Why You Don’t Want to Use Your Data Warehouse as a Feature Store

A feature store built on a data warehouse can lead to limitations in supporting real-time ML use cases, such as real-time predictions and feature serving at low latency and high concurrency levels. Additionally, data warehouses often struggle with streaming data pipelines for real-time feature engineering, which can result in added complexity and compromise on model performance. In contrast, feature platforms are designed to be reusable across various use cases, including real-time ML, and provide features such as flexible declarative feature engineering frameworks, time travel and backfills, and easy-to-use Python SDKs for data scientists. By using a feature platform, teams can reduce the complexity of their infrastructure, shorten their time to value on new features, and require fewer full-time equivalent engineers to maintain the platform.