Data Warehousing
Determining which subjects will be created and populated in the data warehouse is called subject definition. A subject is a logical concept; for example, an organization's customers, including their names, addresses, etc. From this business logic, a logical data model is developed. Next, the logical model is translated into a physical data model that defines the actual data storage architecture for the
Integration and conversion resolves data inconsistencies in value definitions and formats among data. Summarization, consisting of both numerical summarizations and groupings, provides analysts with a historical view, rather than the record-by-record view provided by the OLTP database. Transformation of data consists of two distinct steps: 1) integration and conversion and 2) summarization. As an abstraction layer, metadata masks the technical aspects of data access, making information resources access-friendly. Data warehousing offers organizations an opportunity to reinvent the tools used for decision making by making the tremendous amounts of data collected by business yield copious amounts of useful information about customers. Data transformations are used to convert and summarize operational data into a consistent, business-oriented format. The business benefits derived from implementing a data warehouse are tremendous. Ideally, end users access data from the data warehouse without knowing where the data reside, the format, or any other physical attributes. Metadata is the warehouse repository that defines the rules and content of the warehouse and maps this data to the query user on one end and to the operational sources of data on the other. Physical models can be based on several design constructs, such as an entity relationship model, star schema, snowflake schema, persistent multidimensional store, or summary tables.
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data warehouse,
integration conversion,
data model,
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