This topic collects articles and guides about Data Engineering, with a practical focus on production data, AI and ML engineering systems.
Feature store alternatives for production ML: when feature tables, shared transformation libraries or warehouse-first patterns are enough, and when a feature store earns its cost.
Design feature store architecture across offline feature stores, online lookup, metadata contracts and feature serving for production ML.
Medallion architecture for engineers: design raw data, trusted views and published data products with clear contracts, replay paths and ownership.
Learn what a feature store does, when ML teams need one, and when versioned feature tables or warehouse-first pipelines are enough.