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Star schemas and OLAP cubes

中英对照 Fundamental Concepts Kimball Group 官网公开资料（Dimensional Modeling Techniques Glossary） 原文出处 ↗ 本文 .md

Star schemas and OLAP cubes

星型模式与 OLAP 立方体

Dimensional models focus on process measurement events, dividing data into either measurements or the “who, what, where, when, why, and how” descriptive context.

维度模型聚焦于过程度量事件，将数据划分为度量本身，或"谁、什么、何处、何时、为何、如何"的描述性上下文。

Dimensional models can be instantiated in both relational databases, referred to as star schemas, or multidimensional databases, known as online analytical processing (OLAP) cubes. Star schemas characteristically consist of fact tables linked to associated dimension tables via primary/foreign key relationships. OLAP cubes can be equivalent in content to, or more often derived from, a relational star schema. An OLAP cube contains dimensional attributes and facts, but it is accessed via languages with more analytic capabilities than SQL, such as XMLA. OLAP cubes are included in this list of basic techniques because a cube is often the final deployment step of a dimensional DW/BI system, or may exist as an aggregate structure based on a more atomic relational star schema.

维度模型既可以在关系数据库中实例化，称为星型模式（star schema）；也可以在多维数据库中实例化，称为联机分析处理（OLAP）立方体。星型模式的典型结构是事实表通过主键/外键关系连接到关联的维度表。OLAP 立方体可以在内容上等同于关系型星型模式，更多情况下则是由星型模式派生而来。立方体包含维度属性和事实，但需要通过比 SQL 更具分析能力的语言来访问，例如 XMLA。之所以把 OLAP 立方体列入基础技法清单，是因为立方体往往是维度化 DW/BI 系统最终部署的形态，或者作为基于更原子的关系型星型模式的聚合结构而存在。

The word “Kimball” is synonymous with dimensional modeling. Ralph didn’t invent the original basic concepts of facts and dimensions, however, he established an extensive portfolio of dimensional techniques and vocabulary, including conformed dimensions, slowly changing dimensions, junk dimensions, mini-dimensions, bridge tables, periodic and accumulating snapshot fact tables, and the list goes on. Over the past nearly 30 years, Ralph and his Kimball Group colleagues have written hundreds of articles and Design Tips on dimensional modeling, as well as the seminal text, The Data Warehouse Toolkit, Third Edition (Wiley, 2013).

"Kimball"这个词与维度建模是同义词。Ralph 并没有发明事实与维度这些最初的基本概念，但他建立了一整套丰富的维度技法和词汇体系，包括一致性维度、缓慢变化维度、垃圾维度、微型维度、桥接表、周期快照与累积快照事实表，等等。在过去的近 30 年里，Ralph 和他的 Kimball Group 同事们撰写了数百篇关于维度建模的文章和《设计提示》（Design Tips），以及奠基性著作《数据仓库工具箱》（The Data Warehouse Toolkit，第三版）（Wiley，2013）。

While Ralph led the charge, dimensional modeling is appropriate for organizations who embrace the Kimball architecture, as well as those who follow the Corporate Information Factory (CIF) hub-and-spoke architecture espoused by Bill Inmon and others. Dimensional modeling best practices are architecture-neutral.

在 Ralph 带领这场浪潮的同时，维度建模既适用于拥抱 Kimball 架构的组织，也同样适用于遵循 Bill Inmon 等人所倡导的企业信息工厂（CIF）辐射状架构的组织。维度建模的最佳实践与架构无关。

For a brief overview of dimensional modeling, we suggest starting with the following series of articles. Full coverage is available in The Data Warehouse Toolkit, Third Edition.

关于维度建模的简要概述，我们建议从下面这组文章读起。完整的阐述见《数据仓库工具箱》（第三版）。