Fundamentals of Data Warehousing
What is a Data Warehouse?
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.
Four basic characteristics of a data warehouse were set forth by William Inmon:
Data warehouses are designed to help people to analyze data about various subjects of interest. Hence For to learn more about a company's sales data, one can build a warehouse that concentrates on sales. Using this warehouse, answers to questions like "Who was our best customer for this item last year?" can be given. This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.
Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated.
Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.
In order to discover trends in business, analysts need large amounts of data. A data warehouse's focus on analyzing change over time is what is meant by the term time variant, or focus on variation over time and other dimensions like variation over customers, regions, channels etc..
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