="table" href="/feature-engineering/materialized-views">
    Bring a derived table up to date with its source.
  </Card>
  {/* if functions launched */}
  <Card title="Functions" icon="code" href="/management/function">
    Register Python to fill a column with.
  </Card>
  {/* end if */}
</CardGroup>