Feature Group User Guides#

A feature group is a table of features with a primary key and, usually, an event time. These guides cover creating one, keeping its data correct, and managing it over time.

Start here

Create a feature group and insert a DataFrame. The schema is inferred from the DataFrame on the first insert.
```
fg = fs.get_or_create_feature_group(
    name="transactions",
    version=1,
    primary_key=["tid"],
    event_time="datetime",
)
fg.insert(df)
```
Create a feature group · Data types and schema · Statistics

Create and write

Create a feature group Offline and online tables, primary keys, event time, partitioning.
External feature groups Read data that stays in a warehouse or object store.
Spine groups Supply keys, event times and labels without storing features.
Ingest with dltHub Load from external sources through dlt pipelines.
Data types and schema Type mapping, adding features, schema versions.

Trust

Statistics What is computed on insert and how to configure it.
Data validation Great Expectations on insert, then the advanced guide and best practices.
Feature monitoring Scheduled statistics and comparison to a reference window.
Online ingestion observability Track rows arriving in the online store.

Manage

On-demand transformations Compute features at request time from request parameters.
Notifications Emit change events to a Kafka topic.
Time to live Expire rows after a retention period.
Deprecate Mark a group as retired without deleting it.