glossary of data warehousing terms
|attribute||A field or column of a dimension (or other) table.|
|A suite of software tools used primarily by business administrative staff to navigate through the data of the data warehouse. BI tools provide functionality including managed reporting, queyring, data analysis, data visualization, etc.|
|A multi-dimensional representation of business data in which the cells of the cube contain data measures (i.e. facts) and the edges of the cube represent the data dimensions.
Although a cube implies only 3 dimensions in geometry, a data cube may represent any number of dimensions.
|data mart||A subset of the organization's data, focused on a specific subject area or business area.|
|(1) A collection of data pulled together primarily from operational business systems, structured and tuned for easy access and use by consumers and analysts, especially in support of forecasting and decision-making.
(2) A subject-oriented, integrated, time-varying, non-volatile collection of data in support of the management's decision-making process. (John Inmon)
|dimension||A set of attributes, usually hierarchical, that is used to describe an organization's business by constraining and grouping facts.
Example dimensions include time, students, faculty, organization, funds, etc.
|A software system used to support decision-making processes within an organization.|
|A concise set of customized, high-level, generally graphical views of the organization's information, enabling executive-level management to see the overall health of their business.|
|A set of back-end data staging steps that are used to (1) obtain data from operational sources (i.e. the extraction step), (2) cleanse and prepare data for import into the data warehouse (i.e. the transformation step), and (3) actually importing the transformed data into the data warehouse (i.e. the loading step).|
|fact||A numeric (or other type of) data element by which an organization measures aspects of its business. The most useful facts are indeed numeric and often additive.
Example facts include dollar amounts (e.g. budget, expenditure, encumbrance, revenue), counts (e.g. headcount, credit hours), etc.
|grain||The meaning of a single record in a fact table.|
|metadata||Data about data; any data maintained to support the operation or use of a data warehouse, including business names and definitions of facts, dimensions, attributes, etc.|
|Often used in a Microsoft Windows environment, ODBC is a standard protocol for accessing a majority of database systems, including Oracle, Access, etc.|
|A category of database software systems that primarily involves aggregating large amounts of data from a data warehouse environment.|
|A category of database software systems that typically involves processing transactions in real time.|
|A database (e.g. Oracle) in which information is represented via tables and relationships between such tables. The term RDBMS is also often used to refer to software that helps to administer a database.|
|snapshot||Typically describes a fact table that represents the state of affairs at the end of each time period.|
|A standard language for creating, modifying, and querying an RDBMS.|
|star schema||A collection of dimensions joined together with a single fact table that is used to construct queries against a data warehouse.|
|DSS||Decision Support System|
|EIS||Executive Information System|
|ETL||Extraction, Transformation, Loading|
|ODBC||Open Database Connectivity|
|OLAP||On-Line Analytical Processing|
|OLTP||On-Line Transactional Processing|
|RDBMS||Relational Database Management System|
|SQL||Structured Query Language|