Data Warehousing is an approach that can collect and manage information from multiple sources to support the business a significant business insight. A data warehouse is specifically created for the goals of support management decisions.
A data warehouse defines a database that is maintained separately from a company operational databases. Data warehouse systems enable the integration of several application systems. They support data processing by supporting a solid platform of consolidated, historical records for analysis.
A data warehouse can be considered as a group of materialized views defined over remote base areas. When a query is formal, it is computed locally, using the materialized views, without accessing the initial data sources.
The data warehouse is an active entity that derives continuously over time. As time passes, new queries are required to be answered by them. Various queries can be answered using exclusively the materialized views. In general, though new views need to be inserted into the data warehouse.
A data warehouse system includes databases (source databases, materialized views in the data warehouse), data transport agents that ship records from one database to another, and a repository that saves metadata about the system and its expansion.
There are various challenging problems concerning the construction and application of spatial data warehouses. The first challenge is the unification of spatial information from heterogeneous sources and systems. Spatial data are generally saved in several market firms and government agencies using multiple data formats.
The second challenge is the realization of fast and flexible online analytical processing in spatial data warehouses. The star schema model is the best choice for modeling spatial data warehouses because it supports a concise and organized warehouse structure and supports OLAP services. But, in a spatial warehouse, both dimensions and measures can include spatial elements.
There are four different views regarding the design of a data warehouse must be considered such as the top-down view, the data source view, the data warehouse view, and the business query view.
The top-down view allows the selection of the relevant information necessary for the data warehouse. This data connects the current and future business requirements.
The data source view exposes the data being captured, saved, and handled by operational systems. This data can be documented at several levels of detail and accuracy, from single data source tables to integrated data source tables.
Data sources are often modeled by traditional data modeling techniques, such as the entity-relationship model or CASE (computer-aided software engineering) tools.
The data warehouse view contains fact tables and dimension tables. It defines the data that is saved within the data warehouse, such as precalculated totals and counts, and information concerning the source, date, and time of origin, added to support historical context.
Finally, the business query view is the view of records in the data warehouse from the viewpoint of the end user.