Dynamic Server supports data warehouses and data marts. This typically involves a dimensional database that contains large stores of historical data. The databases that track your grocery purchases and voting trends in your state are examples of data warehouses.
A dimensional database is optimized for data retrieval and analysis. The data is stored as a series of snapshots, in which each record represents data at a specific time. Existing records in a dimensional database are updated infrequently. This type of informational processing is known as online analytical processing (OLAP) or decision-support processing.
A data-warehousing environment can store data in one of the following forms:
A database that is optimized for data retrieval
Data is not stored at the transaction level; some level of data is summarized.
A subset of a data warehouse that is stored in a smaller database and that is oriented toward a specific purpose or subject rather than enterprise-wide strategic planning
A data mart can contain operational data, summarized data, spatial data, or metadata.
A subject-oriented system that is optimized for looking up one or two records at a time for decision making
An operational data store is a hybrid form of data warehouse that contains timely, current, integrated information. This data can serve as the common source of data for data warehouses.
A repository combines multiple data sources into one normalized database
The records in a repository are updated frequently. Data stored in a repository is operational, not historical.
For details on how to plan, build, and implement a dimensional database, see the IBM Informix Database Design and Implementation Guide.
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