Metadata is to the data warehouse what the card catalog is to a traditional library. It serves to identify the contents and location of data in the warehouse. Metadata is a bridge between the data warehouse and the decision support application. In addition to providing a logical linkage between data and application, metadata can pinpoint access to information across the entire data warehouse and can enable the development of applications that automatically update themselves to reflect data warehouse content changes.
In a traditional database, a schema describes the conceptual or logical data structures of all the objects or entities with which the database is concerned, together with all relationships between them known to the database. In such a well-defined concept, the difference between metadata and data disappears — metadata is simple data. However, in the context of a data warehouse, metadata is needed to provide an unambiguous interpretation.
Metadata provides a catalog of data in the data warehouse and the pointers to this data. Metadata may contain data extraction/transformation history, column aliases,. data warehouse table sizes, data communication/modeling algorithms, and data usage statistics.
Metadata, in its broadest sense, defines and describes the entire application environment. It answers such questions as
- What does this field mean in business terms?
- Which business processes does this set of queries support?
- When did the job update the customer da6 in our data mart the last run?
- Which file contains product data, where does it reside, and. what is its detailed structure?
