Feature Stores Cheat Sheet

Manage offline and online feature pipelines for ML with a feature store, covering feature views, materialization, and point-in-time joins.

2 PagesIntermediateJan 29, 2026

Define a Feature View (Feast)

Declare a feature view over an offline source with an entity key and TTL.

python

from feast import Entity, FeatureView, Field, FileSourcefrom feast.types import Float32, Int64from datetime import timedeltadriver = Entity(name="driver_id", join_keys=["driver_id"])driver_stats_source = FileSource(    path="data/driver_stats.parquet",    timestamp_field="event_timestamp",)driver_stats_fv = FeatureView(    name="driver_hourly_stats",    entities=[driver],    ttl=timedelta(days=1),    schema=[Field(name="conv_rate", dtype=Float32), Field(name="trips", dtype=Int64)],    source=driver_stats_source,)

Apply and Materialize

bash

# register feature views/entities defined in feature_store.pyfeast apply# backfill the online store from the offline store up to nowfeast materialize-incremental $(date -u +%Y-%m-%dT%H:%M:%S)# inspect the registryfeast feature-views list

Point-in-Time Correct Training Data

Join historical features to labeled events without leaking future data.

python

from feast import FeatureStorestore = FeatureStore(repo_path=".")training_df = store.get_historical_features(    entity_df=entity_df,  # has driver_id + event_timestamp + label    features=[        "driver_hourly_stats:conv_rate",        "driver_hourly_stats:trips",    ],).to_df()

Fetch Online Features at Inference

Retrieve the latest feature values for a set of entities with low-latency reads.

python

features = store.get_online_features(    features=["driver_hourly_stats:conv_rate", "driver_hourly_stats:trips"],    entity_rows=[{"driver_id": 1001}],).to_dict()print(features)

Core Concepts

Terminology used consistently across Feast, Tecton, and Databricks Feature Store.

Entity- the primary key features are joined on (e.g. user_id, driver_id)
Feature view- a group of related features tied to an entity and a source
Offline store- historical feature values used to build training datasets
Online store- low-latency key-value store serving features at inference time
Materialization- the job that copies computed features from offline to online store
Training-serving skew- mismatch between features seen at train time vs serve time; the problem feature stores solve

Pro Tip

Always compute training data via get_historical_features with point-in-time joins rather than pulling straight from the online store — it's the only way to guarantee the model never trains on data that wasn't actually available at prediction time.

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