RE: What is meant by metadata in context of a Dataware...
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)
RE: What is meant by metadata in context of a Dataware...
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.
RE: What is meant by metadata in context of a Dataware...
meta data is stored in repository only not in dataware house .. but we r placing our repository in database in that way ur correct but not directly stored in the dataware house plz check it mam
RE: What is meant by metadata in context of a Datawarehouse and how it is important?
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.
