Feature Stores from Scratch: A Minimal Working Implementation

2026-05-05 · Source: KDnuggets · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

A minimal feature store implementation is presented, detailing how to build its five essential components using Python, DuckDB, Parquet, Redis, and FastAPI. This infrastructure addresses common challenges like training-serving skew and provides structured user context for Large Language Model (LLM) agents and Retrieval-Augmented Generation (RAG) pipelines, enabling personalized outputs. The article outlines the creation of a feature registry, an offline store for historical data using DuckDB and Parquet with AsOf joins, an online store for low-latency lookups via Redis, a materialization pipeline, and a FastAPI retrieval service. It clarifies that a vector database complements, rather than replaces, a feature store in a modern LLM stack, each solving different retrieval problems.

Key takeaway

For ML/MLOps Engineers building feature infrastructure, understand that a minimal feature store requires five distinct components: a registry, offline store, online store, materialization, and a retrieval API. This architecture prevents training-serving skew and provides critical low-latency user context for LLMs. Prioritize the offline store as the source of truth and use point-in-time joins for accurate training data, ensuring your systems are robust and consistent.

Key insights

A feature store's five core components provide consistent, low-latency feature access for both traditional ML and LLM applications.

Principles

• Define features once in a central registry.
• The offline store is the canonical source of truth.
• The online store functions as a low-latency cache.

Method

Build a feature store with a dataclass-based registry, an offline store using DuckDB/Parquet with AsOf joins, a Redis online store, a materialization pipeline, and a FastAPI retrieval service for production access.

In practice

• Implement DuckDB and Parquet for robust offline feature storage.
• Utilize Redis for sub-millisecond online feature lookups.
• Expose features via a FastAPI service for LLM context retrieval.

Topics

• Feature Stores
• Machine Learning Infrastructure
• LLM Context Retrieval
• DuckDB
• Redis
• FastAPI
• Point-in-Time Joins

Code references

• pgvector/pgvector

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.