Data mapping: Difference between revisions

Revision as of 03:27, 15 December 2014

Revision as of 08:51, 16 January 2015

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{{Data transformation}}

{{inlinemore footnotes|date=June 2010}}

In [[computing]] and [[data management]], '''data mapping''' is the process of creating [[data element]] [[Map (mathematics)|mapping]]s between two distinct [[data model]]s. Data mapping is used as a first step for a wide variety of [[data integration]] tasks including:

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[[Semantic mapping]] is similar to the auto-connect feature of data mappers with the exception that a [[metadata registry]] can be consulted to look up data element synonyms. For example, if the source system lists FirstName but the destination lists PersonGivenName, the mappings will still be made if these data elements are listed as [[synonyms]] in the metadata registry. Semantic mapping is only able to discover exact matches between columns of data and will not discover any transformation logic or exceptions between columns.

Data Lineage is a track of the life cycle of each piece of data as it is ingested, processed and output by the analytics system. This provides visibility into the analytics pipeline and simplifies tracing errors back to their sources. It also enables replaying specific portions or inputs of the dataflow for step-wise debugging or regenerating lost output. In fact, database systems have used such information, called data provenance, to address similar validation and debugging challenges already.<ref>De, Soumyarupa. (2012). Newt : an architecture for lineage ­based replay and debugging in DISC systems. UC San Diego: b7355202. Retrieved from: https://escholarship.org/uc/item/3170p7zn</ref>

==See also==

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==External links==

{{DEFAULTSORT:Data Mapping}}