Importance and function of data warehouse
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The purpose of the Data Warehouse in the overall Data Warehousing Architecture is to integrate corporate data. It contains the "single version of truth" for the organization that has been carefully constructed from data stored in disparate internal and external operational databases.
The amount of data in the Data Warehouse is massive. Data is stored at a very granular level of detail. For example, every "sale" that has ever occurred in the organization is recorded and related to dimensions of interest. This allows data to be sliced and diced, summed and grouped in unimaginable ways.
Contrary to popular opinion, the Data Warehouse does not contain all the data in the organization. Its purpose is to provide key business metrics that are needed by the organization for strategic and tactical decision making.
Decision makers don't access the Data Warehouse directly. This is done through various front-end tools that read data from subject specific Data Marts.
The Data Warehouse can be either "relational" or "dimensional". This depends on how the business intends to use the information.
ETL (Extract Transform Load) jobs extract data from the Data Warehouse and populate one or more Data Marts for use by groups of decision makers in the organizations. The Data Marts can be Dimensional (Star Schemas) or relational, depending on how the information is to be used and what "front end" Data Warehousing Tools will be used to present the information.
Each Data Mart can contain different combinations of tables, columns and rows from the Enterprise Data Warehouse. For example, a business unit or user group that doesn't require a lot of historical data might only need transactions from the current calendar year. The Personnel Department might need to see all details about employees, whereas data such as "salary" or "home address" might not be appropriate for a Data Mart that focuses on Sales
The amount of data in the Data Warehouse is massive. Data is stored at a very granular level of detail. For example, every "sale" that has ever occurred in the organization is recorded and related to dimensions of interest. This allows data to be sliced and diced, summed and grouped in unimaginable ways.
Contrary to popular opinion, the Data Warehouse does not contain all the data in the organization. Its purpose is to provide key business metrics that are needed by the organization for strategic and tactical decision making.
Decision makers don't access the Data Warehouse directly. This is done through various front-end tools that read data from subject specific Data Marts.
The Data Warehouse can be either "relational" or "dimensional". This depends on how the business intends to use the information.
ETL (Extract Transform Load) jobs extract data from the Data Warehouse and populate one or more Data Marts for use by groups of decision makers in the organizations. The Data Marts can be Dimensional (Star Schemas) or relational, depending on how the information is to be used and what "front end" Data Warehousing Tools will be used to present the information.
Each Data Mart can contain different combinations of tables, columns and rows from the Enterprise Data Warehouse. For example, a business unit or user group that doesn't require a lot of historical data might only need transactions from the current calendar year. The Personnel Department might need to see all details about employees, whereas data such as "salary" or "home address" might not be appropriate for a Data Mart that focuses on Sales
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