Think of metadata as the Yellow Pages® of your town. Do you need information about the stores in your town, where they are, what their names are, and what products they specialize in? Go to the Yellow Pages. The Yellow Pages is a directory with data about the institutions in your town. Almost in the same manner, the metadata component serves as a directory of the contents of your data warehouse.
Because of the importance of metadata in a data warehouse, we have set apart all of Chapter 9 for this topic. At this stage, we just want to get an introduction to the topic and highlight that metadata is a key architectural component of the data warehouse.
Metadata in a data warehouse fall into three major categories:
Extraction and Transformation Metadata
Operational Metadata. As you know, data for the data warehouse comes from several operational systems of the enterprise. These source systems contain different data structures. The data elements selected for the data warehouse have various field lengths and data types. In selecting data from the source systems for the data warehouse, you split records, combine parts of records from different source files, and deal with multiple coding schemes and field lengths. When you deliver information to the end-users, you must be able to tie that back to the original source data sets. Operational metadata contain all of this information about the ...