This guide explains the DWD (detail) and DWS (summary) layered modeling approach combined with wide‑table driving, covering model positioning, design principles, concrete schema examples, implementation techniques, performance tips, and common pitfalls to help build clean, reusable, high‑performance enterprise data warehouses.
This article outlines the step‑by‑step evolution of Baidu's Feed data warehouse—from traditional layered modeling to hour‑level core tables, then real‑time wide tables, and finally a flow‑batch integrated multi‑version wide‑table architecture—highlighting the motivations, design choices, challenges, and resulting benefits.
Baidu’s Mobile Ecology team transformed its Feed data warehouse through three progressive stages—hour‑level core tables, a real‑time wide table, and a unified day‑level multi‑version table—consolidating traffic, content, and user data into a single partitioned wide‑table architecture that resolves granularity inconsistencies, cuts processing cost, and delivers real‑time to daily latency for diverse analytics.
This article explains how to accelerate slow MySQL join queries in high‑traffic e‑commerce fulfillment systems by using wide tables, MySQL JSON columns, virtual columns, and indexed expressions, comparing their trade‑offs and providing practical SQL examples.
The article describes how NetEase News' recommendation team replaced a slow, batch‑oriented data‑warehouse pipeline with a Flink‑based, Apache Paimon real‑time wide‑table solution that supports partial updates, reduces latency from hours to minutes, and lowers processing costs while handling both deduplication and non‑deduplication recommendation scenarios.
The article explains how wide tables are maintained without excessive resource consumption, outlines criteria for deciding which metrics belong in a wide table, describes their granularity and timeliness considerations, and clarifies that they do not automatically expand when tracking points change.
This article analyzes the shift from traditional multi‑layer data warehouse dimensional modeling to a single-layer wide‑table approach, detailing business drivers, technical architecture, storage and query performance gains, as well as the development, maintenance, and operational challenges involved.
