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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] Data warehouses are central repositories of integrated data from one or more disparate sources.
The dimension is a data set composed of individual, non-overlapping data elements. The primary functions of dimensions are threefold: to provide filtering, grouping and labelling. These functions are often described as "slice and dice". A common data warehouse example involves sales as the measure, with customer and product as dimensions.
A data mart is a structure/access pattern specific to data warehouse environments, used to retrieve client-facing data. The data mart is a subset of the data warehouse and is usually oriented to a specific business line or team. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department.
In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. Where multiple fact tables are used, these are arranged as a fact constellation schema. A fact table typically has two types of columns: those ...
Star schema. In computing, the star schema or star model is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. [ 1] The star schema consists of one or more fact tables referencing any number of dimension tables. The star schema is an important special case of the ...
Other data warehouses (or even other parts of the same data warehouse) may add new data in a historical form at regular intervals — for example, hourly. To understand this, consider a data warehouse that is required to maintain sales records of the last year. This data warehouse overwrites any data older than a year with newer data.
Kimball lifecycle. The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design, development and deployment " of a data warehouse or business intelligence system. [ 1]
The design of structural metadata commonality using a data modeling method such as entity-relationship model diagramming is important in any data warehouse development effort. They detail metadata on each piece of data in the data warehouse. An essential component of a data warehouse/business intelligence system is the metadata and tools to ...