https://blog.csdn.net/bjweimengshu/article/details/79256504
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In computer science, data warehouse (data warehouse, referred to as DW or DWH), also known as enterprise data warehouse (EDW), it is a data analysis and reporting systems, business intelligence (business intellgence referred to as BI) core components. The central warehouse bin number data from one or more different sources in the integration process. Number of warehouses storing real-time and historical data from one place to generate data reports for employees of all enterprises.
To upload data from the number of bins in the system operation (e.g., marketing or sales), which is used to report the number of data bins may be stored by the operation data, the additional data required cleaning operations, to ensure data quality.
Based on a typical extraction, conversion of the number of bins, loading (ETL) process using the temporary layer, data access layer and the integration layer to build critical functions. Temporary staging layer or raw data of each extracted from different data sources stored in the database. Data integration layer data integration, integrated data is then transferred to a warehouse bin number, where the classification data is arranged into groups, the groups are referred to as dimension table or fact table. A combination of the fact and dimension tables structure sometimes referred to as the stars. The user can retrieve the data access layer.
The main data source data is clean, the converted classification, which allows administrators or business experts in data mining, online process analysis, when it is convenient to support market research and decision-making. However, for data retrieval and analysis, extraction, transformation and loading, data dictionary management tool, the number of bins is a key part of the system. Many references are made this very broad interpretation, therefore, the number of positions broadly defined as business intelligence tools, data extraction, transformation, loading tool in the directory that is to manage and retrieve metadata tools logarithmic warehouse .
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.