This article explains how Hive serves as a data‑warehouse layer for user‑profile tagging, covering data‑warehouse fundamentals, fact‑and‑dimension modeling, partitioned storage, label aggregation, and ID‑mapping techniques with practical Hive DDL/DML examples.
This article explains the fundamentals of ID‑Mapping, its importance for unifying fragmented user and device data, showcases industry solutions from Alibaba, NetEase, 58.com and Meituan, and outlines technical approaches such as priority‑based rules and graph‑based computation.
This article explains why ID mapping is essential for breaking data silos, describes traditional address cleaning and DSP scenarios, and details a graph‑based, six‑step process that builds a unified One‑ID dictionary to enable comprehensive user profiling and analytics in big‑data environments.
This article explains how to design Hive data warehouse schemas, covering fact and dimension table modeling, partitioned storage strategies, tag aggregation techniques, and ID‑mapping implementations using Hive SQL and UDFs to support user profiling and analytics.
This article presents an in‑depth overview of NetEase's massive big‑data ecosystem, its diverse product lines, the full‑link data platform matrix, user‑profile taxonomy, ID‑mapping techniques, regional domain insights, tag management, quality assurance mechanisms, and real‑world case studies demonstrating applications in marketing, recommendation, growth, and anti‑fraud scenarios.
This presentation details NetEase's massive multi‑domain data ecosystem, the design of its user‑profile center—including basic, behavior, preference, and predictive tags—ID‑mapping techniques, quality assurance processes, and several real‑time and offline use cases such as marketing, recommendation, growth operations, advertising, and fraud detection.
The article describes the design, data integration, ID mapping, tag generation, and application scenarios of the 58 user profiling platform, which aggregates billions of user IDs across multiple business lines to provide online and offline persona data for personalization, analytics, and AI modeling.
