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Data Integrity stands for the process that guarantees quality, reliability, outcome, trustworthiness, stability and consistency of the data.

Metadata is data about data. Metadata is used to describe the properties of data such as dimensions, hierarchies, performance metrics and report layout objectsA distinction can be made between business and technical metadata.

A dimension table contains all information for one single entity in context of one or more related fact tables. Within a business question it deals with the "by” part. E.g. a customer dimension contains a customer number, name, address, contacts etc.

A dimension table contains all information for one single entity in context of one or more related fact tables. Within a business question it deals with the "by” part. E.g. a customer dimension contains a customer number, name, address, contacts etc.

A measure is a numeric expression, which can be used and displayed –by dimension- on reports and consist of base facts, aggregated values or mathematical functions.

A measure is a numeric expression, which can be used and displayed –by dimension- on reports and consist of base facts, aggregated values or mathematical functions.

A fact table contains all numeric information for one single business process in context of multiple related dimensions.eg. a sales fact contains number of sold quantities, revenue, margin, etc. The granularity is ideally kept as low as possible in order to enable a variety of reports and analyses.

A fact table contains all numeric information for one single business process in context of multiple related dimensions.eg. a sales fact contains number of sold quantities, revenue, margin, etc. The granularity is ideally kept as low as possible in order to enable a variety of reports and analyses.

A base table is a synonym for a fact table. It contains data stored at the lowest level of detail. In this way the information can be combined with a multitude of dimensional information.

The process of cleaning up the data before loading them into a data warehouse by removing errors, incomplete information or inconsistency between sources.