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数据仓库建设快速入门(一)---概念

Posted on 2009-02-19 14:56  如果你也听说  阅读(2523)  评论(0编辑  收藏  举报

根据Bill.Inmon的定义,“数据仓库是面向主题的、集成的、稳定的、随时间变化的主要用于决策支持的数据库系统”。

我的理解是,数据仓库特点是操作型数据库的延伸,用于归档主题的历史信息,可以温故而知新。

下面列举一些引自http://www.1keydata.com/datawarehousing/glossary1.html的一些概念解释:

ETL: Stands for Extraction, Transformation, and Loading. The movement of data from one area to another.

Fact Table: A type of table in the dimensional model. A fact table typically includes two types of columns: fact columns and foreign keys to the dimensions.

Hierarchy: A hierarchy defines the navigating path for drilling up and drilling down. All attributes in a hierarchy belong to the same dimension.

Metadata: Data about data. For example, the number of tables in the database is a type of metadata.

Metric: A measured value. For example, total sales is a metric.

MOLAP: Multidimensional OLAP. MOLAP systems store data in the multidimensional cubes.

OLAP: On-Line Analytical Processing. OLAP should be designed to provide end users a quick way of slicing and dicing the data.

ROLAP: Relational OLAP. ROLAP systems store data in the relational database.

Snowflake Schema: A common form of dimensional model. In a snowflake schema, different hierarchies in a dimension can be extended into their own dimensional tables. Therefore, a dimension can have more than a single dimension table.

Star Schema: A common form of dimensional model. In a star schema, each dimension is represented by a single dimension table.

以后的章节中,将会举例说明相关的含义,以及一些常见的建设原则