DW数据仓库

https://blog.csdn.net/bjweimengshu/article/details/79256504

from Wikipedia

　　在计算机科学中，数据仓库（data warehouse，简称DW或DWH）也叫做企业数据仓库（EDW），是一种对数据进行分析和报表的系统，是商业智能（business intellgence简称BI）的核心组件。数仓是数据从一个或多个不同的源集成过程中的中心仓库。数仓从一个地方储存实时和历史数据，为所有企业的员工生成数据报表。

　　上传到数仓的数据来源于运行中的系统（如市场或销售），数仓用来报表的这些数据可能通过运行数据存储，需要额外的操作进行数据清洗，来确保数据质量。

　　基于数仓的典型的提取、转换、加载（ETL）过程中使用暂存层、数据集成层和访问层来构建其关键功能。暂存层或暂存数据库存储每一个从不同数据源提取出来的原始数据。数据集成层集成数据，集成的数据接着转移到数仓仓库，在这里数据被安排到分级的组，这些组被称为维度表或事实表。事实表和维度表的组合有时被称作星辰结构。用户可以在访问层检索数据。

　　主要数据源中的数据是干净的、转换过的、分类的，这使得管理者或商业专家在数据挖掘、在线过程分析、市场调查和决策支持时很便利。然而，用来对数据进行检索和分析、提取、转变和加载，管理数据字典的工具，是数仓系统的关键部分。对数仓的许多引用都是用了这种很广泛的解释，因此，数仓广义的定义是商业智能工具，是数据提取、转变、加载到目录中的工具，是管理和检索元数据的工具。

　　

In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place[2] that are used for creating analytical reports for workers throughout the enterprise.[3]

The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing[2] for additional operations to ensure data quality before it is used in the DW for reporting.

The typical extract, transform, load (ETL)-based data warehouse[4] uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.[5]

The main source of the data is cleansed, transformed, catalogued, and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.[6] However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.