The article explains why structured data modeling is essential for large‑scale systems, compares OLTP and OLAP approaches, details ER, dimensional, Data Vault and Anchor methodologies, and walks through Alibaba's multi‑stage data‑modeling practice, including OneData implementation, dimension design, fact‑table types, and aggregation strategies.
This article presents a comprehensive methodology for building an Onedata data warehouse, covering the conceptual framework, data modeling processes, the Inmon and Kimball approaches, practical case studies from Baidu, Huawei, and banking, and key takeaways for enterprise data architecture.
This article outlines Snowball's data warehouse background, compares major modeling approaches such as ER, dimensional, DataVault and Anchor models, describes the current challenges of their dimensional model, and details the OneData methodology—including OneModel, OneID, and OneService—along with its practical implementation, results, and future plans.
This article presents the OneData methodology for designing, standardizing, and governing a data warehouse, detailing background challenges, goals, industry references, core concepts, unified business and design consolidation, data modeling layers, naming conventions, data quality controls, and the resulting operational improvements and business value.
This article presents the OneData methodology for building a robust big‑data platform, detailing background challenges, goals, unified input and output strategies, model design, naming conventions, data‑cleaning rules, and the resulting business benefits and future outlook.
By adapting Alibaba’s OneData methodology, the project establishes a unified data‑warehouse architecture, standards, and governance framework—including consolidated business intake, standardized design layers, naming conventions, and delivery metrics—that resolves data‑quality issues, enhances scalability and reusability, and delivers faster, reliable data support for evolving business needs.
