What is meant by metadata in context of a Datawarehouse and how it is important?

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Dear User, --- Metadata or Meta Data Metadata is data about data. Examples of metadata include data element descriptions, data type descriptions, attribute/property descriptions, range/domain descriptions, and process/method descriptions. The repository environment encompasses all corporate metadata resources: database catalogs, data dictionaries, and navigation services. Metadata includes things like the name, length, valid values, and description of a data element. Metadata is stored in a data dictionary and repository. It insulates the data warehouse from changes in the schema of operational systems. Metadata Synchronization The process of consolidating, relating and synchronizing data elements with the same or similar meaning from different systems. Metadata synchronization joins these differing elements together in the data warehouse to allow for easier access.Should you need any further assistance pls revert to venkatdba2000@yahoo.com or venkata.veluri@gmail.com.RegardsVen ( Venkat)

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Arijit Nag

  • Nov 5th, 2005
 

In context of a Datawarehouse metadata is meant the information about the data .This information  is stored in the designer repository.

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bs

  • Nov 15th, 2005
 

Meta data is the data about data; Business Analyst or data modeler usually capture information about data - the source (where and how the data is originated), nature of data (char, varchar, nullable, existance, valid values etc) and behavior of data (how it is modified / derived and the life cycle ) in data dictionary a.k.a metadata. Metadata is also presented at the Datamart level, subsets, fact and dimensions, ODS etc. For a DW user, metadata provides vital information for analysis / DSS.

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Prathiba

  • Nov 17th, 2005
 

 metadata is data about data , it including things name , location ,length including things.

we can u store data in meatdata in datawarehouse

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Metadata is the Information about the Data which is being stored in DWH ,

Why its imp :
1. To know the flow of source data into Data
Warehouse,
2. In case if you have many star schema or many subject areas, knowing that which Area will contain which data will make you analysis faster, easier and to the target.
3.
While making any enhancements if you know which data is getting impacted so you can change
your ETL and data model design easily.

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