← Back to all products

Feature Store Setup Guide

$39

Build a feature store with Feast or custom implementation. Feature engineering, versioning, and serving patterns.

📁 34 files
MarkdownYAMLPython

📄 Product Preview

Try the interactive reader and demo tools below, or get the full product with all content unlocked.

📖 Interactive Reader (Free Preview) ⚙ Try Demo Tools 📦 Download Free Sample

📁 File Structure 34 files

feature-store-setup/ ├── LICENSE ├── README.md ├── configs/ │ ├── __pycache__/ │ │ └── feast_feature_repo.cpython-312.pyc │ ├── feast_feature_repo.py │ ├── feast_feature_store.yaml │ └── feature_store_config.yaml ├── free-sample.zip ├── guide/ │ ├── 01-feast-integration.md │ └── 02-setup-guide.md ├── guides/ │ ├── feast-integration.md │ └── setup-guide.md ├── index.html ├── requirements.txt ├── src/ │ ├── __init__.py │ ├── __pycache__/ │ │ ├── __init__.cpython-312.pyc │ │ ├── feature_registry.cpython-312.pyc │ │ ├── feature_store.cpython-312.pyc │ │ ├── materialization.cpython-312.pyc │ │ ├── point_in_time.cpython-312.pyc │ │ ├── serving_api.cpython-312.pyc │ │ └── versioning.cpython-312.pyc │ ├── feature_registry.py │ ├── feature_store.py │ ├── materialization.py │ ├── point_in_time.py │ ├── serving_api.py │ └── versioning.py └── tests/ ├── __init__.py ├── __pycache__/ │ ├── __init__.cpython-312.pyc │ ├── test_feature_registry.cpython-312.pyc │ └── test_point_in_time.cpython-312.pyc ├── test_feature_registry.py └── test_point_in_time.py

📖 Documentation Preview README excerpt

Feature Store Setup

Build a feature store with point-in-time correct joins, offline/online stores, and a feature registry.

Feature definitions, versioning, materialization workflows, and a serving API — with a stdlib reference implementation that runs locally and Feast configuration examples for production.

What You Get

  • Feature registry for defining, versioning, searching, and validating features with ownership metadata
  • Offline store (local Parquet reference impl) for batch feature retrieval during training
  • Online store (in-memory reference impl) for low-latency feature serving at inference time
  • Point-in-time correct joins that prevent training/serving skew and data leakage
  • Feature versioning with change tracking, diffs, and rollback support
  • Materialization scheduler for computing features on configurable cadences
  • Feature serving API (stdlib HTTP server) for real-time feature retrieval
  • Feast configuration examples (feature_store.yaml + feature definitions) for production migration
  • TTL-based expiry to automatically invalidate stale features

Quick Start


pip install -r requirements.txt
python -c "
from src.feature_store import FeatureStore
import pandas as pd

store = FeatureStore()
df = pd.DataFrame({
    'customer_id': [1, 2, 3],
    'avg_spend': [100.0, 200.0, 150.0],
    'event_time': pd.Timestamp.now(),
})
store.materialize('avg_spend', df, 'customer_id', 'avg_spend')
print(store.get_online_features(['avg_spend'], ['1', '2']))
"

License

MIT License — see [LICENSE](LICENSE).

Support

Questions or issues? Email support@datanest.dev

Part of [ML Engineer Toolkit](https://datanest-stores.pages.dev/ml-engineer/)

📄 Code Sample .py preview

src/feature_registry.py """ Feature Registry — Define, Version, and Discover Features ========================================================= A centralised catalog of feature definitions. Each feature has a name, data type, entity key, description, and owner — making it easy for teams to discover and reuse features instead of rebuilding them from scratch. Why a registry? - Prevents duplicate features (two teams computing the same thing differently leads to inconsistent models). - Provides lineage: which data source does this feature come from? - Enables governance: who owns this feature? When was it last updated? """ from __future__ import annotations import json import logging from dataclasses import dataclass, field from datetime import datetime, timezone from enum import Enum from pathlib import Path from typing import Any, Dict, List, Optional logger = logging.getLogger(__name__) class FeatureType(str, Enum): INT = "int" FLOAT = "float" STRING = "string" BOOL = "bool" LIST_FLOAT = "list_float" TIMESTAMP = "timestamp" class FeatureStatus(str, Enum): ACTIVE = "active" DEPRECATED = "deprecated" EXPERIMENTAL = "experimental" @dataclass class FeatureDefinition: """Schema for a single feature in the registry.""" name: str feature_type: FeatureType entity_key: str # e.g. "customer_id", "product_id" # ... 193 more lines ...
Buy Now — $39 Back to Products